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Cameron Dodd

This course takes a deep dive into the statistical foundation upon which marketing analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions.

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This course takes a deep dive into the statistical foundation upon which marketing analytics is built. The first part of this course will help you to thoroughly understand your dataset and what the data actually means. Then, it will go into sampling including how to ask specific questions about your data and how to conduct analysis to answer those questions.

Many of the mistakes made by Marketing Analysts today are due to a lack of understanding the concepts behind the tests they run, leading to incorrect tests or misinterpreting the results. This course is tailored to provide you with the necessary background knowledge to comprehend the "what" and "why" of your actions in a practical sense.

By the end of this course you will be able to:

• Understand the concept of dependent and independent variables

• Identify variables to test

• Understand the Null Hypothesis, P-Values, and their role in testing hypotheses

• Formulate a hypothesis and align it to business goals

• Identify actions based on hypothesis validation/invalidation

• Explain Descriptive Statistics (mean, median, standard deviation, distribution) and their use cases

• Understand basic concepts from Inferential Statistics

• Explain the different levels of analytics (descriptive, predictive, prescriptive) in the context of marketing

• Create basic statistical models for regression using data

• Create time-series forecasts using historical data and basic statistical models

• Understand the basic assumptions, use cases, and limitations of Linear Regression

• Fit a linear regression model to a dataset and interpret the output using Tableau and statsmodels

• Explain the difference between linear and multivariate regression

• Run a segmentation (cluster) analysis

• Describe the difference between observational methods and experiments

This course is designed for people who want to learn the basics of descriptive and inferential statistics and analytics in marketing.

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What's inside

Syllabus

Descriptive Statistics
This week you’ll get an overview of the Statistics for Marketing course and you will learn the basics of Descriptive Statistics and when to use them. You will also be introduced to Bayesian statistics. You will also get an overview of your capstone project and at the end of the week you will complete part one.
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Read about what's good
what should give you pause
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Optimized for business leaders, marketing professionals, product managers, data analysts, and career changers interested in a data-driven approach to marketing
Targets marketing professionals, business leaders, and data analysts who want to learn the basics of descriptive and inferential statistics and analytics
Suitable for learners who want to develop their data-driven decision-making skills in marketing
May benefit beginners seeking a foundational understanding of marketing analytics concepts
Offers a practical approach to marketing analytics, focusing on real-world applications
Taught by an experienced instructor with a strong background in marketing analytics

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Reviews summary

Marketing statistics: clear concepts, practical application

According to students, 'Statistics for Marketing' is a highly recommended course, particularly for marketing professionals seeking a strong foundation. Learners consistently praise the clear instructor explanations that demystify complex concepts and show their real-world marketing applicability. The course fosters conceptual understanding and focuses on interpreting results. The capstone project is a valuable practical application. While introducing Tableau for data modeling, some desired more hands-on exercises or found it a surface-level overview. A few noted pacing inconsistencies or suggested updating examples.
Focuses on conceptual application with tools, less on coding.
"While it introduces Tableau, I wish there were more hands-on exercises or clearer instructions for setting up environments."
"The practical application with tools was minimal... Needed more hands-on coding or direct software instruction."
"The Tableau integration was a good start, but actual coding examples (R/Python) would have been more beneficial for real-world scenarios."
Excellent for beginners, but may lack depth for advanced learners.
"The explanations were good for beginners, but for someone with a basic stats background, it might feel a bit slow at times."
"Not deep enough for someone truly wanting to master marketing statistics. It felt like a very surface-level overview."
"This is a good course, especially for beginners; it clarified many statistical concepts for me."
The capstone project effectively integrates and applies learning.
"The capstone project was a great way to apply what I learned."
"The peer review for the capstone was a unique and helpful experience."
"The capstone ties everything together and cemented my learning."
Directly applicable to real-world marketing challenges.
"This course provided an excellent foundation in statistics specifically for marketing."
"It demystified statistics for me and showed how directly applicable it is to real-world marketing challenges."
"Very useful course for a marketing professional... it definitely helps in everyday marketing analytics."
"I learned how to apply the principles in the real-world for my marketing role."
Provides a strong understanding of statistical concepts.
"The instructor was clear and concise, breaking down complex concepts into digestible pieces."
"The conceptual understanding provided was superb, really helping me grasp the 'why' behind the methods."
"Exactly what I needed! As someone who struggled with traditional stats courses, this one made it relevant and easy to understand for marketing contexts."
"I now feel much more confident in understanding and applying statistical concepts in my work."
Some found pacing inconsistent; minor content updates suggested.
"I found the pace inconsistent. Some parts felt rushed, others dragged."
"My main feedback would be to update some of the examples or software versions, as they felt slightly dated in some sections."
"I think explanations could have used more visual aids or interactive components to truly solidify concepts."

