STARTS

Dec 2nd 2016

Regression Modeling in Practice (Coursera)

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This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.

Syllabus

Week 1: Introduction to Regression

Week 2: Basics of Linear Regression

Week 3: Multiple Regression

Week 4: Logistic Regression


Regression Modeling in Practice is course 3 of 5 in the Data Analysis and Interpretation Specialisation.

Learn SAS or Python programming, expand your knowledge of analytical methods and applications, and conduct original research to inform complex decisions. The Data Analysis and Interpretation Specialization takes you from data novice to data expert in just four project-based courses. You will apply basic data science tools and techniques, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python (including, but not limited to, the popular pandas and Scikit-learn python libraries). Throughout the Specialization, you will analyze a research question of your choice and summarize your insights. In the final Capstone Project, you will use real data to address an important issue in society, and report your findings in a professional-quality report. You will have the opportunity to work with our industry partner, DRIVENDATA, to help them solve some of the world's biggest social challenges by joining one of their competitions. Regular feedback from peers will provide you a chance to shape your question in new ways. This Specialization is designed to help you whether you are considering a career in data, work in a context where supervisors are looking to you for guidance about using data, or you just have some burning questions you want to explore. No prior experience is required, but by the end you will have mastered analytical methods and applications to conduct original research that can inform complex decisions.