The live training is scheduled for October, the exact date is to be determinated

Total training duration is 8 hours

Regular Training Price + Office Hours : $399.00

30% discount ends soon

Price with 30% discount




Instructor Bio:

President & Founder| Enplus Advisors, Inc.

Daniel Gerlanc

Daniel Gerlanc is a data scientist, software engineer, and technology instructor. After started his career as a hedge fund quant, he has spent the past decade bootstrapping data science and engineering teams for organizations of all sizes. He has co-authored several open-source R packages, published in peer-reviewed journals, and been an invited speaker at conferences including ODSC and PGConf. He is the author of the Programming with Data: Python and Pandas and teaches regularly on He has a B.A. from Williams College.

What will you learn?

  • Understand the characteristics of the core Pandas data types for univariate and multivariate data, the Series and DataFrame

  • Be able to load data from a variety of sources ranging from CSV to Parquet and the modern Arrow columnar format

  • Join data together using Pandas instead of a SQL engine or custom coding for non-equi joins

  • Transform data between “wide” and “long” formats and generating pivot tables

  • Understand how Pandas works conceptually so that you can troubleshoot your own code and avoid common pitfalls

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Course Abstract

In this training, you will learn how to accelerate your data analyses using the Python language and Pandas, a library specifically designed for tabular data analysis. We start by learning the core Pandas data structures, the Series and DataFrame. 

From these foundations, we will learn to use the split-apply-combine paradigm for grouped computations, manipulate time series, and perform advanced joins between datasets. Specifically, loading, filtering, grouping, and transforming data. Having completed this workshop, you will understand the fundamentals and advanced features of Pandas, be aware of common pitfalls, and be ready to perform your own analyses.

Course Schedule

As a hands-on training, each lesson begins with an instructor-led lecture followed by exercises that participants complete individually to reinforce their understanding of the material. Afterward, the instructor solves the exercises interactively, answers participant questions, and creates additional examples based on participant questions and areas of interest.

Our course of study begins with the fundamentals of Pandas and work up from there to more advanced topics

First Day:  3 lessons

  • Learning to work with univariate data in Pandas using the Series data structure. The lesson is going to cover how to create, index and filter Series will prepare us to perform these operations in multiple dimensions later.
  • The workhorse of Pandas, the DataFrame, in particular, how they're structured, how to select from, them, and how to filter from them.
  • How to apply grouped computations on DataFrames and Series using the split-apply-combine paradigms. 

Second Day: 3 lessons

  • How to import and export data from Pandas. The lesson will common file formats like CSV as well as relational databases and optimized binary formats like parquet.
  • How to work with time-series data and how to use the advanced functionality provided by Pandas.
  • Merging, joining, and concatenating Pandas data structures.

Daniel Gerlanc is looking forward to having you join him for Programming with Data: Python and Pandas, where you will both learn the underpinnings of tabular data analysis with a focus on Python and Pandas as your tool of choice.

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Who will be interested in this course?

  • This training is for people who know how to program, ideally with Python, and want to learn the most popular and feature-rich Python library for tabular data analysis. Based on course feedback, people familiar with other statistical languages like R, Stata, and MATLAB, find this a useful introduction to the similarities and differences of Python and Pandas.

Which knowledge and skills you should have?

  • This training requires an intermediate programming experience in Python. Generally, this means you should know the difference between the fundamental container types (list, dict, tuple), be familiar with control-flow (if/else/for/while), and how to define named and anonymous functions (lambdas).

What is included in your ticket?

  • Access to 2 live training sessions and a QA session with the Instructor

  • Access to the on-demand recordings

  • Certification of completion

  • Complimentary ODSC general pass - full price : $330