Become a Python Data Analyst

This video provides an overview of the entire course.

Explain what Anaconda Distribution is and why we are using it in this course. Also show how to get and install the software.

Introduce the computing environment in which we will work for the rest of the course.

Use the Jupyter notebook for basic Python code and explain the basics of using markdown and code cells in the Jupyter Notebook.

Explain what Numpy is, the problem it solves and why it is important for Python's Data Stack.

Introduce arrays, the main objects in Numpy, and how to create and use them.

Introduce with an example one of the common uses of Numpy: doing simulations.

Explain what pandas is and what we can do with it. An introduction to the main objects: Series and DataFrames.

Show how to use pandas Series and DataFrames with a real-world data set.

Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.

Show the viewer how to use pandas by doing real-world data analysis tasks and answering questions.

Explain to the viewer what matplotlib is and the main concepts needed for using it.

Explain what pyplot is, how to use the pyplot interface, and its limitations.

Explain how to use the Object-Oriented Interface and how it compares with the plyplot interface.

Show some of the common customizations that can be done to plots.

Explain what Exploratory Data Analysis (EDA) is and how to perform it in a real-world dataset; in the process, introduce the Seaborn plotting library.

Show how to analyze and make sense of individual variables depending on their type.

Show how to produce the main plots used to show relationships between variables.

Give a quick introduction to the Scipy package and all the different sub-packages it contains.

Show how to perform statistical calculations with the stats package like confidence intervals and probabilities of events.

Explain how to perform one of the most common statistical tests using the stats package.

Show how to perform a chi-square test using the stats package.

Present an overview of the section. Discuss the concepts of Predictive Analytics and its relationship with Machine Learning and give some characteristics of ML models.

Introduce the Scikit-Learn library and show the workflow traditionally used to build a Predictive Model with this library.