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Data Analytics in Business: A Complete Overview

• Written by John Terra
• Updated on February 27, 2024

In these challenging economic times, businesses seek ways to secure a competitive edge and keep their commerce thriving. One of the primary keys to business success is making sound decisions, and one of the best ways of assuring your choices are good is to base them on accurate information. And for there to be accurate information, you need business analytics.

This article explores the subject of data analytics in business. We will define the term, explain its benefits, why it’s essential, and how data analytics can improve business management. We’ll also share to learn critical skills and tools through online data analytics training .

Let’s get the ball rolling by defining data analytics in business.

What is Data Analytics in Business?

Data analytics in business describes collecting, processing, analyzing, and interpreting vast volumes of data, extracting meaningful insights, trends, and patterns that can guide and inform strategic decisions, improve operational efficiency, and foster overall business growth. Data analytics involves using diverse techniques, tools, and methodologies to change raw data into actionable information, which can then be used to make better-informed choices and optimize different aspects of the business.

Also Read: Data Analytics Applications: Types, Use Cases, and Top Tools

How Do You Conduct Business Analytics?

Business analytics typically breaks down into the following steps:

• Data Collection . Collecting relevant data from different sources, which includes marketing campaigns, customer interactions, operational processes, sales transactions, and external market data.
• Data Processing . Cleaning, organizing, and preparing the collected data for analysis to ensure its accuracy and consistency.
• Data Analysis. Applying mathematical, statistical, and machine learning techniques to discover correlations, patterns, and insights within the processed data.
• Data Interpretation . Interpreting the data analysis results in deriving actionable insights and conclusions that can improve the likelihood of better decision-making.
• Decision Making. Employing the insights from data analysis to make informed decisions that will impact different areas of the organization, such as marketing strategies, resource allocation, product development, etc.
• Continuous Improvement . Monitoring and evaluating decision outcomes based on data analytics, then refining and adjusting the strategies over time-based on subsequently obtained data and insights.

The Difference Between Data Analytics and Business Analytics

Many people use business and data analytics interchangeably, but they are subtly different. Data analytics is a subset of business analytics, which uses data to analyze current and past business performances to obtain insights that help executives make better-informed decisions. Let’s show the differences by exploring what each analyst does:

Data Analysts

• Work with business leaders and stakeholders to define problems or business needs
• Identify and source data
• Clean and prepare data for analysis
• Analyze data, looking for patterns and trends
• Visualize data to make it easier to comprehend
• Present data so that it tells a compelling story

Business Analysts

• Evaluate the company’s current functions and IT structures
• Review processes and interview team members to identify critical areas for improvement
• Present findings and recommendations to management and other appropriate stakeholders
• Create visuals and financial models to support any business decisions
• Train and coach staff in new systems

So, while both positions use data to make better business decisions, they take different paths up the mountain.

Also Read: Tutorial: Data Analysis in Excel

The Four Types of Data Analytics

All forms of data analytics fall under one of the following four categories.

Descriptive Analytics

Descriptive analytics asks, “What has happened?” Descriptive analytics doesn’t look forward but provides a comprehensive picture of past events unfolding. The chief advantage of descriptive data analysis is that it helps people understand what happened and why it happened. Typical examples include:

• Sales performance
• Dashboard reporting
• Fraud detection
• Product demand forecasts

Diagnostic Analytics

Diagnostic analytics asks, “Why did this happen?” It examines the factors that led to an event to answer why an issue occurred. This kind of analysis can help organizations understand what has happened, why, and how it can be prevented from happening again. Common examples of diagnostic analytics are:

• Root cause analysis
• Retrospective analysis
• Regression analysis

Predictive Analytics

Predictive analytics asks, “What will happen in the future?” It uses existing data to forecast future outcomes or trends. Organizations typically use predictive analytics when developing new products or services since it gives them a good guess of what their customers will want in the future based on past behavior. Examples of Predictive analytics include:

• Direct Marketing
• Customer Pricing
• Retail Sales Forecasting

Prescriptive Analytics

Prescriptive analysis asks, “What should we do?” This analysis form takes predictive analytics one step further by recommending future actions based on past trends and data. This form of data analysis is especially helpful in optimizing resources and spotting new business opportunities (e.g., expansion). Also, prescriptive analytics can be employed in making decisions or providing recommendations that let others make better decisions faster. For example, prescriptive models could recommend whether a business should:

• Launch a new product line or end a current one
• Construct a new factory or shut down an existing one
• Put in a bid on a new project, and at what price
• Hire additional staff in a given department
• Send a targeted ad to particular customers

Why is Data Analytics Important for Business?

Data and business analytics lets businesses create reports and spot patterns to help organizations operate more efficiently. Analyzing relevant data can also enhance decision-making by allowing the company to predict industry trends or customers’ wants. These predictions help companies remain on the cutting edge and stay competitive.

Data analytics can also help understand the different types of customers who visit an establishment. For example, if a restaurant discovers that most customers are families with children, it may want to emphasize more family-friendly fare. On the other hand, if students make up most of the regular clientele, the restaurant may want to offer student discounts as part of their marketing incentives.

The restaurant could also use data analytics to assess employee performance based on the sales data collected from each server. If a particular server has low daily sales, the supervisor may want to check on the employee to see if they need more training or to perform up to expectations.

We live and work in the era of big data, where today’s business leaders have access to more information than ever. Analyzing (and monetizing) this information is an essential skill for any professional involved in leadership.

Also Read: What is Exploratory Data Analysis?

The Advantages of Data Analytics in Business

Data analytics can be a valuable tool today’s businesses can leverage to stay competitive and survive in rocky financial markets. The advantages of data analytics in business include:

• Improving efficiency. Data analytics lets businesses collect vast amounts of data, which can then be analyzed and used to identify weaknesses in their business models. Companies don’t often notice inefficiency immediately because it’s easy to slip into (and perpetuate) bad habits or practices that may have worked at one point but don’t anymore. Also, organizations tend to focus on other things. However, inefficiency can cause a noticeable drain on profits and perhaps even lead to the business’s end. Efficiency is critical, but it can be challenging to spot inefficiencies. Data analytics can help.
• Making better decisions. One of the primary advantages of data analytics in business is that it helps companies make better decisions. Understanding what has occurred in the past, what is happening now, and what could happen in the future can be a game-changing advantage for any business. Companies using data analytics can predict customer behaviors and needs, making them more likely to provide the kind of goods and services that customers prefer.
• Reducing costs. Using the company’s data information is a great way to stay budget-conscious and help an organization run more efficiently, typically by pointing out underperforming elements.
• Increasing revenue. Data analytics helps businesses increase their revenue by giving them insights into making better decisions regarding pricing and product offerings. Data analysis could show that most customers who buy a particular product also tend to buy another given product. A business could bundle these two products in a discounted package deal, and customers love a good deal!
• Making the business more competitive. Data analytics allows businesses to move ahead of their competition by providing better insights into their customer base and how they can best reach them. Analytics also helps organizations identify what they’re doing wrong and how to change it.

How Can Data Analytics Improve Business Management?

Let’s run through a brief list of the ways data analytics can specifically help improve business management:

• Customer insights. Data analytics offers a deeper understanding of customer behavior, buying patterns, and preferences, allowing businesses to tailor products and services to the consumers’ needs.
• Competitive advantage . Leveraging data analytics gives businesses a competitive edge by staying current on market trends, responding quickly to changes, and outperforming their competitors.
• Informed decision-making. Data analytics lets businesses strategically use real-time insights and trends, reducing reliance on guesswork, hunches, and intuition.
• Identifying hidden opportunities. Organizations can discover otherwise hidden opportunities, emerging trends, and market gaps by analyzing large datasets, letting them acquire new revenue streams.
• Operational efficiency. Businesses can use data analytics to optimize processes and workflows, identifying inefficiencies, bottlenecks, and areas for improvement.
• Performance tracking. Businesses can monitor their key performance indicators (KPIs) via data analytics, letting them measure success, identify improvement areas, and adapt strategies as necessary.
• Personalized marketing. A company can develop customized marketing campaigns that jibe with individual preferences by analyzing customer data, leading to higher engagement and conversion rates.
• Predictive insights . Data analytics lets businesses predict future trends and outcomes, which can improve long-term planning and strategy development.
• Resource allocation. Businesses can use data to assign resources more effectively, understanding which initiatives yield the best returns, thus ensuring optimal resource utilization.
• Risk management. Data analytics helps identify risks and vulnerabilities by analyzing historical data, allowing organizations to introduce proactive risk mitigation strategies.

Also Read: How To Become a Data Analytics Manager

Data Analytics Use Cases in Business

Here are a couple of use cases for data analytics in business.

Customer Service

Without customers, businesses have nothing. Companies can employ data analytics and artificial intelligence to gain deeper insights into customer behavior. Use cases include:

• Providing customers with personalized content and specifically tailored recommendations
• Identifying common complaints that customers have regarding specific products or services
• Reducing the cost of delivering support (e.g., providing self-service options)
• Resolving issues faster and more effectively using a better understanding of customer history and needs
• Predicting what products or services the customer will probably buy next
• Automating processes like payment processing and fraud detection

Marketing and Sales

Data analytics truly shines in this area. More companies are increasingly turning to data analytics to facilitate marketing and sales. Both sectors benefit from the use of data analytics in separate ways:

• Determining the effectiveness of different advertising and marketing campaigns
• Finding the best combination of products for a given customer
• Finding the best price for a specific product or product bundle
• Identifying which customers will be most likely to respond to a given offer
• Identifying new markets for existing goods and services

Human Resources

Employees are a significant investment for any business or organization, so investing in data analytics helps management assemble a more efficient workforce.

