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Wharton Teaching Staff
The Business Analytics Capstone Project gives you the opportunity to apply what you've learned about how to make data-driven decisions to a real business challenge faced by global technology companies like Yahoo, Google, and Facebook. At the end of this Capstone, you'll be able to ask the right questions of the data, and know how to use data effectively to address business challenges of your own. You’ll understand how cutting-edge businesses use data to optimize marketing, maximize revenue, make operations efficient, and make hiring and management decisions so that you can apply these strategies to your own company or business. Designed with Yahoo to give you invaluable experience in evaluating and creating data-driven decisions, the Business Analytics Capstone Project provides the chance for you to devise a plan of action for optimizing data itself to provide key insights and analysis, and to describe the interaction between key financial and non-financial indicators. Once you complete your analysis, you'll be better prepared to make better data-driven business decisions of your own.
Register for this course and see more details by visiting:
OpenCourser.com/course/0vwwbq/business
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Syllabus
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.
Read more
Syllabus
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.
Read more
Module 2: Defining the Problem
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: Your Strategy
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: Effects of Your Strategy/Measuring these Effects
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: Final Project Submission
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!
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops analytical, problem-solving, and strategic decision-making skills that are central to data-driven decision-making
Uses case studies from global tech companies like Yahoo, Google, and Facebook to provide practical, industry-relevant examples
Taught by Wharton Teaching Staff, who are recognized for their work in business analytics and data science
Provides a comprehensive overview of business analytics, including data collection, analysis, visualization, and communication
Requires no prior knowledge of business analytics or data science
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Reviews summary
Well-structured business analytics capstone
Learners say that this course provides a well-structured capstone to the Business Analytics specialization offered by Coursera and Wharton. They especially value the opportunity to apply the concepts they've learned in earlier courses to a real-world business problem. Additionally, the course offers a comprehensive review of business analytics tools and techniques, making it a valuable resource for those looking to advance their careers in this field.
Course offers a wide range of business analytics topics.
"Five intensive courses, it was very useful and important to build my ability by increasing my knowledge, skills, competence and professionalism."
"This course helped me analyse a case-study and apply all the concepts that I learned in customer analytics, people analytics, operations analytics and accounting analytics."
"As a Finance professional, I find the Accounting and Operational Analytics courses very engaging."
Capstone project allows learners to apply theory to practice.
"A very good and a relevant course for folks who are willing to learn Business analytics."
"Solving a real world problem helps relate and apply your course knowledge which i an added advantage"
"Before joining this course i was not aware the different methods, techniques and tools of data analytics."
Course may struggle with plagiarism on assignments.
"I am utterly furious about how much plagiarism I encountered in this course."
"The disincentives to prevent plagiarism from Coursera are clearly not strong enough, and it really marred my experience of this course."
Course is reviewed based only on peer feedback.
"One stars for the capstone. Months pass by and no one reviews your project."
"Its really painful when other courses peer reviewed completed fast, Upenn specialization reviewed taking too much time without any valid reason."
"Why should my course completion be held back because other participants are not available to review my submission and provide feedback..."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Business Analytics Capstone with these
activities:
Review Calculus Basics
Show steps
Review the basics of calculus, such as limits, derivatives, and integrals. This will provide you with a stronger foundation for the more advanced statistical and analytical concepts covered in the course.
Browse courses on
Calculus
Show steps
-
Go over your notes from a previous course or textbook on calculus.
-
Complete practice problems on basic calculus concepts.
-
Review techniques for solving limits, derivatives, and integrals.
Review Data Analysis Fundamentals
Show steps
Review the fundamentals of data analysis and statistics. This will strengthen your foundation and help you apply the concepts learned in the course more effectively.
Browse courses on
Data Analysis
Show steps
-
Go over your notes from a previous course or textbook on data analysis.
-
Complete practice problems on basic statistical concepts such as mean, median, mode, and standard deviation.
-
Review techniques for data visualization, such as creating charts and graphs.
Compile a Glossary of Data Analysis Terms
Show steps
Create a glossary of key terms and concepts related to data analysis. This will help you build a strong foundation and clarify your understanding of the subject matter.
