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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.

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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.
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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

Business analytics practical capstone project

According to learners, the Business Analytics Capstone provides a highly practical application of the skills learned in the specialization. Many found the real-world scenario of tackling adblocking for a digital content provider to be engaging and relevant. Students appreciate the opportunity to synthesize knowledge from previous courses into a single, comprehensive project. While the peer review process is a core component, some learners reported it could be inconsistent, depending on the quality of feedback received. Overall, it is viewed as a valuable culmination that helps solidify understanding and can be a useful portfolio piece for career development.
Effectiveness varies based on reviewer quality.
"The peer reviews were helpful sometimes, but the quality of feedback varied wildly."
"I found the peer review valuable for getting different perspectives on my strategy."
"Receiving low-effort reviews from peers was frustrating and didn't help improve my work."
Integrates knowledge from prior courses.
"This capstone perfectly tied together all the concepts from the previous courses in the specialization."
"It was challenging but rewarding to see how the different analytical tools fit together in this project."
Offers real-world application of skills.
"The capstone project provided a great opportunity to apply everything learned in the specialization to a real-world business problem."
"Working through the adblocking scenario felt very relevant and helped solidify my understanding of how data analytics impacts strategic decisions."
"This project was the highlight for me, allowing me to synthesize concepts and produce something tangible."
Some found instructions could be clearer.
"Found the instructions for Module 4 slightly confusing regarding the measurement plan."
"The project structure was mostly well-defined, guiding us step-by-step."

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
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.
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  • 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
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
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
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Join a Study Group
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
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
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
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
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
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.

Career center

Learners who complete Business Analytics Capstone will develop knowledge and skills that may be useful to these careers:
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
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.
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.
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.
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.
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
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
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
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
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
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
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
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
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
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.

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.
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.
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.
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.
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.
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.

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