• Analyzing employee performance, retention risks, and attrition patterns
• Assessing training and development needs
• Evaluating a training program’s effectiveness
• Determining the impact of internal promotions on employee morale
• Making better hiring/promoting decisions by analyzing past employee performance and recruitment campaigns to find the best methods for attracting top talent
• Spotting trends that highlight possible issues with staff retention

Here’s How You Can Learn More About Data Analytics

If you’re interested in learning how to apply data analytics in business, consider this 24-week data analytics bootcamp . Through live online instructor-led sessions and hands-on projects, you will learn methods of transforming raw data into actionable insights using various tools and technologies. Additionally, you will study generative AI and prompt engineering and gain practical exposure to tools such as ChatGPT, DALL-E, and Midjourney.

Indeed.com reports that data analysts can earn an annual average of $76,787. If you want a career change or want to improve your data analytics skills, check out this highly instructive online course.

You might also like to read:

Data Analyst Job Description: What Aspiring Professionals Need to Know

Data Analyst Roles and Responsibilities

Data Analytics Certifications: Top Options in 2024

Best Data Analytics Tools in 2024 and Beyond

All About the Data Analyst Skills Professionals Need

Caltech Data Analytics Bootcamp

• Learning Format:

Online Bootcamp

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How to Write a Business Analysis Report [Examples and Templates]

Table of contents

To see what Databox can do for you, including how it helps you track and visualize your performance data in real-time, check out our home page. Click here .

Business analysis reports are a lot like preparing a delicious meal.

Sometimes, the recipe is simple enough that you only need to use the basic ingredients. Other times, you will have to follow specific instructions to ensure those tasty delicacies turn out just right.

Want to make sure your business report never turns out like a chewy piece of meat? You’ve come to the right place.

Stay tuned until the end of this blog post, and we promise you won’t be hungry… for business knowledge!

What Is a Business Analysis Report?

Why is analytical reporting important, what should be included in a business analysis report, how do you write a business analysis report, business data analysis report examples and templates.

• Improve Business Reporting with Databox

A business analysis report provides information about the current situation of your company. This report is usually created by the management to help in the decision-making process and is usually used by other departments within a company.

Business analysis reports can either focus your research on the effectiveness of an existing business process or a proposed new process. Besides, an effective business analysis report should also assess the results to determine if the process changes had a positive or negative effect on the company’s goals. In fact, according to Databox’s State of business reporting , an overwhelming majority of companies said that reporting improved their performance.

Analytical reports are the bridge that connects your company to an effective, data-driven business intelligence strategy . By leveraging analytical reports , you can make informed decisions about your organization’s most critical issues. You will no longer need to rely on gut instinct or anecdotal evidence when assessing risks, threats, and opportunities. Instead, you will have access to a wealth of reliable data to inform your decisions.

Here are some essential benefits of analytical reporting:

• Improve communication and foster collaboration – The most obvious benefit of business analysis report writing is an improvement in communication between all stakeholders involved in the project. Also, analytical business reports can help you to generate more trust and foster better collaboration among your employees and colleagues. By using data analytics reporting tools , you will be able to monitor your employees’ performance on a day-to-day basis. This will allow you to hold them accountable for their actions and give them greater freedom within the business as they know that their superiors have faith in their decision-making capabilities.
• Increase productivity – Without this level of shared insight, businesses struggle to stay on top of their most important tasks and can become less efficient. An effective analytical business report provides the information needed for more efficient internal processes and helps you find more time for strategic activities such as improving your business strategy or working on long-term goals .
• Innovation – In today’s digital age, the pressure to innovate was never greater. When consumers basically have everything they want at their fingertips, stepping up to the plate with a new and improved product or service has never been more important. With an accessible dashboard in place, you will be able to create data-driven narratives for each of your business’ critical functions. For example, if you are a software company, you can use the insights gained from report analysis done with your dashboard software to tailor your product development efforts to the actual needs of your customers. By doing so, you will be able to develop products that are better tailored to specific customer groups. You can also use the same information for developing new marketing strategies and campaigns.
• Continuous business evolution – When it comes to digital businesses, data is everything. No model lasts forever, so having access to a business dashboard software that allows you to constantly keep tabs on your business’ performance will help you refine it as time goes on. If there are any glitches in your business model, or if something isn’t panning out as expected, the insight offered by a business analysis report can help you improve upon what works while scrapping what doesn’t.

A business analysis report has several components that need to be included to give a thorough description of the topic at hand. The structure and length of business analysis reports can vary depending on the needs of the project or task.

They can be broken down into different sections that include an:

• Executive summary
• Study introduction
• Methodology
• Review of statistics

Reports of this nature may also include case studies or examples in their discussion section.

A report can be written in a formal or informal tone, depending on the audience and purpose of the document. While a formal tone is best for executives , an informal tone is more appropriate for technical audiences . It is also a good idea to use something like an executive summary template to report on the results repeatedly with ease.

A good business analysis report is detailed and provides recommendations in the form of actionable steps. Here we have listed some simple steps that you need to follow to write a good business analysis report. Report writing is a major part of the business analysis process. In this section, you will learn how to write a report for your company:

Preparation

Presentation.

Obtain an overview of what you want to analyze in the business report . For example, if you are writing a business analysis report on how to improve customer service at an insurance company, you will want to look through all the customer service processes to determine where the problems lie. The more prepared you are when starting a project, the easier it will be to get results. Here is what your preparation should look like:

Set your goals

The first step in writing this document is to set your goals . What do you hope to accomplish with this paper? Do you need to assess the company’s finances? Are you looking for ways to make improvements? Or do you have outside investors who want to know if they should buy into the company? Once you know what your goal is, then you can begin setting up your project.

PRO TIP: How Well Are Your Marketing KPIs Performing?

Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.

Our Marketing Overview Dashboard includes data from Google Analytics 4 and HubSpot Marketing with key performance metrics like:

• Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
• New Contacts from Sessions . How well is your campaign driving new contacts and customers?
• Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
• Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
• Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?

Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

You can easily set it up in just a few clicks – no coding required.

To set up the dashboard, follow these 3 simple steps:

Step 1: Get the template 

Step 2: Connect your HubSpot and Google Analytics 4 accounts with Databox. 

Step 3: Watch your dashboard populate in seconds.

Assess the Company’s Mission

It’s almost impossible to write a business analysis report without access to the company’s mission statement. Even if you don’t plan on using the mission statement as part of your business analysis summary, it can help you understand the company’s culture and goals. Mission statements are typically short and easy to read, but they may not include every area of focus that you want to include in your report.

Thus, it is important to use other sources when possible. For example, if you are writing a business analysis report for a small start-up company that is just beginning to market its product or service, review the company website or talk directly with management to learn what they believe will be most crucial in growing the company from the ground up.

Stakeholder Analysis

Who is your audience? Create the reader’s persona and tailor all information to their perspective. Create a stakeholder map that identifies all the groups, departments, functions, and individuals involved in this project (and any other projects related to this one). Your stakeholder map should include a description of each group’s role.

Review Financial Performance

Review the financing of the business and determine whether there are any potential threats to the company’s ability to meet its future financial obligations. This includes reviewing debt payments and ownership equity compared with other types of financing such as accounts receivable, cash reserves, and working capital. Determine whether there have been any changes in the funding over time, such as an increase in long-term debt or a decrease in owners’ equity.

Apart from reviewing your debt payments and ownership equity with other types of financing, wouldn’t it be great if you could compare your financial performance to companies that are exactly like yours? With Databox, this can be done in less than 3 minutes.

For example, by  joining this benchmark group , you can better understand your gross profit margin performance and see how metrics like income, gross profit, net income, net operating increase, etc compare against businesses like yours.

One piece of data that you would be able to discover is the average gross profit a month for B2B, B2C, SaaS and eCommerce. Knowing that you perform better than the median may help you evaluate your current business strategy and identify the neccessary steps towards improving it.

Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You

If you ever asked yourself:

• How does our marketing stack up against our competitors?
• Are our salespeople as productive as reps from similar companies?
• Are our profit margins as high as our peers?

Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.

When you join Benchmark Groups, you will:

• Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
• Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
• Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
• Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.

The best part?

• Benchmark Groups are free to access.
• The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.

When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:

And here is an example of an open group you could join:

And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more. 

• Choose criteria so that the Benchmark is calculated using only companies like yours
• Narrow the benchmark sample using criteria that describe your company
• Display benchmarks right on your Databox dashboards

Sounds like something you want to try out? Join a Databox Benchmark Group today!

Examine the “Four P’s”

“Four P’s” — product , price , place, and promotion . Here’s how they work:

• Product — What is the product? How does it compare with those of competitors? Is it in a position to gain market share?
• Price — What is the price of the product? Is it what customers perceive as a good value?
• Place — Where will the product be sold? Will existing distribution channels suffice or should new channels be considered?
• Promotion — Are there marketing communications efforts already in place or needed to support the product launch or existing products?

Evaluate the Company Structure

A business analysis report examines the structure of a company, including its management, staff, departments, divisions, and supply chain. It also evaluates how well-managed the company is and how efficient its supply chain is. In order to develop a strong strategy, you need to be able to analyze your business structure.

When writing a business analysis report, it’s important to make sure you structure your work properly. You want to impress your readers with a clear and logical layout, so they will be able to see the strengths of your recommendations for improving certain areas of the business. A badly written report can completely ruin an impression, so follow these steps to ensure you get it right the first time.

A typical business analysis report is formatted as a cover page , an executive summary , information sections, and a summary .

• A cover page contains the title and author of the report, the date, a contact person, and reference numbers.
• The information section is backed up by data from the work you’ve done to support your findings, including charts and tables. Also, includes all the information that will help you make decisions about your project. Experience has shown that the use of reputable study materials, such as  StuDocu  and others, might serve you as a great assistant in your findings and project tasks.
• A summary is a short overview of the main points that you’ve made in the report. It should be written so someone who hasn’t read your entire document can understand exactly what you’re saying. Use it to highlight your main recommendations for how to change your project or organization in order to achieve its goals.
• The last section of a business analysis report is a short list of references that include any websites or documents that you used in your research. Be sure to note if you created or modified any of these documents — it’s important to give credit where credit is due.

