Learn how to analyze large amounts of data using Haskell. This course shows how to use Haskell for data science, including how to query, clean up, manipulate, and analyze data.
(upbeat music) - [Narrator] Hi, my name is James Church and this is beginning Haskell data analysis. In this course, we are going to learn about data analysis from the perspective of the Haskell programming language. We are going to use the iHaskell environment on the Juniper notebook system.
Now a little bit about myself. For the past several years, I have been a professor of higher education, and I have been a consultant for several companies, as well as the United States National Institute on Drug Abuse. Finally, I am the author of Learning Haskell Data Analysis, also by Packt Publishers. In section one of this course, we are going to cover the CSV file format as well as descriptive statistics. The descriptive statistics we're going to cover are going to be the ones that you're most familiar with, the mean and the mode and the standard deviation.
In section two of this course we're going to cover SQLite3 database, which is a popular database engine for working with flat files. In the third section of this course we're going to be working with regular expressions. Regular expressions are a textual pattern that allows us to filter data and it allows us to make sure that columns in our data match a particular standard. In section four of this course, we're going to create compelling visualizations and make publication-ready charts and graphs.
In section five of this course we're going to learn about the normal distribution. And using that normal distribution, we're going to create a visualization known as the Kernel Density Estimator. In section six of this course we're going to wrap it all up with a start to finish example using the movie lens data set, where we tie together the first five sections. The goal for this course is to take you from a beginner in math and statistics and get you comfortable working with large scale data sets.
Now the pre-requisites for this course are that you know a little bit of the Haskell programming language, you also know a little bit of math and statistics. From there, we can start you on the journey of becoming a data analyst. Well, here we go.
Note: This course was created by Packt Publishing. We are pleased to host this training in our library.
- 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)