Learning Haskell for Data Analysis

Learning Haskell for Data Analysis

Learning Haskell for Data Analysis is an Intermediate level course on Developer, compiled by the author, James Church. This course is an important foundation for a person who needs to sharpen his/her Developer talent. It provides you a thorough grip on Developer, Programming Languages, Haskell, Developer, Programming Languages and Haskell.

Free Download - Learning Haskell for Data Analysis
Author James Church
Publish Date 7/7/2017
Skill Intermediate
Duration 3h 18m
Topic Developer
Video Tutorials 27
Last Update N/A

Data is frequently available in raw form and in massive quantities, so knowing how to find the data you need and analyze it is crucial to being a successful data analyst. This course shows how to use Haskell for your data science needs. Instructor James Church first explains descriptive statistics, so you can discover the importance of ranges, means, medians, and standard deviations. Then, James gets you started with using SQLite3 so you can query data. Next, he takes you through working with regular expressions and visualizations. Finally, he discusses the topic of distribution by covering Kernel Density Estimation.

Note: This course was created by Packt Publishing. We are pleased to host this training in our library.

Topics include:

  • Data ranges, means, and medians
  • Standard deviation
  • SQLite3 command line
  • Slices of data
  • Regular expressions
  • Atoms and modifiers
  • Character classes
  • Line plots of a single variable
  • Plotting a moving average
  • Feature scaling
  • Scatter plots
  • Normal distribution
  • Kernel density estimation (KDE)
  • Visualizations

Learning Haskell for Data Analysis : Video Lessons

  • Course overview2m 37s
  • CSV files11m 32s
  • Data range5m 2s
  • Data mean and standard deviation6m 51s
  • Data median5m 43s
  • Data mode6m 2s
  • SQLite3 command line9m 25s
  • Getting data from SQLite3 into Haskell6m 38s
  • Slices of data6m 56s
  • SQLite3 and descriptive statistics9m 36s
  • Regular expressions: Dot and pipe6m 51s
  • Atoms and modifiers7m 29s
  • Character classes7m 40s
  • Regular expressions in CSV files6m 15s
  • SQLite3 and regular expressions6m 40s
  • Line plots of a single variable8m 29s
  • Plotting a moving average6m 45s
  • Publication-ready plots7m 22s
  • Feature scaling5m 38s
  • Scatter plots5m 49s
  • What is normal distribution?10m 26s
  • Kernel density estimation (KDE)6m 41s
  • Application of the KDE9m 22s
  • CSV variations to SQLite38m 39s
  • SQLite3 SELECT and descriptive stats6m 18s
  • Visualizations5m 24s
  • KDE11m 51s

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