Show steps
-
Review the course materials and identify key terms and concepts.
-
Define each term in your own words.
-
Compile your definitions into a glossary.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a Study Group
Show steps
Connect with other students taking the course to form a study group. This will allow you to discuss the material, ask questions, and learn from each other.
Show steps
-
Reach out to classmates through the course forum or social media.
-
Set up a regular meeting time and place for your study group.
-
Review the course material together, discuss key concepts, and solve problems.
Explore Online Tutorials and Resources
Show steps
Supplement your learning by exploring online tutorials and resources related to the topics covered in the course. This will provide you with additional perspectives and insights.
Show steps
-
Search for online tutorials and resources on data analysis and business analytics.
-
Identify tutorials and resources that align with the topics covered in the course.
-
Go through the tutorials and resources to enhance your understanding.
Complete Practice Problems and Quizzes
Show steps
Practice applying the concepts learned through solving practice problems and quizzes. This will help you improve your problem-solving skills and solidify your understanding.
Show steps
-
Review the practice problems and quizzes provided in the course.
-
Attempt to solve the problems and quizzes on your own.
-
Check your answers and identify areas where you need improvement.
Attend a Data Analytics Workshop
Show steps
Attend a data analytics workshop to learn about the latest tools and techniques in the field. This will provide you with hands-on experience and allow you to connect with professionals in the industry.
Show steps
-
Research and identify data analytics workshops that are relevant to your interests.
-
Register for a workshop and attend the sessions.
-
Participate actively in the workshop and ask questions.
Mentor a Junior Data Analyst
Show steps
Share your knowledge and skills by mentoring a junior data analyst. This will help you solidify your understanding of the subject matter and provide valuable support to someone who is starting out in the field.
Show steps
-
Identify a junior data analyst who is looking for a mentor.
-
Establish regular meetings or communication channels.
-
Provide guidance and support on technical and career-related topics.
Develop a Data Analysis Plan
Show steps
Create a data analysis plan that outlines the steps you would take to analyze a real-world business problem. This will help you develop a deeper understanding of the data analysis process.
Show steps
-
Identify a business problem that you want to analyze.
-
Gather data relevant to the problem.
-
Explore and analyze the data using appropriate techniques.
-
Develop recommendations based on your analysis.
Review Calculus Basics
Show steps
Review the basics of calculus, such as limits, derivatives, and integrals. This will provide you with a stronger foundation for the more advanced statistical and analytical concepts covered in the course.
Browse courses on
Calculus
Show steps
Show steps
Show steps
- Go over your notes from a previous course or textbook on calculus.
- Complete practice problems on basic calculus concepts.
- Review techniques for solving limits, derivatives, and integrals.
Review Data Analysis Fundamentals
Show steps
Review the fundamentals of data analysis and statistics. This will strengthen your foundation and help you apply the concepts learned in the course more effectively.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Go over your notes from a previous course or textbook on data analysis.
- Complete practice problems on basic statistical concepts such as mean, median, mode, and standard deviation.
- Review techniques for data visualization, such as creating charts and graphs.
Compile a Glossary of Data Analysis Terms
Show steps
Create a glossary of key terms and concepts related to data analysis. This will help you build a strong foundation and clarify your understanding of the subject matter.
Show steps
Show steps
Show steps
- Review the course materials and identify key terms and concepts.
- Define each term in your own words.
- Compile your definitions into a glossary.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a Study Group
Show steps
Connect with other students taking the course to form a study group. This will allow you to discuss the material, ask questions, and learn from each other.
Show steps
Show steps
Show steps
- Reach out to classmates through the course forum or social media.
- Set up a regular meeting time and place for your study group.
- Review the course material together, discuss key concepts, and solve problems.
Explore Online Tutorials and Resources
Show steps
Supplement your learning by exploring online tutorials and resources related to the topics covered in the course. This will provide you with additional perspectives and insights.
Show steps
Show steps
Show steps
- Search for online tutorials and resources on data analysis and business analytics.
- Identify tutorials and resources that align with the topics covered in the course.
