patricklss/python-and-spark-for-data-analysis

A four-day course on Python, the Scientific Python stack and PySpark, adapted from a training course given by Patrick Varilly to one of our clients in December 2015

Python and Spark for Data Analysis

These are the IPython notebooks I used for a 4-day training course on Python and Spark for data science, given in December 2015 to a Data Minded client. The audience consisted of experienced data analysts, familiar with technologies like R and SPSS, but who had never used Python and had never worked on a Hadoop cluster.

The content is mildly redacted to remove all references to the actual client, but are otherwise unchanged.

Each day consisted of working through a series of IPython notebooks. Exercises are interspersed throughout. The last notebook of each day contains solutions to that day's exercises.

Objectives

The objectives of the training were to:

• Learn the fundamentals of Python
• Learn the fundamentals of its statistical and machine learning packages
• Learn Apache Spark using Python
• Learn how to apply these technologies in a live Hadoop cluster

Pre-requisites

Before the start of the course, we required the following software to be installed on students' laptops:

• Anaconda 2.4.1 64-bit for Windows. The packages in this version of Anaconda included:
  • Python 2.7.11
  • IPython 4.0.1
  • NumPy 1.9.3
  • SciPy 0.16.0
  • Matplotlib 1.5.0
  • Pandas 0.17.1
  • Seaborn 0.6.0
  • Scikit-learn 0.17
• Apache Spark 1.2.0. The version was chosen to match that in the client's production cluster, even though the latest version at the time of the course was 1.5.2
• JDK 7u79.

Syllabus

The four days covered the following content.

Day 0: Fundamentals of Python

This day was intended for people with very limited programming experience and/or no Python experience. Day 0 was optional.

At the end of this day, the students were able to:

• Start and run python programs interactively with python CLI
• Use an IDE to write programs and execute them, including command line arguments
• Create notebooks locally and on a server
• Import libraries
• Store data in variables and understand their reach
• Know the standard operators
• Control the flow of a program
• Perform common string operations such as concatenation, substring, replace
• Use the correct data structures
• Use functions to structure your program

Day 1: Statistical and Machine Learning Packages

On Day 1, we discussed several of the powerful statistical and machine learning libraries in Python. It was purposely a very hands on introduction and we did not dive into the mathematics behind any of the algorithms.

At the end of this day, the students were able to:

• Import and export data in csv
• Use numpy/scipy to perform mathematical computations
• Slice and dice data
• Use pandas to wrangle data
• Plot data and perform exploratory analysis
• Use scikit-learn
• Perform regression analysis in Python
• Perform classification analysis in Python

Day 2: Apache Spark and Python

On the second day, we dove into Spark. We focused on the essential parts. After a brief introduction into Spark Core, we explored Spark SQL and Spark MLlib.

At the end of this day, the students were able to:

• Understand the role of Spark and pyspark in the eco-system
• Run spark locally from a shell
• Run spark locally in IPython Notebooks
• Do a word count on an input file
• Load data in SparkSQL
• Query data in SparkSQL
• Use Spark MLlib to perform regression and classification analyses at scale

Day 3: Python and Apache Spark on a Cluster

In this last day, we set up a small Cloudera Hadoop cluster on AWS and explored how everything we had learned could be run in a cluster environment. The second half of the day was set aside for an open-ended project. Possible projects included:

1. setting up a machine learning pipeline on data from the UCI Machine Learning Repository;
2. implementing a machine learning algorithm using Spark Core;
3. testing to what extent Spark running times scales linearly with data size.

At the end of this day, the students were able to

• Run python scripts on the cluster from a shell and from ipython notebooks
• Use Spark to read from and write to HDFS
• Use SparkSQL to read data from and write data to Hive
• Understand how YARN works
• Submit spark jobs on the cluster
• Use Spark, SparkSQL and Spark MLlib to run algorithms on large-scale data.