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 Statistics for Marketing with these activities:
Review and Organize Course Materials
Organize materials for effective revision.
Show steps
  • Review lecture notes, assignments, and readings.
  • Create summaries, flashcards, or mind maps for key concepts.
Read 'The Bayesian Way'
Become familiar with Bayesian Statistics before the course begins.
Show steps
  • Purchase or rent the physical or e-book.
  • Read the first three chapters.
Tableau Tutorials
Learn to use Tableau to visualize and analyze data.
Browse courses on Tableau
Show steps
  • Watch Tableau tutorials on descriptive and inferential statistics.
  • Practice creating visualizations using the techniques learned.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Exercises on Data Cleaning
Reinforce your understanding of data manipulation techniques.
Browse courses on Data Cleaning
Show steps
  • Use Python or R to clean a real-world dataset. Make sure to include data types and missing data.
  • Generate summary statistics for each variable after cleaning.
Create an Infographic
Provide a visual representation of key concepts.
Browse courses on Metrics
Show steps
  • Choose a topic from the course.
  • Identify key statistics, concepts, and relationships.
  • Create a visually appealing and informative infographic.
Attend a Marketing Analytics Workshop
Connect with professionals and learn about industry trends.
Browse courses on Marketing Analytics
Show steps
  • Identify relevant workshops on marketing analytics.
  • Register and attend the workshop.
Practice Hypothesis Testing
Build confidence in the hypothesis testing process.
Browse courses on Hypothesis Testing
Show steps
  • State the hypotheses.
  • Calculate the test statistic.
  • Calculate the p-value.
  • Interpret the results.
Marketing Analytics Project
Integrate various concepts learned in the course to solve a practical marketing analytics problem.
Browse courses on Marketing Analytics
Show steps
  • Define the business problem and objectives.
  • Collect and analyze data relevant to the business problem.
  • Develop and implement an appropriate marketing strategy.
Segmentation Project
Apply segmentation techniques to a real-world marketing problem.
Browse courses on Segmentation
Show steps
  • Choose a dataset.
  • Identify and define customer segments based on business goals.
  • Present the findings in a report.

Career center

Learners who complete Statistics for Marketing will develop knowledge and skills that may be useful to these careers:
Marketing Analyst
The Statistics for Marketing course is tailored to provide you with the statistical knowledge you need to be successful as a Marketing Analyst. The course covers topics such as descriptive and inferential statistics, hypothesis testing, and data modeling, all of which are essential skills for Marketing Analysts. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Statistician
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Statistician. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Data Analyst
As a Data Analyst, you will use your knowledge of statistics to collect, analyze, and interpret data. The Statistics for Marketing course will teach you the skills you need to succeed in this role. This course covers topics such as descriptive and inferential statistics, data modeling, and time-series forecasting. By taking this course, you will gain the skills and knowledge you need to succeed as a Data Analyst.
Analyst
The Statistics for Marketing course will provide you with the necessary statistical knowledge to be successful as an Analyst. The course covers topics such as descriptive and inferential statistics, linear regression, and time-series forecasting, all of which are essential skills for Analysts. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Market Researcher
The Statistics for Marketing course will provide you with a strong foundation for a career as a Market Researcher. The course covers topics such as descriptive and inferential statistics, hypothesis testing, and data modeling, all of which are essential skills for Market Researchers. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Quantitative Analyst
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Quantitative Analyst. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Consultant
The Statistics for Marketing course will provide you with the statistical knowledge you need to be successful as a Consultant. The course covers topics such as descriptive and inferential statistics, hypothesis testing, and data modeling, all of which are essential skills for Consultants. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Actuary
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as an Actuary. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Operations Research Analyst
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as an Operations Research Analyst. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Financial Analyst
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Financial Analyst. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Data Engineer
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Data Engineer. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Data Scientist
The Statistics for Marketing course will help you build a foundation in statistics, which is essential for a career as a Data Scientist. The course covers topics such as descriptive and inferential statistics, data modeling, and time-series forecasting. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Product Manager
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Product Manager. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Business Analyst
The Statistics for Marketing course will provide you with a strong foundation in statistics, which is essential for a career as a Business Analyst. The course covers topics such as descriptive and inferential statistics, probability, and data modeling. By taking this course, you will gain the skills and knowledge you need to succeed in this role.
Software Engineer
The Statistics for Marketing course may be useful for a career as a Software Engineer. The topics covered in the course, such as descriptive and inferential statistics and data modeling, are not directly related to software engineering. However, they may be helpful in developing the analytical and problem-solving skills that are essential for success in this role.

Reading list

We've selected 13 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 Statistics for Marketing.
Provides a comprehensive overview of marketing analytics and data-driven techniques using Python. It covers topics such as data collection, data cleaning, exploratory data analysis, statistical modeling, and predictive modeling. The book is written in a clear and concise style and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of marketing data science and modeling techniques using Python. It covers topics such as data collection, data cleaning, exploratory data analysis, statistical modeling, and predictive modeling. The book is written in a clear and concise style and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of predictive analytics for marketing. It covers topics such as data mining, machine learning, and predictive modeling. The book is written in a clear and concise style and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of time series analysis for marketing. It covers topics such as time series data, time series models, and time series forecasting. The book is written in a clear and concise style and is suitable for both beginners and experienced practitioners.
Provides a comprehensive overview of multivariate analysis for marketing. It covers topics such as cluster analysis, discriminant analysis, and factor analysis. The book is written in a clear and concise style and is suitable for both beginners and experienced practitioners.
Covers a wide range of marketing analytics topics and is written for a business audience with no prior experience in analytics. It can be read independently of this course or as a supplement to it, and it includes exercises and case studies that can be used to practice the concepts learned.
This textbook provides a comprehensive overview of econometrics and is written for a technical audience with a strong background in mathematics. It can be used as a reference or as a textbook for a more advanced course in econometrics.
Provides a comprehensive overview of research methods used in business and social science and is written for a graduate-level audience. It can be used as a reference or as a textbook for a more advanced course in research methods.
This textbook provides a comprehensive overview of linear regression analysis and is written for a technical audience with a strong background in mathematics.
This textbook provides a thorough overview of business statistics and is written for a technical audience with a strong background in mathematics. It can be used as a reference or as a textbook for a more advanced course in business statistics.
This advanced textbook covers a wide range of statistical learning methods and is written for a technical audience with a strong background in mathematics and statistics. It can be used as a reference or as a textbook for a more advanced course in marketing analytics.
Provides a gentle introduction to Bayesian statistics, the statistical theory underpinning the Meta course. It can be read independently of this course or as a reference, as a supplement to the material covered in week 1.

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