Pandas and Plots for Data Analysis

Introducing the Anaconda Python distribution and JupyterLab IDE

Data types

Loops and list comprehensions

Loading and using packages

Introduction to the pandas package

Importing data from CSV, Excel and SQL databases

Data types in pandas (numerical, categorical, binary, boolean)

Creating numerical summaries

Exploring data grouped by a set of variables

Exploratory statistical graphics using the seaborn package

Estimating basic statistics like mean, median, standard deviation and quantiles

Basic probability distributions (normal/Gaussian, binomial, Poisson, exponential, Chi-squared) including generating random numbers and finding critical values.

How pandas creates dummy variables from categorical variables

Linear & logistic regression and the formula interface

Creating publication-quality graphics Best practices for data analyses

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