Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data.

As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions.

About the Author

Stephen Klosterman is a machine learning data scientist at CVS Health. He enjoys helping to frame problems in a data science context and delivering machine learning solutions that business stakeholders understand and value. His education includes a Ph.D. in biology from Harvard University, where he was an assistant teacher of the data science course.

Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.

In parallel, since Aug 2017, she is also engaged in strategical group projects for the automotive company and with side contract as an analytical external consultant for different industries (retail, sensorics, building) at Leadership Synergy Community. She is also a data science trainer for Elderberry data, specialized in MS Excel and Knime analytics platform in both face-to-face and elearning forms (available on Udemy).