Business Analytics Specialization Course Reviews

The course covers a range of topics, including an introduction to data analysis and visualization, regression analysis, and hypothesis testing. Students will also learn about customer analytics, such as segmentation and customer lifetime value, and marketing analytics, including price optimization and promotion effectiveness.

In addition, the course covers operations analytics, including supply chain management and inventory optimization, and financial analytics, such as forecasting and risk management. Students will have the opportunity to work on hands-on projects, allowing them to apply the concepts they've learned in a practical setting.

By the end of the course, students will have a solid understanding of how to use business analytics to improve decision making, identify opportunities for growth, and enhance operational efficiency. They will be prepared to apply these skills in their own professional practice or pursue further education in business analytics.

Course Content:

This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.

The Business Analytics Specialization course, authored by Eric Bradlow and available on Coursera, consists of 5 individual courses series. The modules and the number of lectures in each module are as follows:

Course 1: Customer Analytics

There are 5 modules in this course

Mudule 1: Introduction to Customer Analytics (2 videos)

What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization? These short videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.

• Course Introduction and Overview•1 minute•Preview module
• Overview of the Business Analytics Specialization•1 minute

Module 2: Descriptive Analytics (7 videos + 2 readings + 1 quiz)

In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.

Module 3: Predictive Analytics (16 videos + 5 readings + 1 quiz)

Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.

Module 4: Prescriptive Analytics (7 videos + 1 reading + 1 quiz)

How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising. By the end of this module, you’ll be able to define a problem, define a good objective, and explore models for optimization which take competition into account, so that you can write prescriptions for data-driven actions that create success for your company or business.

Module 5: Application/Case Studies (8 videos + 2 readings + 1 quiz)

How do top firms put data to work? In this module, you’ll learn how successful businesses use data to create cutting-edge, customer-focused marketing practices. You’ll explore real-world examples of the five-pronged attack to apply customer analytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, and continuing all the way to data-driven decisions. At the end of this module, you’ll know the best way to put data to work in your own company or business, based on the most innovative and effective data-driven practices of today’s top firms.

Course 2: Operations Analytics

There are 4 modules in this course

Module 1: Introduction, Descriptive and Predictive Analytics (5 videos + 1 reading)

In this module you’ll be introduced to the Newsvendor problem, a fundamental operations problem of matching supply with demand in uncertain settings. You'll also cover the foundations of descriptive analytics for operations, learning how to use historical demand data to build forecasts for future demand. Over the week, you’ll be introduced to underlying analytic concepts, such as random variables, descriptive statistics, common forecasting tools, and measures for judging the quality of your forecasts.

Module 2: Prescriptive Analytics, Low Uncertainty (6 videos + 1 reading)

In this module, you'll learn how to identify the best decisions in settings with low uncertainty by building optimization models and applying them to specific business challenges. During the week, you’ll use algebraic formulations to concisely express optimization problems, look at how algebraic models should be converted into a spreadsheet format, and learn how to use spreadsheet Solvers as tools for identifying the best course of action.

Module 3: Predictive Analytics, Risk (4 videos + 1 reading)

How can you evaluate and compare decisions when their impact is uncertain? In this module you will learn how to build and interpret simulation models that can help you to evaluate complex business decisions in uncertain settings. During the week, you will be introduced to some common measures of risk and reward, you’ll use simulation to estimate these quantities, and you’ll learn how to interpret and visualize your simulation results.

Module 4: Prescriptive Analytics, High Uncertainty  (6 videos + 1 reading)

This module introduces decision trees, a useful tool for evaluating decisions made under uncertainty. Using a concrete example, you'll learn how optimization, simulation, and decision trees can be used together to solve more complex business problems with high degrees of uncertainty. You'll also discover how the Newsvendor problem introduced in Week 1 can be solved with the simulation and optimization framework introduced in Weeks 2 and 3.

Course 3: People Analytics

There are 4 modules in this course

Module 1: Introduction to People Analytics, and Performance Evaluation (11 videos + 2 readings)

In this module, you'll meet Professors Massey, Bidwell, and Haas, cover the structore and scope of the course, and dive into the first topic: Performance Evaluation. Performance evaluation plays an influential role in our work lives, whether it is used to reward or punish and/or to gather feedback. Yet its fundamental challenge is that the measures we used to evaluate performance are imperfect: we can't infer how hard or smart an employee is working based solely on outcomes. In this module, you’ll learn the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome, and see them at work in current companies, including an extended example from the NFL. By the end of this module, you’ll understand how to separate skill from luck and learn to read noisy performance measures, so that you can go into your next performance evaluation sensitive to the role of chance, knowing your environment, and aware of the four most common biases, so that you can make more informed data-driven decisions about your company's most valuable asset: its employees.

