Getting Started with Haskell Data Analysis

Описание: Perform meaningful analysis on real-world data in the Haskell language while utilizing the IHaskell environment for Jupyter notebooks.
Create publication-ready visualizations of data.
Understand the mathematics behind simple data analysis procedures.
We use a gentle introduction to the mathematics behind data analysis.

Learning
Learn to parse a CSV file and read data into the Haskell environment.
Create Haskell functions for the common descriptive statistics functions that you already know about: range, mean, median, mode, and standard deviation.
Learn to create a SQLite3 database using an existing CSV file.
Learn the versatility of the SELECT query for slicing data into smaller chunks.
Learn to craft regular expressions through simple examples.
Learn to apply regular expressions in large-scale datasets using both CSV files and SQLite3 files.
Understand the formula for normal distribution and how the parameters affect the shape of the distribution.
Learn to create a kernel density estimator visualization, which is an application of normal distribution.

About
Data analysis is part computer science and part statistics. An important part of data analysis is validating your assumptions with real-world data to see if there is a pattern, or a particular user behavior that you can validate. This video 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 onto more advanced concepts like understanding the importance of normal distribution. Whilst 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.

Style and Approach:
The style of this course is driven by problem solving using real-world data. In some sections, we will begin by seeking out datasets that are readily accessible on the Internet, downloading them, and then performing some analysis. Each video builds a little on the video before it at a conversational pace. We use the Jupyter notebook system, which allows us to easily create and share notebooks of our analysis work. You can download the notebooks that we create alongside each of our videos.


Descriptive Statistics
The Course Overview
CSV Files
Data Range
Data Mean and Standard Deviation
Data Median
Data Mode
SQLite3
SQLite3 Command Line
Data Range
Slices of Data
SQLite3 and Descriptive Statistics
Regular Expressions
Regular Expressions – Dot and Pipe
SQLite3 and Descriptive Statistics
Character Classes
Regular Expressions in CSV files
SQLite3 and Regular Expressions
Visualizations
Line Plots of a Single Variable
Plotting a Moving Average
Publication – Ready Plots
Feature Scaling
Scatter Plots
Kernel Density Estimation
What Is Normal Distribution?
Kernel Density Estimation
Application of the KDE
Course Review
CSV Variations to SQLite3
SQLite3 SELECT and Descriptive Stats
Visualizations
KDE