Python for Data Analysis

Python for Data Analysis

40 Hour Course

Online - Live Virtual

Course Details

Course Details

This 40-hour class combines our Python Foundations with our project-based Pandas Mastery Workshop. The curriculum provides an excellent introduction to the Python language and its capabilities for all things data, while also providing intensive exposure to the core workhorse tools of NumPy and Pandas that are central to data analysis in Python.

This class is perfect for people who want to start using Python and Pandas regularly in their day-to-day work and need to achieve a high level of proficiency rapidly. With a hands-on, exercise-intensive design and individualized instructor coaching, students will leave this class with the capability to immediately transfer their learnings to their day-to-day work.

Pandas (the Python Data Analysis library) provides a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering. Quantitative analysts, data scientists, and business analysts will find this class particularly beneficial.

This course is instructor-led. Consult the class schedule below for times and locations. Course registration will close at 12pm CT the Thursday before a Monday start date.

Course Overview

Days 1–2: Python and NumPy

  • It begins with a one-day introduction to the Python language focusing on standard data structures, control constructs, and code organization.
  • After a brief overview of the Scientific Python ecosystem, we dive into techniques for numeric data processing, including efficiently manipulating and processing large data sets using NumPy arrays and data visualization with 2D plots using Matplotlib.

Days 3–5: Pandas Mastery Workshop materials

The class progresses step-by-step through a repeatable data analysis workflow using the Python Pandas library, including reading in data from multiple sources and databases, cleaning, merging, and munging data to prepare it for analysis, and data exploration and visualization.

Topics covered include

  • Accessing Data From Multiple Sources
  • Cleaning and Preparing Data
  • Database Access and Data Wrangling
  • Data Visualization
  • Data Analysis
  • Real-World Modeling and Problem Solving

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