Module 2: Staffing (12 videos + 2 readings)

In this module, you'll learn how to use data to better analyze the key components of the staffing cycle: hiring, internal mobility and career development, and attrition. You'll explore different analytic approaches to predicting performance for hiring and for optimizing internal mobility, to understanding and reducing turnover, and to predicting attrition. You'll also learn the critical skill of understanding causality so that you can avoid using data incorrectly. By the end of this module, you'll be able to use data to improve the quality of the decisions you make in getting the right people into the right jobs and helping them stay there, to benefit not only your organization but also employee's individual careers.

Module 3: Collaboration (7 videos + 2 readings)

In this module, you'll learn the basic principles behind using people analytics to improve collaboration between employees inside an organization so they can work together more successfully. You'll explore how data is used to describe, map, and evaluate collaboration networks, as well as how to intervene in collaboration networks to improve collaboration using examples from real-world companies. By the end of this module, you'll know how to deploy the tools and techniques of organizational network analysis to understand and improve collaboration patterns inside your organization to make your organization, and the people working within in it, more productive, effective, and successful.

Module 4: Talent Management and Future Directions (9 videos + 2 readings)

In this module, you explore talent analytics: how data may be used in talent assessment and development to maximize employee ability. You'll learn how to use data to move from performance evaluation to a more deeper analysis of employee evaluation so that you may be able to improve the both the effectiveness and the equitability of the promotion process at your firm. By the end of this module, you'll will understand the four major challenges of talent analytics: context, interdependence, self-fulfilling prophecies, and reverse causality, the challenges of working with algorithms, and some practical tips for incorporating data sensitively, fairly, and effectively into your own talent assessment and development processes to make your employees and your organization more successful. In the course conclusion, you'll also learn the current challenges and future directions of the field of people analytics, so that you may begin putting employee data to work in a ways that are smarter, practical and more powerful.

Course 4: Accounting Analytics

There are 4 modules in this course

Module1: Ratios and Forecasting (9 videos + 2 readings)

The topic for this week is ratio analysis and forecasting. Since ratio analysis involves financial statement numbers, I’ve included two optional videos that review financial statements and sources of financial data, in case you need a review. We will do a ratio analysis of a single company during the module. First, we’ll examine the company's strategy and business model, and then we'll look at the DuPont analysis. Next, we’ll analyze profitability and turnover ratios followed by an analysis of the liquidity ratios for the company. Once we've put together all the ratios, we can use them to forecast future financial statements. (If you’re interested in learning more, I’ve included another optional video, on valuation). By the end of this week, you’ll be able to do a ratio analysis of a company to identify the sources of its competitive advantage (or red flags of potential trouble), and then use that information to forecast its future financial statements.

Module2: Earnings Management (6 videos + 2 readings)

This week we are going to examine "earnings management", which is the practice of trying to intentionally bias financial statements to look better than they really should look. Beginning with an overview of earnings management, we’ll cover means, motive, and opportunity: how managers actually make their earnings look better, their incentives for manipulating earnings, and how they get away with it. Then, we will investigate red flags for two different forms of revenue manipulation. Manipulating earnings through aggressive revenue recognition practices is the most common reason that companies get in trouble with government regulators for their accounting practices. Next, we will discuss red flags for manipulating earnings through aggressive expense recognition practices, which is the second most common reason that companies get in trouble for their accounting practices. By the end of this module, you’ll know how to spot earnings management and get a more accurate picture of earnings, so that you’ll be able to catch some bad guys in finance reporting!

