• Cover all fundamental libraries for operation and manipulation of Python for data analysis
• Implement real-world datasets to perform predictive analytics with Python
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• Get a hands-on, fast-paced introduction to the Python data science stack
• Explore ways to create useful metrics and statistics from...

• Grasp all aspects of numerical computing and understand NumPy
• Explore examples to learn exploratory data analysis (EDA), regression, and clustering
• Access...

• Learn how to choose between imperative and functional approaches based on expressiveness, clarity, and performance
• Get familiar with complex concepts such as monads,...

• Learn to set up data analysis pipelines with pandas and Jupyter notebooks
• Effective techniques for data selection, manipulation, and...

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their...

• Get up and running with the Jupyter ecosystem and some example datasets
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• Implement different regression analysis techniques to solve common problems in data science - from data exploration to dealing with missing values
• From Simple Linear Regression...