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Course Skill Level:


Course Duration:

2 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Those with basic developing and Python experience

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or other attendees with Python skills who want to get Haskell skills to work and generate publication-ready visualizations in no time at all.
  • Skill-level: Foundation-level Haskell Data Analysis skills for Intermediate skilled team members. This is not a basic class.
  • Developing: Basic (1-2 years’ experience)
  • Python: Basic (1-2 years’ experience)

About this course

Every business and organization that collects data is capable of tapping into its own data to gain insights how to improve. Haskell is a purely functional and lazy 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Haskell Data Analysis expert instructor, students will learn about and explore:
  • Taking your data analysis skills to the next level using the power of Haskell
  • Understanding regression analysis, perform multivariate regression, and untangle different cluster varieties
  • Creating publication-ready visualizations of data
  • Learning to parse a CSV file and read data into the Haskell environment
  • Creating Haskell functions for common descriptive statistics functions
  • Creating an SQLite3 database using an existing CSV file
  • Learning the versatility of SELECT queries for slicing data into smaller chunks
  • Applying regular expressions in large-scale datasets using both CSV and SQLite3 files
  • Creating a Kernel Density Estimator visualization using normal distribution

Course breakdown / modules

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

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

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

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

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

  • 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