Getting Started with Python for Data Analysis

Boston (April 14-15), Washington D.C. (June 9-10), New York City (Nov 17-18)

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Workshop Overview

Our two day workshop is intended for programming beginners who want to upgrade their existing toolset and learn Python to extract insights from data. Easy to learn, powerful, and flexible; Python is quickly becoming one of the most popular languages used for data analytics.

We will cover the foundations of programming in Python and learn how to use three industry-grade, open source libraries for data analysis and visualization:

Jupyter notebook as a programming environment and exploration laboratory
Pandas for data importing, cleaning, and merging
Plotly for interactive and flexible visualization

Programming novices welcome. Experience not required.

Chase Coleman & Spencer Lyon


This course is taught by Chase Coleman and Spencer Lyon, PhD students at NYU’s Stern School of Business and co-founders of Valorum Data, a data consulting and training company.

Day 1 – Python foundations

In this section we start with installation and work our way through core Python concepts and skills.

Outline of topics:

  • Introduction and installation
  • First steps
  • Core data-types:
    • Strings
    • Numbers
    • Collections (lists and tuples)
    • Dictionaries
  • Conditional statements
  • Iteration
  • Functions

Day 2 – Data analysis and visualization

This section of the workshop covers data ingestion, cleaning, manipulation, analysis, and visualization in Python.

We build on the skills learned in the Python foundations section and learn how to use the pandas library for handling data and introduce the plotly library for data visualization

Outline of topics:

  • Introduction to pandas
  • Basic functionality
  • The index: labeling rows and columns of the data
  • Data cleaning and importing messy data
  • Reshaping data
  • Merging datasets
  • Performing analysis on groups
  • Time series analysis
  • Intermediate plotting and visualization