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Learning Path: Haskell for Data Analysis

Video Description

Learn to perform efficient data analysis using Haskell

In Detail

Haskell, an advanced functional programming language, is well designed to work with complex data and handle large data analysis problems. If your work concerns statistics and data analysis and you wish to expand your knowledge using Haskell, this Learning Path is for you. This course will begin with the fundamentals and building blocks of Haskell programming language with special emphasis on functional programming. Then, you’ll move on to learn statistical computing, descriptive statistics, charts, and onto more advanced concepts like understanding the importance of normal distribution. By the end of this Learning Path, you’ll have an understanding of data analysis, different ways to analyze data, and the various clustering algorithms available.

Prerequisites: No prior knowledge of Haskell is required

Resources: Code downloads and errata:

  • Learning Haskell Programming

  • Getting Started with Haskell Data Analysis

  • Advanced Data Analysis with Haskell

  • PATH PRODUCTS

    This path navigates across the following products (in sequential order):

  • Learning Haskell Programming (4h 10m)

  • Getting Started with Haskell Data Analysis (3h 18m)

  • Advanced Data Analysis with Haskell (4h 4m)

  • Table of Contents

    1. Chapter 1 : Learning Haskell Programming
      1. The Course Overview 00:02:59
      2. Installing Haskell 00:03:40
      3. Installation Instructions for OS X 00:04:45
      4. Installation Instructions for Windows 00:03:11
      5. Installation Instructions for Linux 00:05:33
      6. Discovering Haskell with ghci 00:13:03
      7. Built-in Data Structures 00:10:32
      8. Editing Haskell Source Code 00:06:25
      9. Introduction to Functions 00:10:07
      10. Building Your Own Data Structures 00:09:23
      11. Pattern Matching 00:09:28
      12. Creating a Project with Stack 00:11:51
      13. Setting up the Word Game Grid 00:08:26
      14. Searching for Words 00:19:39
      15. Searching in All Directions 00:14:02
      16. Unit Testing the Grid with Hspec 00:07:33
      17. Grid Coordinates and Infinite Lists 00:20:44
      18. Fleshing Out the Grid Model 00:23:38
      19. Searching the Grid Recursively 00:22:21
      20. Making the Game Playable 00:30:20
      21. Some Final Polish 00:12:24
    2. Chapter 2 : Getting Started with Haskell Data Analysis
      1. The Course Overview 00:02:38
      2. CSV Files 00:11:33
      3. Data Range 00:05:03
      4. Data Mean and Standard Deviation 00:06:52
      5. Data Median 00:05:44
      6. Data Mode 00:06:02
      7. SQLite3 Command Line 00:09:26
      8. Data Range 00:06:39
      9. Slices of Data 00:06:57
      10. SQLite3 and Descriptive Statistics 00:09:36
      11. Regular Expressions – Dot and Pipe 00:06:51
      12. SQLite3 and Descriptive Statistics 00:07:30
      13. Character Classes 00:07:40
      14. Regular Expressions in CSV files 00:06:15
      15. SQLite3 and Regular Expressions 00:06:40
      16. Line Plots of a Single Variable 00:08:37
      17. Plotting a Moving Average 00:06:54
      18. Publication – Ready Plots 00:07:23
      19. Feature Scaling 00:05:39
      20. Scatter Plots 00:05:50
      21. What Is Normal Distribution? 00:10:26
      22. Kernel Density Estimation 00:06:42
      23. Application of the KDE 00:09:22
      24. CSV Variations to SQLite3 00:08:40
      25. SQLite3 SELECT and Descriptive Stats 00:06:19
      26. Visualizations 00:05:25
      27. KDE 00:11:51
    3. Chapter 3 : Advanced Data Analysis with Haskell
      1. The Course Overview 00:02:37
      2. CSV Files to SQLite3 00:05:59
      3. Regular Expressions 00:10:08
      4. Visualizations 00:06:00
      5. Kernel Density Estimation 00:07:38
      6. Linear Regression 00:10:32
      7. Correlation Coefficients 00:06:05
      8. Drawbacks of Linear Regression 00:09:12
      9. Logarithmic Regression 00:07:04
      10. Polynomial Regression 00:17:35
      11. Creating Matrices in HMatrix 00:11:35
      12. Performing Multivariate Regression 00:08:57
      13. Calculating the Adjusted R^2 00:09:47
      14. Improving the Adjusted R^2 Score 00:09:59
      15. Preparing Our Text 00:07:26
      16. Finding the Set of N-Grams 00:05:51
      17. Cosine Similarity 00:07:06
      18. Overview of TF-IDF 00:04:22
      19. Applying TF-IDF 00:05:56
      20. Clustering: An Overview 00:05:49
      21. Random Cluster Generation 00:07:33
      22. Distances between Clusters 00:07:28
      23. Performing K-Means Clustering 00:05:56
      24. Performing Hierarchical Clustering 00:07:39
      25. Bayes: A Discussion 00:08:50
      26. Bayes: The Code 00:07:16
      27. Bayes on Full Documents 00:08:00
      28. PCA: A Discussion 00:07:06
      29. Preparing Our Dataset 00:08:37
      30. Eigendecomposition 00:04:28
      31. Dimensionality Reduction 00:03:41
      32. Recommendation Engine 00:08:22