Four-Project Series

Regression for Life Expectancy Analysis you own this product

intermediate Python • basics of statistics (regression analysis) • basics of NumPy and pandas
skills learned
perform data cleaning and exploration • perform regression modeling and diagnostics • perform model selection
Monica Guimaraes
4 weeks · 8-10 hours per week average · INTERMEDIATE

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5, 10 or 20 seats+ for your team - learn more

In this liveProject series, you’ll take on the role of a data analyst working for the Jones Family Philanthropic Foundation. The board of directors is interested in learning about the life expectancy of Americans so that they can better target their charitable spending.

Your challenges in this series of liveProjects include running a regression analysis on demographic data to find factors related to life expectancy, and answering data-mining questions about the distribution of these demographic variables. To do this, you’ll clean and model your data, assess the accuracy of your findings, and present your results—all with open-source tools from the Python ecosystem.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

Manning author Monica Guimaraes shares what she likes about the Manning liveProject platform.

here's what's included

Project 1 Demographic Data Analysis
In this liveProject, you’ll use the Python data ecosystem to explore how demographic data affects American life expectancy. You’ll use pandas and NumPy to clean and merge a newly collected data set Matplotlib, and seaborn to create a variety of plots that showcase the distribution of the data and expose the relationships between the variables.
Project 2 Detecting Unusual Data
In this liveProject, you’ll build your own Python data library that can detect unusual data. This data can affect regression analysis, and so your library will help spot it before it can negatively affect results. You’ll program functions to detect high-leverage points, outliers, and influential observations. Through the analysis of multiple examples, you'll gain a deep understanding of the kind of problems that can arise with real data.
Project 3 Regression Diagnostics
In this liveProject, you’ll build your own Python data library to check three of the assumptions of the regression model: normality, linearity, and constant variance. Confidence in your regression models depends on how well you have satisfied these assumptions. Once you’ve developed functions and plots that can check these assumptions, you’ll master techniques for correcting them. With this library, you will expand your data science toolbox with important diagnostics tools that will allow you to be confident in your results.
Project 4 Life Expectancy Analysis
In this liveProject, you’ll perform a regression analysis on precleaned data to determine factors relating to American life expectancy. You will learn how to do a comprehensive regression analysis and how to select a model in the presence of multicollinearity. You will use the libraries developed in the second and third projects to check the validity of your final model.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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project author

Monica Guimaraes
Monica Guimaraes has more than fifteen years of experience organizing and teaching undergraduate courses in automata theory, numerical analysis, programming languages, and statistics. Her passion for artificial intelligence goes back to her college years, having done her thesis in automatic-theorem-proving and working later as an AI researcher. She also has extensive experience in software development in corporations.


This liveProject is for confident Python programmers. To begin this liveProject you will need to be familiar with the following:

  • Intermediate Python
  • Basics of pandas
  • Basics of NumPy
  • Basic Jupyter Notebook
  • Basics of Python data analysis

you will learn

In this liveProject, you’ll learn vital skills for planning and orchestrating a data analysis project. These foundational skills are easy to transfer to almost any data undertaking.

  • Reading, cleaning and merging newly collected data with pandas
  • Exploring data for trends and distributions
  • Building data visualizations with Matplotlib and seaborn


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.