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Haskell Data Analysis

This course is geared for Python experienced developers, analysts or others who wants to get Haskell skills to work and generate publication-ready visualizations in no time at all.

Every business and organization that collects data is capable of tapping into its own data to gain insights on how to improve. Haskell is a purely functional programming language, well-suited to handling large data analysis problems. This course will take you through the more difficult problems of data analysis in a hands-on manner. This 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 progress to more advanced concepts such as understanding the importance of normal distribution. While 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. By the end of this course, you will have a thorough understanding of data analysis, and the different ways of analyzing data. You will have a mastery of all the tools and techniques in Haskell for effective data analysis. 

Available formats for this course
In-Person
Live Online
Private Team Training
Duration
2 days/16 hours of instruction
Public Classroom Pricing

Part 1: Descriptive Statistics 

  • Descriptive Statistics 
  • The CSV library – working with CSV files 
  • Data range 
  • Data mean and standard deviation 
  • Data median 
  • Data mode 

Part 2: SQLite3 

  • SQLite3 
  • SQLite3 command line 
  • Working with SQLite3 and Haskell 
  • Slices of data 
  • Working with SQLite3 and descriptive statistics 

Part 3: Regular Expressions 

  • Regular Expressions 
  • Dots and pipes 
  • Atom and Atom modifiers 
  • Character classes 
  • Regular expressions in CSV files 
  • SQLite3 and regular expressions 

Part 4: Visualizations 

  • Visualizations 
  • Line plots of a single variable 
  • Plotting a moving average 
  • Creating publication-ready plots 
  • Feature scaling 
  • Scatter plots 

Part 5: Kernel Density Estimation 

  • Kernel Density Estimation 
  • The central limit theorem 
  • Normal distribution 
  • Introducing kernel density estimation 
  • Application of the KDE 

Part 6: Course Review 

  • Course Review 
  • Converting CSV variation files into SQLite3 
  • Using SQLite3 SELECT and the DescriptiveStats module for descriptive statistics 
  • Creating compelling visualizations using EasyPlot 
  • Reintroducing kernel density estimation 

Professionals who would benefit from this training include:

  • Developers
  • Analysts
  • IT Managers
  • Product Managers

A working knowledge of Python is required to attend this course.

  • Learn to parse a CSV file and read data into the Haskell environment 
  • Create Haskell functions for common descriptive statistics functions 
  • Create an SQLite3 database using an existing CSV file 
  • Learn the versatility of SELECT queries for slicing data into smaller chunks 
  • Apply regular expressions in large-scale datasets using both CSV and SQLite3 files 
  • Create a Kernel Density Estimator visualization using normal distribution 

Haskell Data Analysis Schedule

Location
Date
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There are currently no scheduled classes for this course. Please contact us if you would like more information or to schedule this course for you or your company.

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