Module3: Big Data and Prediction Models (7 videos + 2 readings)

This week, we’ll use big data approaches to try to detect earnings management. Specifically, we're going to use prediction models to try to predict how the financial statements would look if there were no manipulation by the manager. First, we’ll look at Discretionary Accruals Models, which try to model the non-cash portion of earnings or "accruals," where managers are making estimates to calculate revenues or expenses. Next, we'll talk about Discretionary Expenditure Models, which try to model the cash portion of earnings. Then we'll look at Fraud Prediction Models, which try to directly predict what types of companies are likely to commit frauds. Finally, we’ll explore something called Benford's Law, which examines the frequency with which certain numbers appear. If certain numbers appear more often than dictated by Benford's Law, it's an indication that the financial statements were potentially manipulated. These models represent the state of the art right now, and are what academics use to try to detect and predict earnings management. By the end of this module, you'll have a very strong tool kit that will help you try to detect financial statements that may have been manipulated by managers.

Module 4: Linking Non-financial Metrics to Financial Performance (8 videos + 2 readings)

Linking non-financial metrics to financial performance is one of the most important things we do as managers, and also one of the most difficult. We need to forecast future financial performance, but we have to take non-financial actions to influence it. And we must be able to accurately predict the ultimate impact on financial performance of improving non-financial dimensions. In this module, we’ll examine how to uncover which non-financial performance measures predict financial results through asking fundamental questions, such as: of the hundreds of non-financial measures, which are the key drivers of financial success? How do you rank or weight non-financial measures which don’t share a common denominator? What performance targets are desirable? Finally, we’ll look at some comprehensive examples of how companies have used accounting analytics to show how investments in non-financial dimensions pay off in the future, and finish with some important organizational issues that commonly arise using these models. By the end of this module, you’ll know how predictive analytics can be used to determine what you should be measuring, how to weight very, very different performance measures when trying to analyze potential financial results, how to make trade-offs between short-term and long-term objectives, and how to set performance targets for optimal financial performance.

Course 5: Business Analytics Capstone

There are 5 modules in this course

Module 1:  Module 1: Capstone Project Topic - The Problem of Adblocking

The Business Analytics Specialization was designed to help you learn how to think about using data in making big (and small) business decisions. In this Capstone project, you'll be asked to create a strategy for a fictional digital search engine and content provider, GoYaFace, Inc. (often abbreviated as “GYF”). The strategy will be used in responding to the increasing popularity and availability of “adblocking” software, which could have significant negative repercussions for GYF’s business. You are to assume the role of the leader of the Digital Advertising Tactics and Action (“DATA”) Team at GYF, which has been assigned the job of formulating GYF’s strategy in responding to the threat of adblocking. Your task is to develop a strategy that will be recommended to GYF’s senior leadership. Using what you've learned about business analytics, you'll (i) create a detailed problem statement focusing on GYF’s ad-buying customers (Module 2), (ii) develop a strategy (Module 3), (iii) describe the anticipated effects of the strategy (Module 4), and (iv) form a plan for measuring the effects of your strategy (Module 4). You'll then put these four pieces together into a final project (Module 5). First, please read the full description of the project in the “Project Description” link below, and then look at the background information about adblockers and the “GYF Company Profile” link in the content for Module 1. When you are ready to begin the first assignment, please move on to Module 2: Defining the Problem.

Module 2: Module 2: Defining the Problem (8 videos + 5 readings)

In Module 2, you'll define the problem adblockers poses for GYF. GYF is intended to be a composite of leading internet platform and content providers who derive substantial revenues from mobile advertising like Google, Yahoo, and Facebook, so you should frame your research around the real-world problems these companies have faced and are facing. Defining the problem thoroughly will have a direct impact on how successful your strategy will be received by your peers. The more deeply you consider the effects of adblockers on the companies that buy advertising space from GYF, the more appropriate your overall strategy is likely to be. Please use the resources below to find out more about the problem, and then create your Problem Statement and submit it for peer review below. You can and should draw from all of the Business Analytics Specialization courses, but your Problem Statement should focus on how adblockers might adversely affect GYF’s relationship with the companies that pay GYF to place advertisements on GYF’s mobile applications and content. You should consider the issue of causality in your Problem Statement - we've included some lectures from the underlying courses to refresh you on that topci. And you are strongly encouraged to complete and include a response to Application Exercise 1 (see link below) as part of your Problem Statement.

