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Machine Learning

Master machine learning fundamentals in four hands-on courses

About This Specialization This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Created by: Industry Partners: 4 courses Follow the suggested order or choose your own. Projects Designed to help you practice and apply the skills you learn. Certificates Highlight your new skills on your resume or

Data Analysis Tools

Data Analysis Tools

About this course: In this course, you will develop and test hypotheses about your data. You will learn a variety of statistical tests, as well as strategies to know how to apply the appropriate one to your specific data and question. Using your choice of two powerful statistical software packages (SAS or Python), you will explore ANOVA, Chi-Square, and Pearson correlation analysis. This course will guide you through basic statistical principles to give you the tools to answer questions you have developed. Throughout the course, you will share your progress with others to gain valuable feedback and provide insight to other learners about their work.

Created by:   Wesleyan University

  • Jen Rose
    Taught by:    Jen Rose, Research Professor
    Psychology

  • Lisa Dierker
    Taught by:    Lisa Dierker, Professor
    Psychology
Basic Info
Language
English
How To PassPass all graded assignments to complete the course.
User Ratings
Average User Rating 4.4See what learners said
Syllabus
WEEK 1
Hypothesis Testing and ANOVA
This session starts where the Data Management and Visualization course left off. Now that you have selected a data set and research question, managed your variables of interest and visualized their relationship graphically, we are ready to test those relationships statistically. The first group of videos describe the process of hypothesis testing which you will use throughout this course to test relationships between different kinds of variables (quantitative and categorical). Next, we show you how to test hypotheses in the context of Analysis of Variance (when you have one quantitative variable and one categorical variable). Your task will be to write a program that manages any additional variables you may need and runs and interprets an Analysis of Variance test. Note that if your research question does not include one quantitative variable, you can use one from your data set just to get some practice with the tool. If your research question does not include a categorical variable, you can categorize one that is quantitative.

14 videos10 readings
  1. Video: Lesson 1 - The role of probability in inference
  2. Video: Lesson 2 - From sample to population
  3. Video: Lesson 3 - Steps in hypothesis testing
  4. Video: Lesson 4 - What is a p value?
  5. Video: Lesson 5 - How to choose a statistical test
  6. Video: Lesson 6 - Ideas behind ANOVA
  7. Reading: Choosing SAS or Python
  8. Reading: Getting Started with SAS
  9. Reading: Getting Started with Python
  10. Reading: Course Codebooks and Data
  11. Reading: Uploading Your Own Data to SAS
  12. Reading: SAS Program Code for Video Examples
  13. Video: SAS Lesson 7 - ANOVA: Explanatory variable with 2 levels
  14. Video: SAS Lesson 8 - ANOVA: Explanatory variables with more than 2 levels
  15. Video: SAS Lesson 9 - Post hoc tests for ANOVA
  16. Video: SAS Lesson 10 - ANOVA summary
  17. Reading: Python Program Code for Video Examples
  18. Video: Python Lesson 7 - ANOVA: Explanatory variables with two levels
  19. Video: Python Lesson 8 - ANOVA: Explanatory variables with more than 2 levels
  20. Video: Python Lesson 9 - Post hoc tests for ANOVA
  21. Video: Python Lesson 10 - ANOVA Summary
  22. Reading: Getting set up for the assignments
  23. Reading: Tumblr Instructions
  24. Reading: Example: Running an analysis of variance
Graded: Running an analysis of variance
WEEK 2
Chi Square Test of Independence
This session shows you how to test hypotheses in the context of a Chi-Square Test of Independence (when you have two categorical variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a Chi-Square Test of Independence. Note that if your research question only includes quantitative variables, you can categorize those just to get some practice with the tool.

7 videos3 readings
  1. Video: Lesson 1 - Ideas behind the Chi Square test of independence
  2. Reading: SAS Program Code for Video Examples
  3. Video: SAS Lesson 2 - Chi Square Test of independence in practice
  4. Video: SAS Lesson 3 - Post hoc tests for Chi Square tests of independence
  5. Video: SAS Lesson 4 - Chi Square summary
  6. Reading: Python Program Code for Video Examples
  7. Video: Python Lesson 2 - Chi Square test of independence in practice
  8. Video: Python Lesson 3 - Post hoc tests for Chi Square tests of independence
  9. Video: Python Lesson 4 - Chi Square summary
  10. Reading: Example: Running a Chi-Square Test of Independence
Graded: Running a Chi-Square Test of Independence
WEEK 3
Pearson Correlation
This session shows you how to test hypotheses in the context of a Pearson Correlation (when you have two quantitative variables). Your task will be to write a program that manages any additional variables you may need and runs and interprets a correlation coefficient. Note that if your research question only includes categorical variables, you can choose other variables from your data set just to get some practice with the tool.

4 videos2 readings
  1. Video: Lesson 1 - Pearson Correlation
  2. Video: Lesson 2 - Correlation Example
  3. Reading: SAS Program Code for Video Examples
  4. Video: SAS Lesson 3 - Calculating Correlation
  5. Reading: Python Program Code for Video Examples
  6. Video: Python Lesson 3 - Calculating Correlation
Graded: Generating a Correlation Coefficient
WEEK 4
Exploring Statistical Interactions
In this session, we will discuss the basic concept of statistical interaction (also known as moderation). In statistics, moderation occurs when the relationship between two variables depends on a third variable. The effect of a moderating variable is often characterized statistically as an interaction; that is, a third variable that affects the direction and/or strength of the relation between your explanatory (X) and response (Y) variable. Your task will be to test your own research question in the context of one or more potential moderating variables.

9 videos2 readings
  1. Reading: SAS Program Code for Video Examples
  2. Video: SAS Lesson 1 - Defining moderation, a.k.a. statistical interaction
  3. Video: SAS Lesson 2 - Testing moderation in the context of ANOVA
  4. Video: SAS Lesson 3 - Testing moderation in the context of chi square
  5. Video: SAS Lesson 4 - Testing moderation in the context of correlation
  6. Video: Python Lesson 1 - Defining moderation, a.k.a. statistical interaction
  7. Video: Python Lesson 2 - Testing moderation in the context of ANOVA
  8. Video: Python Lesson 3 - Testing moderation in the context of Chi-Square
  9. Video: Python Lesson 4 - Testing moderation in the context of correlation
  10. Reading: Python Program Code for Video Examples
  11. Video: A Question of Causation (Used with Permission from Annenberg Learner)
Graded: Testing a Potential Moderator
How It Works
Coursework
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
Wesleyan University
At Wesleyan, distinguished scholar-teachers work closely with students, taking advantage of fluidity among disciplines to explore the world with a variety of tools. The university seeks to build a diverse, energetic community of students, faculty, and staff who think critically and creatively and who value independence of mind and generosity of spirit.


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