The Process of Investigation

Explain the problem – Clearly identify the issue and determine who is affected by it. You should include a detailed description of the problem you are analyzing, as well as an in-depth analysis of its components and effects. If you’re analyzing a small issue on a local scale, make sure that your report reflects this scale. That way, if someone else reads your work who had no idea about its context or scope, they would still be able to understand it.

Explain research methods – There are two ways to do this. Firstly, you can list the methods you’ve used in the report to determine your actions’ success and failure. Secondly, you should add one or two new methods to try instead. Always tell readers how you came up with your answer or what data you used for your report. If you simply tell them that the company needs to improve customer service training then they won’t know what kind of data led you to that conclusion. Also, if there were several ways of addressing a problem, discuss each one and why it might not work or why it may not be appropriate for the company at this time.

Analyze data – Analyzing data is an integral part of any business decision, whether it’s related to the costs of manufacturing a product or predicting consumer behavior. Business analysis reports typically focus on one aspect of an organization and break down that aspect into several parts — all of which must be analyzed in order to come to a conclusion about the original topic.

The Outcome of Each Investigation Stage

The recommendations and actions will usually follow from the business objectives not being met. For example, if one of your goals was to decrease costs then your recommendations would include optimization strategies for cost reduction . If you have more than one suggestion you should make a list of the pros and cons of each one. You can make several recommendations in one report if they are related. In addition, make sure that every recommendation has supporting arguments to back them up.

Report Summary

Every business analysis report should start with a summary. It’s the first thing people see and it needs to capture their attention and interest. The report summary can be created in two ways, depending on the nature of the report:

• If the report is a brief one, that simply gives a summary of the findings, then it can be created as part of the executive summary.
• But if it’s a long report, it could be too wordy to summarise. In this case, you can create a more detailed overview that covers all the main aspects of the project from both an internal and external point of view.

Everything comes down to this section. A presentation is designed to inform, persuade and influence decision-makers to take the next action steps.

Sometimes a slide or two can make them change their mind or open new horizons. These days, digital dashboards are becoming increasingly popular when it comes to presenting data in business reports. Dashboards combine different visualizations into one place, allowing users to get an overview of the information they need at a glance rather than searching through a bunch of documents or spreadsheets trying.

Databox offers dynamic and accessible digital dashboards that will help you to convert raw data into a meaningful story. And the best part is that you can do it with a ‘blink of an eye’ even if you don’t have any coding or designs skills. There is also an option of individual report customization so that you can tailor any dashboard to your own needs.

Pre-made dashboard templates can be extremely useful when creating your own business analysis report. While examples serve as inspiration, templates allow you to create reports quickly and easily without having to spend time (and money) developing the underlying data models.

Databox dashboard templates come with some of the most common pre-built metrics and KPIs different types of businesses track across different departments. In order to create powerful business insights within minutes, all you need to do is download any of our free templates and connect your data source — the metrics will populate automatically.

Business Report Examples and Templates

Databox business dashboard examples are simple and powerful tools for tracking your business KPIs and performance. These dashboards can be used by executive teams and managers as well as by senior management, marketing, sales, customer support, IT, accounting, and other departments. If you are new to this kind of reporting, you may not know how to set up a dashboard or what metrics should be displayed on it. This is where a premade template for business dashboards comes in handy.

For example, this Google Ads Report Template is designed to give you a simple way to keep track of your campaigns’ performance over time, and it’s a great resource for anyone who uses Google’s advertising platform, regardless of whether they’re an SMB, an SME or an enterprise.

KPI Report Examples and Templates

KPIs are the foundation of any business analysis, and they can come in a multitude of forms. While we’ve defined KPIs as metrics or measurements that allow you to assess the effectiveness of a given process, department, or team, there are a number of ways to evaluate your KPIs. Through the use of color-coding, user-friendly graphs and charts, and an intuitive layout, your KPIs should be easy for anyone to understand. A good way to do this is by having a dedicated business analyst on your team who can take on the task of gathering data, analyzing it, and presenting it in a way that will drive actionable insights. However, if you don’t have a dedicated analyst or don’t want to spend money on one, you can still create KPI reporting dashboards using free KPI Databox templates and examples .

For example, this Sales Overview template is a great resource for managers who want to get an overview of their sales team’s performance and KPIs. It’s perfect for getting started with business analysis, as it is relatively easy to understand and put together.

Performance Report Examples and Templates

All businesses, regardless of size or industry, need to know how well they are performing in order to make the best decisions for their company and improve overall ROI. A performance dashboard is a strategic tool used to track key metrics across different departments and provide insight into the health of a business. Databox has a collection of 50+ Performance Dashboard Examples and Templates which are available for free download.

For example, if your business is investing a lot into customer support, we recommend tracking your customer service performance with this Helpscout Mailbox Dashboard which will give you insights into conversations, your team’s productivity, customer happiness score, and more.

Executive Report Examples and Templates

An executive dashboard is a visual representation of the current state of a business. The main purpose of an executive dashboard is to enable business leaders to quickly identify opportunities, identify areas for improvement, pinpoint issues, and make data-informed decisions for driving sales growth, new product launches, and overall business growth. When an executive dashboard is fully developed, as one of these 50+ Databox Free Executive Examples and Templates , it offers a single view of the most important metrics for a business at a glance.

For example, you probably have more than one set of financial data tracked using an executive dashboard software : invoices, revenue reports (for accounting), income statements, to mention a few. If you want to view all this data in one convenient place, or even create a custom report that gives you a better picture of your business’s financial health, this Stripe Dashboard Template is a perfect solution for you.

Metrics Report Examples and Templates

Choosing the right metrics for your business dashboard can be crucial to helping you meet your business objectives, evaluate your performance, and get insights into how your business is operating. Metrics dashboards are used by senior management to measure the performance of their company on a day-to-day basis. They are also used by mid-level managers to determine how their teams are performing against individual goals and objectives. Databox provides 50+ Free Metrics Dashboard Examples and Templates that you can use to create your company’s own dashboards. Each is unique and will depend on your business needs.

For example, if you are looking for ways to track the performance of your DevOps team, and get the latest updates on projects quickly – from commits, and repository status, to top contributors to your software development projects, this GitHub Overview Dashboard is for you.

Small Business Report Examples and Templates

A lot of small business owners don’t realize how important it is to have a proper dashboard in place until they actually use one. A dashboard can help you track and compare different metrics, benchmark your performance against industry averages, evaluate the effectiveness of your marketing and sales strategies, track financials, and much more. So if you’re looking for a tool to help you measure and manage your small business’ performance, try some of these 50+ Free Small Business Dashboard Examples and Templates .

For example, this Quickbooks Dashboard template can help you get a clear understanding of your business’s financial performance, ultimately allowing you to make better-informed decisions that will drive growth and profitability.

Agency Report Examples and Templates

Agency dashboards are not a new concept. They have been around for years and are used by companies all over the world. Agency dashboards can be powerful tools for improving your marketing performance, increasing client loyalty, and landing new clients. There is no single correct way to create an agency dashboard. Everyone has their own goals and objectives, which will ultimately determine which data points you choose to include or track using a client dashboard software , but with these Databox 100+ Free Agency Dashboard Examples and Templates you have plenty of options to start with.

For example, you can use this Harvest Clients Time Report to easily see how much time your employees spend working on projects for a particular client, including billable hours and billable amount split by projects.

Better Business Reporting with Databox

Business analysis is all about finding smart ways to evaluate your organization’s performance and future potential. And that’s where Databox comes in.

Databox can be a helpful tool for business leaders who are required to analyze data, hold frequent meetings, and generate change in their organizations. From improving the quality and accessibility of your reporting to tracking critical performance metrics in one place, and sharing performance metrics with your peers and team members in a cohesive, presentable way, allow Databox to be your personal assistant in these processes, minimize the burdens of reporting and ensure you always stay on top of your metrics game.

Sign up today for free to start streamlining your business reporting process.

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Business Analytics: What It Is & Why It's Important

• 16 Jul 2019

Business analytics is a powerful tool in today’s marketplace that can be used to make decisions and craft business strategies. Across industries, organizations generate vast amounts of data which, in turn, has heightened the need for professionals who are data literate and know how to interpret and analyze that information.

According to a study by MicroStrategy , companies worldwide are using data to:

• Improve efficiency and productivity (64 percent)
• Achieve more effective decision-making (56 percent)
• Drive better financial performance (51 percent)

The research also shows that 65 percent of global enterprises plan to increase analytics spending.

In light of these market trends, gaining an in-depth understanding of business analytics can be a way to advance your career and make better decisions in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” said Harvard Business School Professor Jan Hammond, who teaches the online course Business Analytics , in a previous interview . “If you’re able to go into a meeting and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Before diving into the benefits of data analysis, it’s important to understand what the term “business analytics” means.

Check out our video on business analytics below, and subscribe to our YouTube channel for more explainer content!

What Is Business Analytics?

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

There are four primary methods of business analysis:

• Descriptive : The interpretation of historical data to identify trends and patterns
• Diagnostic : The interpretation of historical data to determine why something has happened
• Predictive : The use of statistics to forecast future outcomes
• Prescriptive : The application of testing and other techniques to determine which outcome will yield the best result in a given scenario

These four types of business analytics methods can be used individually or in tandem to analyze past efforts and improve future business performance.

Business Analytics vs. Data Science

To understand what business analytics is, it’s also important to distinguish it from data science. While both processes analyze data to solve business problems, the difference between business analytics and data science lies in how data is used.

Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process , whereas data science is focused on making sense of raw data using algorithms, statistical models, and computer programming. Despite their differences, both business analytics and data science glean insights from data to inform business decisions.

To better understand how data insights can drive organizational performance, here are some of the ways firms have benefitted from using business analytics.

The Benefits of Business Analytics

1. more informed decision-making.

Business analytics can be a valuable resource when approaching an important strategic decision.