Module 3: Module 3: Your Strategy (7 videos + 3 readings)

In Module 3, you will focus on creating your recommended strategy for GYF to address adblockers. Your strategy does not have to be lengthy, but it must be clear, and it must address the problem. (Hint: if you have a clearly defined problem, your strategy is much more likely to be clearly defined as well). You'll be submitting your strategy for peer review, and then also reviewing the work of at least 3 of your peers. It's OK if reviewing the strategies of other learners in this course gives you further ideas for revising your own strategy. One of the primary benefits of peer review is to expand the range of feedback you can get, and we designed this Module around peer review so that you can get as much feedback as possible before moving on to the next phase of the project. You may find the resources and lectures below helpful in formulating your strategy and considering how data can be leveraged and appropriately understood. You are strongly encouraged to complete and include your response to Application Exercise 2 as part of your Strategy.

Module 4: Module 4: Effects of Your Strategy/Measuring these Effects (8 videos + 4 readings)

Module 4 was designed to give you the opportunity to focus on the effects of your strategy. Effects and Measurement can be often overlooked in strategy development; creating a thoughtful and thorough plan for measuring the effects will improve your final project tremendously. In this part of the project, you will describe two events: what you think will happen and how you will measure it. Look to the courses in the Business Analytics Specialization to see what kind of data companies use to measure effects to create a measurement plan of your own. You are strongly encouraged to complete and include your responses to Application Exercises 3 and 4 as part of your Effects and Measurement components. You may create a scenario (Operations Analytics) to predict some of the intended effects of your strategy, either following the outline of Application Exercise 3, or of your own design. Once you submit your own plan for effects and measurement, please review the work of at least three of your peers. You may find new ideas, or new ways of looking at data and measurement from this exercise. We encourage you to incorporate what you've learned into your final submission!

Module 5: Module 5: Final Project Submission (3 videos)

In this final Module, you will combine the four revised elements of your presentation (Problem Statement, Strategy, Effects, and Measurement, including any responses to the Application Exercises you've completed) into one presentation and submit it for peer review. You'll then be asked to review the work of at least three of your peers. Once you have gotten feedback on your plan, you may use it as an example of strategic thinking at your current job, or as a work sample when you are applying for a new one. A successful strategic analysis which describes the use of data-driven decision making will make you much more marketable in almost any field. Good luck!

• Applications: ROI (Customer Analytics)•11 minutes•Preview module
• Radically New Data Sets in Marketing (Customer Analytics)•7 minutes
• Analytics Applied: Kohl's, Netflix, AmEx and more (Customer Analytics)•18 minutes

Reviews:

As a former student of the Business Analytics Specialization by Eric Bradlow on Coursera, I can say that this course was an incredibly valuable learning experience. The specialization consists of four courses that cover topics such as data analysis, visualization, regression analysis, and predictive modeling.

One of the strengths of the course is its focus on practical applications. The assignments and projects require students to work with real-world data sets and to apply the techniques they have learned to solve real-world problems. This not only helps to reinforce the concepts covered in the courses, but also provides valuable experience that can be applied to a variety of industries and job roles.

The course materials are well-organized and easy to follow, with a good mix of videos, readings, and interactive assignments. The instructors are knowledgeable and engaging, and are always available to answer questions and provide feedback. The discussion forums provide a great opportunity for collaboration and discussion with other students.

One of the key skills that I developed during the course was the ability to use spreadsheet software such as Microsoft Excel to manipulate and analyze data. The course also introduced me to programming languages such as R and Python, which are powerful tools for data analysis and visualization.

The time commitment for the course is significant, with each course taking approximately four weeks to complete. However, the workload is manageable and the course is designed to be flexible, allowing students to work at their own pace.

Overall, I would highly recommend the Business Analytics Specialization to anyone looking to develop their skills in data analysis and visualization. The course provides a solid foundation in the principles and techniques of business analytics, and is an excellent investment in one's professional development.

At the time, the course has an average rating of 4.6 out of 5 stars based on over 15,710 ratings.

What you'll learn:

After completing the Business Analytics Specialization course, authored by Eric Bradlow and available on Coursera, students will have gained the following skills:

1. Data analysis: The courses in the specialization will teach students how to use statistical methods and tools such as regression analysis, hypothesis testing, and machine learning to analyze business data. They will learn how to manipulate data, identify trends and patterns, and draw conclusions from their analyses. They will also gain experience working with data analysis software such as R and Python.

2. Data visualization: Effective data visualization is essential for communicating insights to stakeholders. Students will learn how to use tools such as Tableau and ggplot2 to create compelling visualizations that are easy to understand. They will learn how to choose the appropriate visualization for different types of data and how to use color, labels, and annotations to highlight key findings.

