Getting Started with Haskell Data Analysis

Features Includes:
  • Self-paced with Life Time Access
  • Certificate on Completion
  • Access on Android and iOS App

Course Preview Video


Put your Haskell skills to work and generate publication-ready visualizations in no time at all.

Data analysis is part computer science and part statistics. An important part of data analysis is validating your assumptions with real-world data to see if there is a pattern, or a particular user behavior that you can validate. This video course will help you get up to speed with the basics of data analysis and approaches in the Haskell language. You'll learn about statistical computing, file formats (CSV and SQLite3), descriptive statistics, charts, and onto more advanced concepts like understanding the importance of normal distribution. Whilst mathematics is a big part of data analysis, we’ve tried to keep this course simple and approachable so that you can apply what you learn to the real world.

About the Author

  • James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.

Basic knowledge
  • It’s not a programming course, and a basic understanding of the Haskell language is expected

What will you learn
  • Learn to parse a CSV file and read data into the Haskell environment
  • Create Haskell functions for the common descriptive statistics functions that you already know about: range, mean, median, mode, and standard deviation
  • Learn to create a SQLite3 database using an existing CSV file
  • Learn the versatility of the SELECT query for slicing data into smaller chunks
  • Learn to craft regular expressions through simple examples
  • Learn to apply regular expressions in large-scale datasets using both CSV files and SQLite3 files
  • Understand the formula for normal distribution and how the parameters affect the shape of the distribution
  • Learn to create a kernel density estimator visualization, which is an application of normal distribution
Course Curriculum
Number of Lectures: 27 Total Duration: 03:18:32

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