When ride-hailing company Uber upgraded its Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve speed and accuracy when responding to support tickets—it used prescriptive analytics to examine whether the product’s new iteration would be more effective than its initial version.

Through A/B testing —a method of comparing the outcomes of two different choices—the company determined that the updated product led to faster service, more accurate resolution recommendations, and higher customer satisfaction scores. These insights not only streamlined Uber’s ticket resolution process, but saved the company millions of dollars.

2. Greater Revenue

Companies that embrace data and analytics initiatives can experience significant financial returns.

Research by McKinsey shows organizations that invest in big data yield a six percent average increase in profits, which jumps to nine percent for investments spanning five years.

Echoing this trend, a recent study by BARC found that businesses able to quantify their gains from analyzing data report an average eight percent increase in revenues and a 10 percent reduction in costs.

These findings illustrate the clear financial payoff that can come from a robust business analysis strategy—one that many firms can stand to benefit from as the big data and analytics market grows.

Related: 5 Business Analytics Skills for Professionals

3. Improved Operational Efficiency

Beyond financial gains, analytics can be used to fine-tune business processes and operations.

In a recent KPMG report on emerging trends in infrastructure, it was found that many firms now use predictive analytics to anticipate maintenance and operational issues before they become larger problems.

A mobile network operator surveyed noted that it leverages data to foresee outages seven days before they occur. Armed with this information, the firm can prevent outages by more effectively timing maintenance, enabling it to not only save on operational costs, but ensure it keeps assets at optimal performance levels.

Why Study Business Analytics?

Taking a data-driven approach to business can come with tremendous upside, but many companies report that the number of skilled employees in analytics roles are in short supply .

LinkedIn lists business analysis as one of the skills companies need most in 2020 , and the Bureau of Labor Statistics projects operations research analyst jobs to grow by 23 percent through 2031—a rate much faster than the average for all occupations.

“A lot of people can crunch numbers, but I think they’ll be in very limited positions unless they can help interpret those analyses in the context in which the business is competing,” said Hammond in a previous interview .

Skills Business Analysts Need

Success as a business analyst goes beyond knowing how to crunch numbers. In addition to collecting data and using statistics to analyze it, it’s crucial to have critical thinking skills to interpret the results. Strong communication skills are also necessary for effectively relaying insights to those who aren’t familiar with advanced analytics. An effective data analyst has both the technical and soft skills to ensure an organization is making the best use of its data.

Improving Your Business Analytics Skills

If you’re interested in capitalizing on the need for data-minded professionals, taking an online business analytics course is one way to broaden your analytical skill set and take your career to the next level

Through learning how to recognize trends, test hypotheses , and draw conclusions from population samples, you can build an analytical framework that can be applied in your everyday decision-making and help your organization thrive.

“If you don’t use the data, you’re going to fall behind,” Hammond said . “People that have those capabilities—as well as an understanding of business contexts—are going to be the ones that will add the most value and have the greatest impact.”

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics to learn how to use data analysis to solve business problems.

This post was updated on November 14, 2022. It was originally published on July 16, 2019.

About the Author

20 Data Analytics Projects for All Levels

After learning the fundamentals of data analytics, it is time to apply your skills by working on projects. Companies prefer recruiting students with multiple project experiences, and they are looking for employees who are good at data ingestion and cleaning, data manipulation, probability and statistics, predictive analytics, and reporting.

It is not about learning a new language or tools. It is all about understanding the data and extracting important information. You need to work on multiple projects to get better at understanding the data and producing reports for non-technical people.

This blog will cover data analytics projects for beginners, professionals, and final-year students. Furthermore, you will learn about end-to-end projects that involve all essential steps, from data importing to reporting.

If you're looking for projects that are more focused on artificial intelligence, check out our separate guide to some of the top AI projects you can start working on today.

AI Upskilling for Beginners

Why choose data analytics projects.

Data analytics projects are crucial stepping stones for anyone looking to excel in today's data-centric world. Here’s why they are essential:

• Practical skill application : They offer hands-on experience, bridging the gap between theoretical knowledge and real-world practice.
• Industry versatility : Data analytics is vital across various sectors. Working on diverse projects broadens your understanding and adaptability.
• Critical thinking : These projects develop your ability to analyze complex issues, identify patterns, and create data-driven solutions.
• Technical proficiency : Engaging in projects hones your skills in key tools and languages, making you a more competent and versatile analyst.
• Effective communication : They teach you to translate complex data insights into understandable and actionable information, a skill highly valued in any professional setting.
• Career advancement : Completing projects enhances your portfolio, showcasing your abilities to potential employers and expanding your career opportunities.

In essence, data analytics projects not only sharpen your technical skills but also prepare you for the challenges and demands of the modern workplace.

Data Analytics Projects for Beginners 

As a beginner, you need to focus on importing, cleaning, manipulating, and visualizing the data. 

• Data Importing : learn to import the data using SQL, Python, R, or web scraping. 
• Data Cleaning : use various Python and R libraries to clean and process the data. 
• Data Manipulation : using various techniques to shape the dataset for data analysis and visualization.
• Data Visualization : display the data using plots and graphs. 

Data Importing and Cleaning Projects

1. exploring the nyc airbnb market .

In the Exploring the NYC Airbnb Market project, you will apply data importing and cleaning skills to analyze the Airbnb market in New york. You will ingest and combine the data from multiple file types, and clean strings and format dates to extract accurate information.  

Image by Author | Code from the project

The project is perfect for beginners who want to get data importing and cleaning experience. You can apply similar methods to this Online Ticket Sales dataset to get even better at handling and processing the data. 

Learn more about data importing and cleaning by taking short courses:

• Introduction to Importing Data in Python
• Cleaning Data in Python

2. Word Frequency in Classic Novels

In the Word Frequency in Classic Novels project, you will use requests and BeautifulSoup to scrape a novel from the Project Gutenberg website. After scraping and cleaning the text data, you will use NLP to find the most frequent words in Moby Dick. The project introduces you to the world of Python web scraping and natural language processing.

For data analysts and data scientists, web scraping is an essential skill to learn. You can take a short Web Scraping with Python course to understand the tools and components of an HTML web page. 

Master NLP in Python Today

Data manipulation projects, 3. exploring the bitcoin cryptocurrency market.

In the Exploring the Bitcoin Cryptocurrency Market project, you will explore bitcoin and other cryptocurrency data. You will clean the dataset by discarding cryptocurrencies without market capitalization, comparing Bitcoin with other currencies, and preparing data for visualization.  

You can apply similar methods to Stock Exchange Data and learn to manipulate the data for data analysis . Furthermore, you can learn data transformation, aggregation, slicing, and indexing by taking Data Manipulation with pandas course. 

4. Analyze Your Runkeeper Fitness Data

In the Analyze Your Runkeeper Fitness project, you will import, clean, manipulate, and analyze the seven years of Runkeeper’s training data. It is a perfect sample project for learning complex data filtering, transformation, and processing. Furthermore, you will analyze the data and present a detailed summary report. 

You can apply similar methods to NBA Shooting Data and gain more experience in data manipulation and analysis, or you can take Manipulating Time Series Data in Python course for handling complex time series datasets. 

Data Visualization Projects

5. visualizing covid-19.

In the Visualizing COVID-19 project, you will visualize COVID-19 data using the most popular R library ggplot . You will analyze confirmed cases worldwide, compare China with other countries, learn to annotate the graph, and add a logarithmic scale. The project will teach you skills that are in high demand for R programmers. 

Image from the project

You can apply ggplot methods to Measles Data and gain more experience in data visualization and analysis. Moreover, you can take Intermediate Data Visualization with the ggplot2 course to learn the best data visualization practices. 

6. The Android App Market on Google Play 

In the Android App Market on Google Play project, you will import, clean, and visualize the Google Play Store data to understand the Android app market. 

• Clean the data.
• Correct the data types.
• Explore app categories.
• Understand the distribution of app ratings, size, popularity, and price. 
• Perform sentiment analysis on users' reviews. 

You can apply lessons learned from the project on a similar dataset: Google Play Store Apps to gain more experience in visualizing and exploring the data. 

Using the code to display interactive data visualization is easy, but understanding and interpreting the data is hard. Take the Understanding Data Visualization course to explain visualization distribution and learn the best data visualization techniques to communicate complex data.  

Advanced Data Analytics Projects

For more advanced data analytics projects, you need command over mathematics, probability, and statistics. Furthermore, you will perform exploratory data and predictive analytics to understand the data in detail.

• Probability & Statistics : perform mean, median, standard deviation, probability distribution algorithms, and correlation on the data. 
• Exploratory Data Analysis : explore the data distribution, understand various types of columns, and understand trends and patterns. 
• Predictive Analytics : perform regression, classification, clustering, and forecasting using machine learning algorithms.  

Probability & Statistics Projects

7. real-time insights from social media data.

For the Real-time Insights from Social Media project, you will use various statistical tools to dive deep into Twitter’s hot trends. You will understand local and global patterns and common trends and perform frequency and language analysis. 

You can apply similar methods used in the project on a new dataset: Internet News and Consumer Engagement , and learn more about analyzing news trends and consumer behavior. 

8. Find Movie Similarities from Plot Summaries

In the Find Movie Similarity from Plot Summaries project, you will use NLP and clustering to find the similarities between movie plots. It is the perfect project if you are looking to apply statistical techniques to text data. 

You will import two datasets, combine them, perform tokenization and stemming, convert words into vectors, and use KMeans to perform clustering. Furthermore, you will calculate similarity distance and visualize the results using Matplotlib, Linkage, and Dendrograms.

Use your lessons from the project and apply them to a new dataset: Netflix Movie Data , to get experience applying statistics to text data.

If you are interested in learning about the most common statistical techniques, probability, data distribution, correlation, and experimental design, take the Introduction to Statistics in Python course.

Exploratory Data Analysis (EDA) Projects

9. analyze international debt statistics.

