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+# Introduction to data analysis with Python
+
+This material covers a short course on using Python for data analysis.
+
+The material assumes that the student is aware of basic mathematics and
+statistics. While doing statistical analysis it always helps to know
+statistics fairly well. We will attempt to provide some links to freely
+available material that covers some of these basics.
+
+An excellent book on doing statistical analysis with Python is Allen Downey's
+Think Stats book which is freely available. The material is not a traditional
+approach to statistics but will get you thinking for sure.
+
+The emphasis of this course is to expose the student to the various libraries
+and tools available in Python so they can embark on their own data analysis.
+There is a lot of material already available. We will attempt to provide the
+attendees links to some useful material.
+
+## Pre-requisites
+
+- Students should have completed the basic Python programming material.
+- One should have a Python 3.x installation with the following packages:
+    - IPython, scipy, matplotlib
+    - pandas, statsmodels
+- Use a reasonable editor, Canopy will work.
+- If one desires a more advanced editor, I suggest VS Code
+  (https://code.visualstudio.com/) which is free, open source, and very
+  powerful.
+- Knowledge of basic statistics.
+
+## Contents
+
+* Introduction
+
+* Simple statistics with `numpy`
+    * Basic stats functions, mean, std etc.
+    * Percentiles
+    * Random numbers: normal, random, choice, shuffle
+
+* Statistical plots
+    * hist
+    * boxplot
+    * scatter
+    * pie chart
+
+* Using `scipy.stats`
+    * pdf
+    * cdf
+    * rvs
+
+* Using `pandas`
+    * Quick introduction
+    * Categorical vs numerical data
+    * Data frames
+    * Basic operations
+    * String operations
+    * simple plots
+    * Groupby
+    * Pivot
+    * Maps
+    * pdvega
+
+* Using `statsmodel`
+    * regression
+    * anova
+
+* Seaborn?