- Go through the tutorials and resources to enhance your understanding.
Complete Practice Problems and Quizzes
Show steps
Practice applying the concepts learned through solving practice problems and quizzes. This will help you improve your problem-solving skills and solidify your understanding.
Show steps
Show steps
Show steps
- Review the practice problems and quizzes provided in the course.
- Attempt to solve the problems and quizzes on your own.
- Check your answers and identify areas where you need improvement.
Attend a Data Analytics Workshop
Show steps
Attend a data analytics workshop to learn about the latest tools and techniques in the field. This will provide you with hands-on experience and allow you to connect with professionals in the industry.
Show steps
Show steps
Show steps
- Research and identify data analytics workshops that are relevant to your interests.
- Register for a workshop and attend the sessions.
- Participate actively in the workshop and ask questions.
Mentor a Junior Data Analyst
Show steps
Share your knowledge and skills by mentoring a junior data analyst. This will help you solidify your understanding of the subject matter and provide valuable support to someone who is starting out in the field.
Show steps
Show steps
Show steps
- Identify a junior data analyst who is looking for a mentor.
- Establish regular meetings or communication channels.
- Provide guidance and support on technical and career-related topics.
Develop a Data Analysis Plan
Show steps
Create a data analysis plan that outlines the steps you would take to analyze a real-world business problem. This will help you develop a deeper understanding of the data analysis process.
Show steps
Show steps
Show steps
- Identify a business problem that you want to analyze.
- Gather data relevant to the problem.
- Explore and analyze the data using appropriate techniques.
- Develop recommendations based on your analysis.
Career center
Learners who complete Business Analytics Capstone will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst uses data to build dashboards and create reports that help businesses understand their customers and make better decisions. This course can help you become a Data Analyst by teaching you how to collect, clean, and analyze data. You will also learn how to use data visualization tools to create dashboards and reports that are easy to understand.
Business Analyst
Business Analyst
A Business Analyst helps businesses improve their operations by analyzing data and identifying areas for improvement. This course can help you become a Business Analyst by teaching you how to collect, clean, and analyze data. You will also learn how to use data visualization tools to create dashboards and reports that are easy to understand.
Product Manager
Product Manager
A Product Manager is responsible for the development and launch of new products. This course can help you become a Product Manager by teaching you how to collect, clean, and analyze data. You will also learn how to use data to make decisions about product development and launch.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to understand customer behavior and develop marketing campaigns. This course can help you become a Marketing Analyst by teaching you how to collect, clean, and analyze data. You will also learn how to use data visualization tools to create dashboards and reports that are easy to understand.
Data Scientist
Data Scientist
A Data Scientist uses data to build models that can predict future events. This course can help you become a Data Scientist by teaching you how to collect, clean, and analyze data. You will also learn how to use machine learning algorithms to build models that can predict future events.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. This course can help you become a Financial Analyst by teaching you how to collect, clean, and analyze data. You will also learn how to use data to build financial models.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve business problems. This course can help you become an Operations Research Analyst by teaching you how to collect, clean, and analyze data. You will also learn how to use data to build models that can solve business problems.
Actuary
Actuary
An Actuary uses data to assess risk and calculate insurance premiums. This course can help you become an Actuary by teaching you how to collect, clean, and analyze data. You will also learn how to use data to build actuarial models.
Statistician
Statistician
A Statistician uses data to design experiments and analyze data. This course can help you become a Statistician by teaching you how to collect, clean, and analyze data. You will also learn how to use statistical methods to design experiments and analyze data.
Data Engineer
Data Engineer
A Data Engineer is responsible for the design and construction of data systems. This course may be useful for someone who wants to become a Data Engineer because it teaches how to collect, clean, and analyze data. You will also learn how to use data engineering tools to build data systems.
Software Engineer
Software Engineer
A Software Engineer is responsible for the design, development, and maintenance of software systems. This course may be useful for someone who wants to become a Software Engineer because it teaches how to collect, clean, and analyze data. You will also learn how to use data to design and develop software systems.