In the Analyze International Debt Statistics project, you will write SQL queries to explore and analyze international debt using the World Bank dataset. SQL is the most popular and essential tool for performing data analytics on the go. 

In the project, you will be finding the:

• Distinct countries
• Distinct debt indicators
• Total amount of debt owed by the countries
• Country with the highest debt
• Average amount of debt across indicators
• The highest amount of principal repayments
• The most common debt indicator

You will connect World Nations MariaDB dataset and apply similar queries to get additional experience in handling and analyzing SQL databases. Additionally, you can Exploratory Data Analysis in SQL course to advance techniques and queries in handling various SQL databases. 

10. Investigating Netflix Movies and Guest Stars in The Office

In the Investigating Netflix Movies and Guest Stars in The Office project, you will use data manipulation and visualization to solve a real-world data science problem. You will perform deep exploratory data analysis and draw conclusions from detailed graphs. 

You can work on a portfolio project by applying similar skills to a new dataset: Netflix Movie Data . Furthermore, you can take Exploratory Data Analysis in Python to learn more about data cleaning and validation, understand the relationship and distribution, and explore multivariate relationships.

Predictive Analytics Projects

11. functions for food price forecasts.

In the Functions for Food Price Forecasts project, you will perform predictive analysis of food prices in Rwanda. You will import, wrangle, manipulate, and forecast data using R packages. It is perfect for beginners and professionals who are getting started with the R language and predictive analysis. 

You can create a time-series forecast R function for Loan Data and get better at performing predictive analysis using R packages. Moreover, you can complete the Forecasting Product Demand in R course to learn more about forecasting demand with time series, blending regression, and hierarchical forecasting.

12. Predicting Credit Card Approvals

In the Predicting Credit Card Approvals project, you will build the best-performing machine learning model for predicting credit card application approvals. 

First, you will understand the data and impute missing values. After that, you will preprocess the data and train a logistic regression model on the training set. In the end, you will evaluate the results and improve the model performance using Grid searching.

Applying simple machine learning algorithms is an essential part of a data analyst’s life. You can gain more experience by applying similar methods to a new dataset: Bank Marketing . 

Learn more about classification, regression, fine-tuning, and preprocessing by taking a short Supervised Learning with the scikit-learn course.

Become a ML Scientist

Data analytics projects for final year students.

Final year student projects are usually research-based and require at least 2-3 months to complete. You will be working on a specific topic and trying to improve the results using various statistical and probability techniques.  

Note : there is a growing trend for machine learning projects for data analytics final-year projects.  

13. Reducing Traffic Mortality in the USA

For the Reducing Traffic Mortality in the USA project, you will find a good strategy for reducing traffic-related deaths in the USA. You will be importing, cleaning, manipulating, and visualizing the data. Furthermore, you will perform feature engineering and apply various machine learning models (multivariate linear regression, KMeans clustering) to come up with stately and communicate the results. 

If you want to learn more about unsupervised learning, check out Cluster Analysis in Python course. 

14. Classify Song Genres from Audio Data

In the Classify Song Genres from Audio project, you will apply machine learning algorithms to classify songs into genres.

You will be:

• Preparing the dataset
• Splitting them into train and test sets
• Normalize the data
• Apply PCA on scaled data
• Train decision tree and logistic regression compare the performance
• Balance the data for better performance
• Understanding model bias
• Apply cross-validation to evaluate our models

Image by Author | Result from the project

Learn more about logistic regression and SVM by taking Linear Classifiers in Python course.

15. World Population Analysis 

The World Population Analysis project is the best example of performing deep exploratory analysis. You will be exploring various columns, visualizing the least and most populated countries, and exploring population density and growth rate. Furthermore, you will display the country rank distribution and correlation map. 

Learn easy ways to plot data visualization in Python by completing Intermediate Data Visualization with Seaborn course. 

16. Data Science and MLOps Landscape in Industry

The Data Science and MLOps Landscape in Industry project is a holy grail for all data manipulation, visualizations, and exploratory and geospatial analysis. You will learn to effectively use box plots, doughnut charts, bar charts, heatmaps, parallel categorical graphs, bubble charts, funnel charts, radar charts, icicle charts, and maps. Furthermore, you will learn to interpret various types of graphs. 

Take Introduction to Data Visualization with Plotly in Python course to learn about advanced Plotly features and customization. 

End-to-end Data Analytics Projects

End-to-end projects are great for your resume and understanding of the data analytic project life cycle. 

In general, you will be:

• Dealing with multiple datasets
• Understanding the data distribution
• Applying data cleaning and manipulation
• Applying probability and statical techniques
• Performing data analysis and visualization
• Using machine learning model for predictive analysis
• Creating the report or dashboard

17. Predict Taxi Fares with Random Forests

In the Predict Taxi Fares with Random Forests project, you will be on taxi journeys in New York and use tree-based models to predict the value of fares and tips, based on location, date, and time. Furthermore, you will get familiar with popular R packages dplyr , ggplot2 , and randomForests . 

You will clean the data and visualize it on the map to understand ride hotspots, predict taxi fares using tree and random forest models, display the prediction on the map, and analyze the results. 

If you are new to R language tree-based models, take the Machine Learning with Tree-Based Models with R course and become an expert. 

18. Mobile Games A/B Testing with Cookie Cats

A/B testing new features and products is a crucial part of a data analyst's job. In the Mobile Games A/B Testing with Cookie Cats project, you will analyze the result of A/B testing, where the first gate in Cookie Cats was moved from level 30 to level 40.

In detail, you will need to apply data manipulation, statistics, data visualization, and critical thinking to decide which version is the best. 

If you are new to A/B testing, try completing the Customer Analytics and A/B Testing in Python course to understand KPIs, explore and visualize customer behavior and application and analyze the results of A/B testing. 

19. An End-to-End Project on Time Series Analysis and Forecasting with Python

In the Time Series Analysis and Forecasting project, you will dive deep into analyzing the trends, apply the ARIMA model for forecasting, compare the results, and visualize the results to understand the sales for both furniture and office supplies.

Time-series analysis and forecasting projects are in high demand in financial sectors, and they will help you land a high-paying job. The only thing you need to do is to interpret various trends and accurately forecast the numbers. 

Note : financial analysis and forecasting is a high-paying job, but it is the hardest job too. 

If you are struggling to analyze and forecast, try completing ARIMA Models in Python course to learn about ARMA models, fitting the future, selecting the best models, and training seasonal ARIMA models.

20. Build a multi-objective recommender system

The goal of Build a multi-objective recommender system project is to predict e-commerce clicks, cart additions, and orders. In short, you will be creating a multi-objective recommender system based on previous events in a user session.

Upon completing the project, you will master:

• Data manipulation and analysis
• Understand sessions and events
• Data visualization and reporting
• Handling time series data
• Analyze time series data to explore user behavior
• Predict top clicks, carts, and orders

Supporting Your Team's Growth with DataCamp for Business

While individual projects are essential for personal skill development, organizations also need to ensure their teams are well-equipped to handle the complexities of data analytics. DataCamp for Business offers tailored solutions that help companies upskill their employees in data science, analytics, and machine learning. With access to a vast library of interactive courses, custom learning tracks, and real-world projects, teams can advance their skills in data ingestion, cleaning, manipulation, visualization, and predictive analytics—all key areas highlighted in this blog.

Whether you're a small startup or a large enterprise, DataCamp for Business provides the tools to upskill, reskill, and create a data-driven culture to stay competitive in today's market. You can request a demo today to learn more. 

After learning essential skills, you need to build a strong portfolio to showcase your knowledge. Furthermore, you will be learning new tools, features, and concepts that are beneficial for your professional life. 

In this post, we have learned about beginner-friendly projects, advanced projects, final-year student projects, and end-to-end data analytics projects. Moreover, we have covered projects on data ingestion and cleaning, probability and statistics, data manipulation and visualization, and exploratory data and predictive analysis. 

So, what’s next? After completing at least 12 projects, try to Get certified as a Professional Data Analyst . It will increase your odds of getting hired. You can also check out our post on how to become a data analyst for more career tips. 

Become an ML Scientist

Upskill in Python to become a machine learning scientist.

As a certified data scientist, I am passionate about leveraging cutting-edge technology to create innovative machine learning applications. With a strong background in speech recognition, data analysis and reporting, MLOps, conversational AI, and NLP, I have honed my skills in developing intelligent systems that can make a real impact. In addition to my technical expertise, I am also a skilled communicator with a talent for distilling complex concepts into clear and concise language. As a result, I have become a sought-after blogger on data science, sharing my insights and experiences with a growing community of fellow data professionals. Currently, I am focusing on content creation and editing, working with large language models to develop powerful and engaging content that can help businesses and individuals alike make the most of their data.

Data Analysis Courses

.css-1531qan{-webkit-text-decoration:none;text-decoration:none;color:inherit;} Exploratory Data Analysis in Python

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DATA 275 Introduction to Data Analytics

• Getting Started with SPSS
• Variable View
• Option Suggestions
• SPSS Viewer
• Entering Data
• Cleaning & Checking Your SPSS Database
• Recoding Data: Collapsing Continuous Data
• Constructing Scales and Checking Their Reliability
• Formatting Tables in APA style
• Creating a syntax
• Public Data Sources

Data Analytics Project Assignment

• Literature Review This link opens in a new window

For your research project you will conduct data analysis and right a report summarizing your analysis and the findings from your analysis. You will accomplish this by completing a series of assignments. 

Data 275 Research Project Assignment

In this week’s assignment, you are required accomplish the following tasks:

1. Propose a topic for you project

The topic you select for your capstone depends on your interest and the data problem you want to address. Try to pick a topic that you would enjoy researching and writing about.

Your topic selection will also be influenced by data availability. Because, this is a data analytics project, you will need to have access to data. If you have access to your organization’s data, you are free to use it. If you choose to do so, all information presented must be in secure form because Davenport University does not assume any responsibility for the security of corporate data. Otherwise, you can select a topic that is amenable to publicly available data.

