This course takes a deep dive into the statistical foundation upon which data 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 data 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
• 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.
Overview
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.
- Inferential Statistics
- This week you will be introduced to inferential statistics and how to define samples and populations for marketing. You’ll also be introduced to the concept of variables. At the end of the week you will complete part two of your capstone project.
- Designing Experiments and Testing Hypotheses
- In week three, you’ll dig into how to formulate and test appropriate hypotheses for your business goals. You’ll wrap up the week with part three of your capstone project.
- Data Modeling
- This week you’ll be introduced to various model families and how to create them using Tableau. You’ll also learn how to interpret the results of these models. You’ll complete the fourth and final part of your capstone project.
- Using Statistics in Real-World Settings
- This week you will combine and apply all the information you have learned throughout the course and finalize your capstone project. You’ll finish out the course by hearing from a marketing analyst about how they apply the principles you learned in this course in the real-world.
Taught by
Cameron Dodd
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5.0 rating, based on 1 Class Central review
4.7 rating at Coursera based on 273 ratings
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This course was a good refresher on
- false positive, false negative
- experiment vs observational study
There were also hands-on exercises in Google Sheets and Google Slides as you apply the concepts you learned and submit for grading during week 5.