Systems Analyst
Systems Analyst
A Systems Analyst is responsible for the analysis, design, and implementation of computer systems. This course may be useful for someone who wants to become a Systems Analyst because it teaches how to collect, clean, and analyze data. You will also learn how to use data to design and implement computer systems.
Information Technology Specialist
Information Technology Specialist
An Information Technology Specialist is responsible for the installation, maintenance, and repair of computer systems. This course may be useful for someone who wants to become an Information Technology Specialist because it teaches how to collect, clean, and analyze data. You will also learn how to use data to install, maintain, and repair computer systems.
Computer Programmer
Computer Programmer
A Computer Programmer is responsible for the coding and maintenance of software systems. This course may be useful for someone who wants to become a Computer Programmer because it teaches how to collect, clean, and analyze data. You will also learn how to use data to code and maintain software systems.
Database Administrator
Database Administrator
A Database Administrator is responsible for the design, implementation, and maintenance of databases. This course may be useful for someone who wants to become a Database Administrator because it teaches how to collect, clean, and analyze data. You will also learn how to use data to design, implement, and maintain databases.
For more career information including salaries, visit:
OpenCourser.com/course/0vwwbq/business
Reading list
We've selected 19 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Business Analytics Capstone.
Provides an overview of data analytics principles, methods and practices for improving business decision-making.
Save
Explores the concepts, tools and techniques of predictive analytics for making better predictions in business and life.
Covers the fundamental concepts and techniques of data mining for business intelligence, including data preparation, exploration, modeling and evaluation.
Save
Provides a comprehensive introduction to R for data science. It covers a wide range of topics, including data manipulation, data visualization, and machine learning.
Provides a comprehensive introduction to Python for data analysis. It covers a wide range of topics, including data manipulation, data visualization, and machine learning.
Save
Offers a practical, step-by-step guide to using Microsoft Excel for marketing analytics, including data analysis, visualization, and modeling.
Provides a comprehensive introduction to econometrics. It covers a wide range of topics, including linear regression, time series analysis, and causal inference.
Provides a beginner-friendly introduction to Google Analytics, including tracking website traffic, analyzing data, and optimizing website performance.
Provides a comprehensive overview of statistics for business and economics. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Save
Offers a comprehensive guide to web analytics, including data collection, analysis, and interpretation for measuring website traffic, improving user experience, and optimizing digital marketing campaigns.
Provides a deep dive into advanced analytics techniques with Apache Spark, including machine learning, graph analysis, and real-time stream processing.
Provides a comprehensive introduction to deep learning for natural language processing. It covers a wide range of topics, including word embeddings, sequence models, and attention mechanisms.
Save
Explores the concepts, challenges and opportunities of big data, including data management, analysis, and visualization.
Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, including linear regression, logistic regression, and decision trees.
Explores the business applications of deep learning, including image and speech recognition, natural language processing, and predictive modeling.
Provides a comprehensive introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preparation, model building, and model evaluation.
Provides a comprehensive introduction to Bayesian data analysis. It covers a wide range of topics, including Bayesian inference, Bayesian modeling, and Bayesian computation.
Provides a comprehensive introduction to causal inference in statistics. It covers a wide range of topics, including graphical models, counterfactuals, and causal effects.
Provides an overview of data science for business, including data mining, machine learning, and statistical modeling for making informed decisions.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0vwwbq/business
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Effort
4 weeks of study, 4-5 hours/week
Level
Intermediate
Via
Coursera
Institution
University of Pennsylvania
Instructor
Wharton Teaching Staff
Language
English
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This course belongs to a
collection
called
Business Analytics. It's comprised of six courses:
- Accounting Analytics
- Operations Analytics
- People Analytics
- Business Analytics Capstone (viewing)
- Customer Analytics
- Analytics in Python
Good to know
Develops analytical, problem-solving, and strategic decision-making skills that are central to data-driven decision-making
Uses case studies from global tech companies like Yahoo, Google, and Facebook to provide practical, industry-relevant examples
Taught by Wharton Teaching Staff, who are recognized for their work in business analytics and data science
Provides a comprehensive overview of business analytics, including data collection, analysis, visualization, and communication
Requires no prior knowledge of business analytics or data science
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