Click the link for some useful suggestions: Project Proposal Suggestions 

2. Find a data set of your interest and download it

There are many publicly available data sets that you can use for your project. The library has compiled a list of many possible sources of data. Click on the link below to explore these sources. 

Public Data Sources 

The data set you select must have:

At least 50 observations (50 rows) and at least 4 variables (columns) excluding identification variables At least one dependent variable

You must provide:

A proper citation of the data source using APA style format A discussion on how the data was collected and by whom The number of variables in the data set The number of observations/subjects in the data set A description of each variable together with an explanation of how it is measured (e.g. the unit of measurement).

Deliverable

A minimum of one page description of your data analytics project which must include the following:

A title for your project A brief description of the project Major stakeholders who would use the information that would be generated from your analysis and how they would use/benefit from that information A description of the dataset you will use for your project

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BUS431: Business Data Analysis

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Business Analytics Assignment On The Consumption Of Cosmetics

Task 1- Background information Write a description of the selected dataset and project, and its importance for your chosen company. Information must be appropriately referenced.

Task 2 – Perform Data Mining on data view Upload the selected dataset on SAP Predictive Analysis. For your dataset, perform the relevant data analysis tasks on data uploaded using data mining techniques such as classification/association/time series/clustering and identify the BI reporting solution and/or dashboards you need to develop for the operational manager of the chosen company

Task 3 – Research Justify why you chose thee BI reporting solution/dashboards/data mining technique in Task 3 and why those data sets attributes are present and laid out in the fashion you proposed (feel free to include all other relevant justifications).

Note: To ensure that you discuss this task properly, you must include visual samples of the reports you produce (i.e. the screenshots of the BI report/dashboard must be presented and explained in the written report; use ‘Snipping tool’), and also include any assumptions that you may have made about the analysis from Task 3.

Task 4 – Recommendations for CEO The CEO of the chosen company would like to improve their operations. Based on your BI analysis and the insights gained from your “Dataset” in the lights of analysis performed in previous tasks, make some logical recommendations to the CEO, and justify why/how your proposal could assist in achieving operational/strategic objectives with the help of appropriate references from peer-reviewed sources.

Task 5 – Cover letter Write a cover letter to the CEO of the chosen firm with the important data insights and recommendations to achieve operational/strategic objectives.

Other Tasks – Please refer to the marking scheme at the end of the assignment for other tasks and expectations.

1.0 Introduction The excellence business has kept on developing and flourish as of late, with the monetary downturn doing little to decrease British purchasers' energy and hunger for new items and creative increments to their own consideration routines. Yet, it's not simply items that are developing. Clients' preferences and wishes are likewise continually in motion.

Excellence as an idea, not to mention an industry, has experienced immense changes throughout the years. What we see to be delightful, in vogue and even worthy is continually moving, starting patterns and styles that are presently everlastingly connected with a minute in time. You just need to think back to the excitement of the forties or the unmistakable style of the sixties to perceive how much changes in only a couple of decades, and usually these looks and patterns that we partner with those times as much as any verifiable or social occasion.

The drivers behind these progressions discussed in this business analytics assignment are regularly driven by the business, with design houses or famous people managing a specific style or look which is then received and spread by real brands. In any case, we ought not belittle the impact or capability of clients themselves, and how their inclinations and necessities can manage the bearing that the business takes as far as item advancement.

2.0 Project Overview With the end goal of this business analytics assignment we are concentrating exclusively on the female market. In this way our potential clients base (to create projections) does exclude any insights or arrangements for male purchasers. We have utilized the statistic report for spa goers directed by spa week by week as a reason for our suppositions. In view of this study the spa goer is overwhelmingly female (85%), knowledgeable (46% went to school), and crosses salary levels (26% gain under $35,000; 32% win somewhere in the range of $35,000 and $74,999 and 42% procure over $75,000) (Laursen and Thorlund, 2016).

Utilizing this statistic as the reason for our approach analyzed in this assignment on business analytics we built up our potential client base with the accompanying parameters: Women with some school between the ages of 25 and 65. We totally limited ladies with no school, ladies somewhere in the range of 18 and 25, ladies more than 65 and the whole male populace (Holsapple et al. 2014).. It is evaluated that the female populace will develop at a rate of 5.18% every year from 2000 to 2025 (source: the U.S. evaluation department). This information is for the whole United States. Of the 33,642,000 ladies spoke to between the ages of 25 and 65 who went to school, 29,293,000 (87%) live in major CMSA's.3.0 Analytical Solution

Restorative excellence or cosmetics items are compound blends that are utilized to improve scent or presence of the human body. Aromas, shading and cosmetics beauty care products, antiperspirants, haircare, healthy skin, and sun care are sure items that are broadly accessible and are utilized by people. Retail locations that incorporate claim to fame stores, restrictive brand outlets and general stores are the significant dissemination channels. In the present period, online channels are additionally picking up fame among the clients.

Individual consideration and magnificence item deals are on the ascent and are anticipated to enroll a development from 3.5 to 4.5% somewhere in the range of 2015 and 2020. It is foreseen to reach USD 500 billion by 2020. The Asia Pacific records for a noteworthy offer in the worldwide individual consideration industry; expanding request in the district is ascribed to its protruding populace. In the U.S, developing Hispanic populace is driving interest for sumptuous individual consideration marks and will raise amid the conjecture years (Holsapple et al. 2014).

Excellence or corrective items industry is one of the segments that stayed unaffected, regardless of the variances in the economy. Corrective deals have kept up a specific volume all through its general items. The deal can be ascribed to expanding and reliable utilization of items, particularly by people. Individual consideration organizations are making their items accessible online at focused costs. The web affects each business class be it antiperspirant or shaving items. Clients are eager to buy the products that can come legitimately to them through internet retailing (Acito and Khatri, 2014).

Various clients are worried about natural effect of the products they use. In this manner, makers are tricking their potential purchasers by promoting their items as natural and manageable. These qualities are even featured on items names and are expanding their image prominence as discussed in this assignment on business analytics. Moreover, a moral segment of the business includes client's worries about the items testing on creatures (Dubey and Gunasekaran, 2015). Makers are dealing with every one of these elements for advancing their items and profit the advantages of such worthwhile industry.

Worldwide corrective items advertise is ordered as healthy skin items, hair care items, shading beautifying agents, scents, individual consideration items, and oral consideration items. Skincare item is foreseen to overwhelm the worldwide corrective items advertise amid the conjecture time frame attributable to its various variations, for example, cosmetics remover, depilatories, hand care, and facial consideration. In light of structure, worldwide restorative items advertise is ordered into arrangements, creams, moisturizers, salves, suspensions, tablets, powders, gels, sticks, and pressurized canned products. Gels are anticipated to observe greatest increases over the conjecture time allotment inferable from rising appropriation of the item in youths for hair gel and face wash (Lim et al. 2013).

In this segment of the assignment on business analytics, the analyst has exhibited two contrasting model of symptomatic courses of action spread out with the assistance of SAP Lumira analytics apparatus. While stooping the models, the ace endeavored to show the conceivable market areas, target clients, propelling channel principal and simplicity of spreading data about the thing in end thing plot. The keen model orchestrated with the assistance of SAP Lumira picture the information amassed through this examination survey (Vera-Baquero et al. 2013). Then again, the pivot charts orchestrated with the assistance of outperform wants demonstrates numerical figures.

3.1 Analytical Solution 1 [SAP Lumira] 3.1.1 Market Opportunities: The worldwide magnificence and individual consideration items advertise measure was esteemed at USD 455.3 billion of every 2017. It is foreseen to enlist a CAGR of 5.9% amid the conjecture time frame. The market is foreseen to step along a sound development track attributable to rising inclination for normal and natural individual consideration (NOPC) items, expanding appropriation of Augmented Reality (AR) in the magnificence business, developing interest for hostile to - maturing items, and prospering prominence of men's prepping items.

This market is ready to observe critical development over the gauge time frame inferable from a few variables. One of the unmistakable variables is developing inclination for NOPC items, since buyers presently lean toward items that contain common fixings.

3.1.2 Targeted Customers

The significance of socioeconomics can't be downplayed. Truth be told, business new businesses will at first accumulate statistic data to incorporate into their field-tested strategies with an end goal to raise seed capital, which is essentially imperative to propelling a business. Statistic data can include: age, area, sex, pay level, training level, conjugal or family status, occupation, ethnic foundation.

The organisation may likewise require neighborhood socioeconomics about what number of individuals claim autos or homes, who goes to school or what level of inhabitants are web or web based life clients. Besides, you can likewise consider the psychographics of the socioeconomics you are focusing on. These may include: identity, frames of mind, values, interests/diversions, ways of life, conduct.

Regardless Notwithstanding whether the economics delineate national or adjacent markets or little social occasions, for instance, those inside an age run, the information keeps up a key separation from the hit-and-miss publicizing so routinely utilized by various associations. As you can envision, the ROI is commonly unsuitable.

The procedure of deliberately deciding socioeconomics to distinguish perfect clients can frequently be difficult. Regardless, this is pivotal as certain promoting methodologies must be put into play that incorporate focused on item bundling, notices and valuing, among different variables.

3.1.3 Channel for campaigns All things considered, how about we investigate some regular socioeconomics and how advertisers may discover these at first valuable to distinguish target markets.

Age – This is a typical client statistic that chiefs use to fragment markets. An organization selling dietary enhancements may have practical experience in at least one wellbeing classes. The promoting plan could express the age bunches that are probably going to buy each sort of item highlighted in that classification. For instance, for a cancer prevention agent equation, advertisers could target people between the ages of 40 and 60 years. In view of statistical surveying, the item may be evaluated underneath normal, have the most recent bleeding edge fixings, and brag the most recent biotechnology.