3. Predictive modeling: Predictive modeling involves using statistical techniques to forecast future trends and make data-driven decisions. Students will learn how to build predictive models using regression analysis, time series analysis, and machine learning algorithms such as random forests and neural networks. They will also learn how to evaluate the accuracy and reliability of their models and how to use them to make predictions.

4. Business decision-making: The courses in the specialization are designed to help students make better business decisions by using data. They will learn how to identify key performance indicators (KPIs) for their organizations and how to track and analyze them. They will also learn how to use data to identify opportunities for growth and improvement and how to develop data-driven strategies to achieve their goals.

5. Communication: Effective communication is essential for sharing insights with stakeholders and driving organizational change. Students will learn how to communicate their findings in a clear and concise manner using visualizations, reports, and presentations. They will also learn how to tailor their communication style to different audiences, including executives, managers, and technical staff.

6. Problem-solving: The courses in the specialization are designed to help students develop a problem-solving mindset. They will learn how to approach business problems from an analytical perspective, how to identify the root causes of problems, and how to develop and implement data-driven solutions. They will also learn how to evaluate the effectiveness of their solutions and how to adjust their approach based on feedback.

Overall, completing the Business Analytics Specialization will provide students with a comprehensive set of skills in data analysis, data visualization, predictive modeling, business decision-making, communication, and problem-solving. These skills are highly valued in a variety of industries, including finance, healthcare, marketing, and technology, and can lead to rewarding and fulfilling careers.

Author:

Eric Bradlow is a well-respected professor of marketing, statistics, education, and economics at the University of Pennsylvania's Wharton School. He holds the K.P. Chao Professorship and is also the Faculty Director of Wharton Customer Analytics, a research center that focuses on the use of customer data to inform business decisions.

In addition to his academic work, Bradlow is a recognized expert in the field of business analytics. He has consulted for a wide range of companies, including Google, American Express, and Coca-Cola, helping them use data to make informed decisions and drive growth.

Bradlow has received numerous awards for his contributions to the field of marketing and analytics, including the John D.C. Little Award for the best marketing paper published in Marketing Science or Management Science, and the Paul E. Green Award for the best paper published in the Journal of Marketing Research.

As an educator, Bradlow is highly regarded for his ability to teach complex analytical concepts in a clear and engaging manner. He has developed and taught a number of popular courses on business analytics, marketing, and data analysis, both at Wharton and through online platforms such as Coursera.

Overall, Eric Bradlow is a respected academic and expert in the field of business analytics, with a proven track record of helping companies use data to drive growth and make informed decisions. His expertise, combined with his engaging teaching style, make him a valuable resource for anyone looking to develop their skills in this area.

Requirements:

The Business Analytics Specialization course by Eric Bradlow on Coursera has the following detailed requirements:

1. Basic math skills: Students should have a strong foundation in mathematics, including algebra, statistics, and probability theory. This will enable them to understand the concepts and techniques covered in the courses.

2. Familiarity with spreadsheets: Students should be comfortable using spreadsheet software such as Microsoft Excel or Google Sheets to manipulate and analyze data. This will be necessary for completing assignments and projects throughout the specialization.

3. Familiarity with programming: While not required, students who have some experience with programming languages such as R or Python will be better prepared for the course material. They will be able to more easily manipulate and analyze data, and will be able to apply more advanced analytical techniques.

4. Access to data: Students will need access to real-world data sets in order to complete the assignments and projects in the courses. This can be data from their own organizations, publicly available data sets, or data provided by the instructors.

5. Time commitment: The specialization consists of four courses, each taking approximately four weeks to complete. Students should be prepared to commit several hours per week to watching lectures, completing assignments, and participating in discussions. They should also be prepared to devote time to independent study and practice outside of the course materials.

6. English proficiency: All course materials and instruction are in English, so students should have a strong command of the language. They should be able to read, write, and speak English fluently in order to fully engage with the course content and participate in discussions.

7. Access to a computer: Students will need access to a computer with a reliable internet connection in order to participate in the courses. They should also have access to the necessary software, such as spreadsheet software and programming languages, as required by the course materials.

Overall, the Business Analytics Specialization requires a strong foundation in mathematics and spreadsheet skills, as well as a willingness to learn new programming and data analysis techniques. Students who meet these requirements and are willing to commit the necessary time and effort will gain valuable skills in data analysis, visualization, and predictive modeling that can be applied to a variety of industries and job roles.