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Haskell Data Analysis

  • Course Code: Data Analysis / BI - Haskell Data Analysis
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 2 Days Audience: 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.

Course Snapshot 

  • Duration: 2 days 
  • Skill-level: Foundation-level Haskell Data Analysis skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: 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. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation, or remote instructor led delivery, or CBT/WBT (by request). 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

Working in a hands-on learning environment, led by our Haskell Data Analysis expert instructor, students will learn about and explore: 

  • Take your data analysis skills to the next level using the power of Haskell 
  • Understand regression analysis, perform multivariate regression, and untangle different cluster varieties 
  • Create publication-ready visualizations of data 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • 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 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to get Haskell skills to work and generate publication-ready visualizations in no time at all. 

Pre-Requisites:  Students should have  

  • developers with some knowledge of Python.  

Course Agenda / Topics 

  1. Descriptive Statistics 
  • Descriptive Statistics 
  • The CSV library – working with CSV files 
  • Data range 
  • Data mean and standard deviation 
  • Data median 
  • Data mode 
  1. SQLite3 
  • SQLite3 
  • SQLite3 command line 
  • Working with SQLite3 and Haskell 
  • Slices of data 
  • Working with SQLite3 and descriptive statistics 
  1. Regular Expressions 
  • Regular Expressions 
  • Dots and pipes 
  • Atom and Atom modifiers 
  • Character classes 
  • Regular expressions in CSV files 
  • SQLite3 and regular expressions 
  1. Visualizations 
  • Visualizations 
  • Line plots of a single variable 
  • Plotting a moving average 
  • Creating publication-ready plots 
  • Feature scaling 
  • Scatter plots 
  1. Kernel Density Estimation 
  • Kernel Density Estimation 
  • The central limit theorem 
  • Normal distribution 
  • Introducing kernel density estimation 
  • Application of the KDE 
  1. 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 
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