Sexual orientation – Portioning markets as demonstrated by sex is another typical exhibiting framework. As a result of social trim and physical differences, folks and females have different prerequisites. Sexual direction division is typically found in the publicizing of ordinary prosperity and greatness things. Sexual orientation jobs have changed drastically throughout the years. Advertisers need to abstain from falling into conventional generalizations when showcasing wellbeing and magnificence items in this day and age. For instance, sports sustenance has changed drastically lately. Lady, presently like never before, are using a portion of the equivalent restless games sustenance items men use. For instance, protein equations and dinner substitutions are similarly well known among people. The equivalent can be said for nitric oxide sponsors for both male and female perseverance competitors.

Pay – This is an exceptionally viable statistic advertisers use in contriving their showcasing plans. Frequently, clusters with different pay levels make different tendencies. The improvement of different tendencies is, all things considered, as a result of moderateness and access. A couple of pros fight pay isn't the most strong measurement. A lower pay social affair might be the first to purchase another broad feeding formula if it abstains from the necessity for a couple of free product things. Strangely, inclinations can likewise move when lower salary bunches want upward versatility and purchase items that intrigue to those aspirations.

Training – You may see some cover with the salary statistic. The conviction is that advanced education prompts higher normal wages. In any case, training is likewise a statistic numerous advertisers interface with social class. Social class can be genuine or seen. For instance, individuals with advanced educations may see themselves to be in the upper white collar class. For instance, instructors regularly have advanced educations however won't have a relating upper white collar class salary. These individuals still may lean toward increasingly wealthy items and way of life.

Understand that various elements are considered in choosing the perfect target showcase for an advertising effort. All in all, the objective is to pursue the market that offers the best present or long haul benefit potential. Market measure, development potential, number of contenders and friends qualities are among the key variables. The bigger the market, the more potential to win benefit. Markets that are quickly developing and less aggressive additionally offer preferences.

3.1.4 Overall Strategies

4.0 Recommendations and Conclusion The magnificence business has kept on developing and flourish lately, with the financial downturn doing little to decrease British buyers' energy and hunger for new items and imaginative augmentations to their own consideration routines. In any case, it's not simply items that are developing. Clients' preferences and wishes are additionally continually in transition.

Excellence as an idea, not to mention an industry, has experienced tremendous changes throughout the years. What we see to be lovely, chic and even adequate is continually moving, starting patterns and styles that are presently perpetually connected with a minute in time. You just need to think back to the fabulousness of the forties or the distinct style of the sixties to perceive how much changes in only a couple of decades, and usually these looks and patterns that we partner with those periods as much as any verifiable or social occasion.

The drivers behind these progressions are regularly driven by the business, with design houses or big names managing a specific style or look which is then received and spread by significant brands. In any case, we ought not to belittle the impact or capability of clients themselves, and how their inclinations and requirements can manage the course that the business takes as far as item improvement.

Webb deVlam has directed research on three particular gatherings of female excellence purchasers: the sure ager, the new to normal and the baffled beginner. Instead of simply being characterized by age, riches or status, these customer types depend on disposition and certainty, and each gathering has clear issues and worries that they need their excellence items to address and correct.

Usually essentially alluded to as the "over 50s", there is an inclination to accept that this statistic just thinks about enemy of maturing and how to annihilate wrinkles. Our exploration uncovered a solid pattern of "sure agers" who are splendidly alright with their life arrange. They are not hoping to look to days of yore or recover youth, and for them it's increasingly about accomplishing skin wellbeing.

A large number of the ladies we addressed felt that a great deal of magnificence brands attempted to over-entangle their items and showcasing materials with logical equations and cases to make individuals look more youthful. In any case, this isn't a need for this gathering – they need items which will enable them to accomplish the skin they need, not return them to what they may have once had. Delicate, clear and solid were words that continued being rehashed all through these discussions.

Brands need to handle the unaddressed skin worries that the business has all the earmarks of being awkward facing, from grown-up beginning skin inflammation to postmenopausal skin. Those with develop skin are feeling disliked and under-adjusted and it's time that brands and the business in general set aside the effort to comprehend and provide food for the entire scope of issues, bogeymen and needs that drive this gathering.

These shoppers are probably going to have cash and time to spend on magnificence routines and put resources into items over and over, yet they are additionally less inspired by complex science and entangled aromas. Sure agers hunger for straightforwardness and immaculateness, both as far as item substance and the bundling and promoting that goes with them.

It is recommended in this assignment on business analytics that this gathering comprises of sure, guaranteed ladies who comprehend what they need and what suits their skin and their way of life. They have well-created excellence routines and are beginning to search for explicit items handling skincare and maturing. Normal items are speaking to this gathering, and they have begun to explore by means of huge high road brands, for example, Lush and The Body Shop.

Brands need to strike a fragile parity here. In spite of the fact that they need direction on which regular items are directly for them, an excess of data and whine will put this gathering off, as they are sure and educated and searching for a utilitarian, reasonable answer for their magnificence issues.

They are probably going to drive or working all day and thusly requiring handy solution items that will enable them to keep up their look with least exertion. As far as discussed in this business analytics assignment bundling and brand configuration, clear correspondence about the item's substance and beginning are fundamental, and items that fill more than one need will dependably engage these purchasers. Business Analytics assignments are being prepared by our business statistics assignment help experts from top universities which let us to provide you a reliable assignment help best service.

References Acito, F. and Khatri, V., 2014. Business analytics: Why now and what next?.

Duan, L. and Xiong, Y., 2015. Big data analytics and business analytics. Journal of Management Analytics, 2(1), pp.1-21.

Dubey, R. and Gunasekaran, A., 2015. Education and training for successful career in Big Data and Business Analytics. Industrial and Commercial Training, 47(4), pp.174-181.

Laursen, G.H. and Thorlund, J., 2016. Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.

Lim, E.P., Chen, H. and Chen, G., 2013. Business intelligence and analytics: Research directions. ACM Transactions on Management Information Systems (TMIS), 3(4), p.17.

Ragsdale, C., 2014. Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. Nelson Education.

Sharma, R., Mithas, S. and Kankanhalli, A., 2014. Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. European Journal of Information Systems, 23(4), pp.433-441.

Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.

Vera-Baquero, A., Colomo-Palacios, R. and Molloy, O., 2013. Business process analytics using a big data approach. IT Professional, 15(6), pp.29-35.

Wixom, B.H., Yen, B. and Relich, M., 2013. Maximizing Value from Business Analytics. MIS Quarterly Executive, 12(2).

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Solving Business Case Study Assignments For Data Scientists

This article was published as a part of the Data Science Blogathon.

Introduction

D ata scientists /engineers/Analysts are in huge demand and various large and small firms are hiring left right and centre for various roles. Entry to mid-level roles involves quite a lot of individual contributor roles as well, where individuals own a project or a solution end to end. Business case study assignments can be used as a proxy to understand candidates’ ability to work as individual contributors. Knowing the nuances of effectively solving a case study assignment can surely help in landing multiple job offers.

Data scientists hiring involves case studies and it is an effective way to judge a candidate’s eligibility for the role. It is mostly considered an elimination round, and about 80% of the candidates are filtered out. Usually, in business analytics case studies, coding knowledge, problem-solving ability, presentation skills and overall role fitness are evaluated. Some case studies might focus just on building models, while others might focus on insights and final outcomes and recommendations, and many eligible candidates fail to clear this round due to a lack to clear understanding as to what needs to be presented.

This article sheds some light on how to go about solving business analytics or data scientists using these case studies.

Problem Statement

About the Data – The client is a leading fashion retailer in Australia. Company A runs a display advertising campaign for this brand, where it shows ads to users leading them to make a purchase on the brand’s website. The given dataset is the Sales data for all users who made a purchase online in the first half of October ’17. Link to download data.

• Overall – Total Sales and Revenue in the given time period
• Basket – Avg. unique quantity, revenue per order.
• Attributes – Time of Day, Day of Week, Geography, Payment Type
• Frequency – How many are single/multiple purchasers? What is the frequency of multi-purchase? Any typical attributes?
• Product Affinity – Which products are more likely to sell together?
• Based on your analysis, arrive at a statistical segmentation of the brand’s audience based on Revenue. The number of segments is up to you. Please provide definitions of each group.

Output and Requirements

• Overview of the brand’s sales performance

Product Affinity

Segmentation.

• Please include a slide or two on how would you broadly go about targeting more users for this brand. Come up with a well-defined media strategy.

Solutioning

Broadly below are the steps to be followed:

• Read the problem statement carefully a couple of times even before opening the dataset. Write down all ideas, questions and solution methods that come to your mind.
• Data preprocessing, validation. Identify data issues, incomplete data, or any other problem that can potentially derail the analysis.
• Create the skeleton of your presentation. This provides clarity of thought as to what needs to be done.
• Creating a master dataset, which has almost all the features/columns needed.
• Feature engineering if there is a need to create composite features.
• Create summaries and insights using excel, tableau public or python. Dashboards provide great visualization capabilities and are quite useful in finding insights as compared to excel.
• Create segments and identify their personas
• Using the Apriori data mining algorithm, identify product affinity.
• Once the outputs are validated, populate the PPT. Make sure uniform fonts and formats are used across slides.
• Make sure there is a story to tell.
• The important step is to QC your output more than once before sending it out. Keep aside at least 5% of your time to go through the final PPT, and identify errors, spelling mistakes, and formatting errors if any.

Load Dataset

Data Preprocessing

Data cleaning takes the most effort during work but is the least assessed skill in interviews. As there is no standard process, this step can be used to highlight programming as well as data processing skills. Below are the certain operations that need to be done, before segmentation or before using the apriori algorithm.

• Convert the timestamp column into DateTime format.
• Get date and time columns from DateTime.
• Filter products where the product name is undefined.
• Change Product Name, Product ID and Number of Products dtypes from object to a string.
• Get unique columns or primary key columns
• Create new features like coupon tags, hours, and days of the week. It’s advised to create an exhaustive list of variables while processing before removing on machine learning or data mining algorithms
• Prepare data that can be used for the apriori algorithm.

