Live training with Boris Paskhaver starts on April 6th at 1 PM (ET)

Training duration: 4 hours (Hands-on)

Price with 30% discount

Regular Price: $210.00

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Instructor Bio:

Boris Paskhaver

Software Engineer | Stride Consulting

Boris Paskhaver

Boris Paskhaver is a full-stack web developer based in New York City with experience building apps in React / Redux and Ruby on Rails. His favorite part of programming is the never-ending sense that there's always something new to master --- a secret language feature, a popular design pattern, an emerging library or -- most importantly -- a different way of looking at a problem.

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By the end of the course, participants will be able to:

  • Apply concepts to a NEW dataset , and watch and practice the concepts.

  • Have a solid grasp of the capabilities of the pandas library.

  • Perform various data manipulations - sorting, joining, cleaning, aggregating, deduping, and more.


Course Abstract

This tutorial offers a comprehensive introduction to the powerful pandas library for data analysis built on top of the Python programming language. Pandas represents a great step forward for graphical spreadsheet users looking to grow their data manipulation skills. I like to call it "Excel on steroids". By completing this workshop, you'll have a strong foundation for using Pandas in your day-to-day data analysis needs. We'll start out with the basics -- importing datasets, selecting rows and columns, filtering rows by criteria -- and progress to advanced concepts like grouping values, joining multiple datasets together, and cleaning text. Students will be exposed to diverse data sets across different disciplines -- sports, finance, entertainment, and more. The training is open to all industries and is targeted for beginners --- basic Python knowledge is preferred but not required.

Course Outline

Lesson 1. Foundations of pandas: 

  • Discover the 1-dimensional Series and the 2-dimensional DataFrame, the two core data structures in pandas. 
  • Learn how to sort values across one or more columns, identify missing values, remove duplicates, count occurrences of values, filter rows based on one or more criteria, and more. 
  • At the end of this lesson, you'll have knowledge of the most popular features of pandas.

Lesson 2. Working with Data of Different Types

  • Data can come in a variety of formats (and be messy to boot)! In this lesson, we'll explore how to convert columns from one data type to another. 
  • We'll optimize our dataset to reduce memory consumption. 
  • We'll discover how to clean messy text data and how to extract date-time information from text. 
  • Finally, we'll have a chance to review all concepts from the previous lesson with new datasets.

Lesson 3. Working with Text Data

  • Real-world text data can be riddled with issues -- whitespace, letter casings, inconsistent formats, and more. 
  • In this lesson, we'll learn how to to clean text data in pandas. 
  • We'll apply text operations like splitting, replacing, and joining to whole columns of data. 
  • We'll conclude with a quick discussion on regular expressions, which allow us to define search patterns for text.

Lesson 4. Aggregating and Joining Datasets

  • In this section, we'll learn how to merge data across multiple datasets.
  • We'll explore the pandas equivalent of common SQL operations like inner joins, outer joins, left joins, and right joins. 
  • We'll also introduce the GroupBy object for grouping rows by shared values across one or more columns.
  • Finally, we'll walk through common aggregation operations like pivoting, melting, stacking, unstacking, and more.

Which knowledge and skills you should have?

  • Basic/intermediate experience with spreadsheet software (Excel, Google Sheets, etc.)

  • Basic experience with Python programming language

What is included in your ticket?

  • Access to live training and QA session with the Instructor

  • Access to the on-demand recording

  • Certificate of completion

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