Update DateTime column type and get two new columns

Change product name, product id, and number of products dtypes from object to a string, identify the uniqueness of the data, convert to numeric and fillna, apriori preprocessing – product_name product_id number_of_products.

Multiple products from the same order are represented as a list, separated by commas, this is not an ideal input for the apriori algorithm. Product pairs from the same transaction are one way to represent data for the apriori algorithm. This has been done below.

Saved to CSV and apriori algorithm is run to get product affinity. The final output is at order_id, product level. An order with a single product isn’t very useful to the apriori algorithm and can be ignored as well.

Create new columns for coupon applied and time of the day

Top-down approach: Top-down approach uses already known business knowledge to build segments. For example – Segments of customers between the ages of 18 to 25 have more than 5 transactions with an average quantity of 2. The approach is neat, defines the segment clearly, has a clear objective and is easy to implement.

Bottom-up approach: Bottom-up approach identifies users with similar attributes and groups them as a segment. Once the segments are created, there cannot be direct actionability. These segments need to evaluate based on their metrics and identify and name those segments.

Due to the time constraints in getting these assignments out as quickly as possible, the Top-down approach has been chosen.

Summary metrics to create plots and charts

Which day of the week has the highest revenue, quantity and orders?

Is coupon redemption distributed equally during the day?

–> 0-7 Midnight-Morning, 8-13 First Half, 14-17 Second Half, >17 Night

A few other EDA questions that can be answered for the data scientists roles are :

• Which payment method is preferred by users and does it have any relationship with coupons?
• Is it ideal to give coupons on the day, when there is higher redemption or when there is lower redemption?
• Is there a specific pattern based on gender?
• Is the distribution normal?

Apriori Algorithm In SQL

Use Product_Details_002.csv as the input data. Converting the below formulas into SQL code to get product affinity pairs.

SQL Apriori code link .

PPT Template

PPTs should contain the below sub-sections:

• Title Slide
• Supporting slide with charts and insights
• Recommendation and Conclusion

Summary – Supporting slides with insights

Recommendation – Customer Persona

Recommendation – Targetting strategy

What’s Next?

Until HR calls for interviews for the data scientists roles, below are a few things that might help.

• Review possible questions that the interviewer might ask about the data scientists’ roles.
• Be through with the solution and reason for choosing a particular methodology.
• Play your own devil’s advocate and defend your solution.
• List the shortcomings of the solution clearly and keep possible alternative solutions handy.
• Know the algorithm used in detail. For example, if XGBoost is used, know the minute details for this algorithm and the reasons for choosing it over decision trees, or logistic regression.
• Why not use a clustering algorithm like Kmeans or DBSCAN?
• What are the mathematical formula for support, lift, and confidence? Can they be manipulated to consider quantity, and revenue as well?
• How can you measure or evaluate the performance of the top-down clustering approach?
• Explain Kmeans algorithm.

While case studies for data scientists can be a daunting task, it provides a clear picture of the role offered. It also shows that the firm is ready to ready to put in a lot of effort in selecting the best candidates as well. In summary :

• A case study is a preliminary round and used to filter unfit candidates, hence maximum efforts in terms of techniques(use multiple techniques) and presentation matter the most.
• Provide original ideas and solutions, don’t be limited by assumption.
• Consider challenges as opportunities to further improve the solution. For example, if certain data isn’t provided, provide information as to how this data would be useful and the opportunity lost by not having it.
• Not all solutions need to be practical, provide hypothetical solutions to show creativity.
• Presentation – just data, tables, and images on a PPT slide just aren’t enough. Callouts, writing a descriptive heading and providing clear cut bullet points, add great value and help make the message clear.
• Highlight the value proposition of the solution.
• The focus needs to be on getting a working solution and not achieving the perfect solution. There is no perfection in case study assignments.
• Try to showcase technical abilities, social and behavioural skills, or a combination of the two.
• Do not be shortsighted and only answer the questions asked, if there are opportunities to go beyond those questions, use them to showcase your skill. For example: In a data munging/wrangling case study, build a model as well. If time permits create an impressive dashboard.

Good luck! Here’s my Linkedin profile if you want to connect with me or want to help improve the article. Feel free to ping me on Topmate / Mentro ; you can drop me a message with your query. I’ll be happy to be connected. Check out my other articles on data science and analytics here .

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

Data scientist. Extensively using data mining, data processing algorithms, visualization, statistics, and predictive modeling to solve challenging business problems and generate insights. My responsibilities as a Data Scientist include but are not limited to developing analytical models, data cleaning, explorations, feature engineering, feature selection, modeling, building prototype, documentation of an algorithm, and insights for projects such as pricing analytics for a craft retailer, promotion analytics for a fortune 500 wholesale club, inventory management/demand forecasting for a jewelry retailer and collaborating with on-site teams to deliver highly accurate results on time.

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There are 4 modules in this course

In this course you will learn how to create models for decision making. We will start with cluster analysis, a technique for data reduction that is very useful in market segmentation. You will then learn the basics of Monte Carlo simulation that will help you model the uncertainty that is prevalent in many business decisions. A key element of decision making is to identify the best course of action. Since businesses problems often have too many alternative solutions, you will learn how optimization can help you identify the best option. What is really exciting about this course is that you won’t need to know a computer language or advanced statistics to learn about these predictive and prescriptive analytic models. The Analytic Solver Platform and basic knowledge of Excel is all you’ll need. Learners participating in assignments will be able to get free access to the Analytic Solver Platform.

Data Exploration and Reduction — Cluster Analysis

What's included.

7 videos 1 reading 2 quizzes 1 discussion prompt

7 videos • Total 53 minutes

• Introduction to the Course • 3 minutes • Preview module
• 0. What is Cluster Analysis • 7 minutes
• 1. Data Reduction and Unsupervised Learning • 7 minutes
• 2. Preparing Data and Measuring Dissimilarities • 8 minutes
• 3. Hierarchical and k-Means Clustering • 7 minutes
• 4. Cluster Analysis with Excel • 8 minutes
• 5. Cluster Analysis with XLMiner • 9 minutes

1 reading • Total 10 minutes

• Register for Analytic Solver Platform for Education (ASPE) • 10 minutes

2 quizzes • Total 60 minutes

• Week 1 Quiz • 30 minutes
• Week 1 Application Assignment - Clustering • 30 minutes

1 discussion prompt • Total 20 minutes

• Week 1 Discussion: Cluster Analysis in Nilsen Analytics Tools • 20 minutes

Dealing with Uncertainty and Analyzing Risk

7 videos 2 quizzes 1 discussion prompt

7 videos • Total 51 minutes

• 0. Risk Analysis and Monte Carlo Simulation • 7 minutes • Preview module
• 1. Adding Uncertainty to a Spreadsheet Model • 9 minutes
• 2. Defining Output Variables and Analyzing the Results • 7 minutes
• 3. Using Historical Data to Model Uncertainty • 7 minutes
• 4. Models with Correlated Uncertain Variables • 5 minutes
• 5. Creating and Interpreting Charts • 8 minutes
• 6. Using Average Values versus Simulation • 5 minutes
• Week 2 Quiz • 30 minutes
• Week 2 Application Assignment - Monte Carlo Simulation • 30 minutes

1 discussion prompt • Total 10 minutes

• Week 2 Discussion: How Betterment Holdings Inc. Used Monte Carlo Simulation • 10 minutes

Identifying the Best Options — Optimization

At the end of this module students should be able to: 1. Develop a spreadsheet model for an optimization problem 2. Use Excel to solve optimization models 3. Interpret solutions and conduct what-if analysis

7 videos • Total 54 minutes

• 0. Optimization and Decision Making • 5 minutes • Preview module
• 1. Formulating an Optimization Problem • 7 minutes
• 2. Developing a Spreadsheet Model • 7 minutes
• 3. Adding Optimization to a Spreadsheet Model • 5 minutes
• 4. What-if Analysis and the Sensitivity Report • 9 minutes
• 5. Evaluating Scenarios and Visualizing Results to Gain Practical Insights • 9 minutes
• 6. Digital Marketing Application of Optimization • 8 minutes

2 quizzes • Total 75 minutes

• Week 3 Quiz • 30 minutes
• Week 3 Application Assignment - Linear Optimization • 45 minutes
• Week 3 Discussion: How Amazon Uses Optimization for Its Supply Chain • 10 minutes

Decision Analytics

At the end of this module students should be able to: 1. Given a business situation, apply an appropriate technique to identify the best solution alternatives 2. Formulate and solve models for business problems that requires yes/no decisions and logical constraints 3. Create models that mix techniques and tools such as simulation and optimizationAnalyze and interpret results to make informed decisions

6 videos 2 quizzes 1 discussion prompt

6 videos • Total 39 minutes

• 0. Advanced Models for Better Decisions • 3 minutes • Preview module
• 1. Business Problems with Yes/No Decisions • 5 minutes
• 2. Formulation and Solution of Binary Optimization Problems • 7 minutes
• 3. Metaheuristic Optimization • 8 minutes
• 4. Chance Constraints and Value At Risk • 6 minutes
• 5. Simulation Optimization • 6 minutes
• Week 4 Quiz • 30 minutes
• Week 4 Application Assignment - Simulation Optimization • 45 minutes
• Week 4 Discussion: How Uber Optimized Its Dispatch System • 10 minutes

Instructor ratings

We asked all learners to give feedback on our instructors based on the quality of their teaching style.

CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.

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Learner reviews

Showing 3 of 1797

1,797 reviews

Reviewed on Jun 26, 2021

A good learning Platform and i have learned so many parameters and easy way to optimize Data with this course. Very happy to taken this course before and after.

Reviewed on Jul 11, 2017

The instructor is great. However, I would like to know the solutions for the exam. I sometimes pass the exam but struggle with certain questions but had no clue how to resolve it.

Reviewed on May 19, 2018

This course provides a brilliant overview of Business Analytics problems. Bit more details about the underlying mathematical techniques used would be helpful.

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