by James Church | Released July 2016 | ISBN: 9781785880841

Put your Haskell skills to work and generate publication-ready visualizations in no time at all About This VideoPerform 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.In DetailData 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. Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Getting-Started-with-Haskell-Data-Analysis-Video-. If you require support please email: [email protected] Show and hide more

1. Chapter 1 : Descriptive Statistics
   • The Course Overview 00:02:38
   • CSV Files 00:11:33
   • Data Range 00:05:03
   • Data Mean and Standard Deviation 00:06:52
   • Data Median 00:05:44
   • Data Mode 00:06:02
2. Chapter 2 : SQLite3
   • SQLite3 Command Line 00:09:26
   • Data Range 00:06:39
   • Slices of Data 00:06:57
   • SQLite3 and Descriptive Statistics 00:09:36
3. Chapter 3 : Regular Expressions
   • Regular Expressions – Dot and Pipe 00:06:51
   • SQLite3 and Descriptive Statistics 00:07:30
   • Character Classes 00:07:40
   • Regular Expressions in CSV files 00:06:15
   • SQLite3 and Regular Expressions 00:06:40
4. Chapter 4 : Visualizations
   • Line Plots of a Single Variable 00:08:37
   • Plotting a Moving Average 00:06:54
   • Publication – Ready Plots 00:07:23
   • Feature Scaling 00:05:39
   • Scatter Plots 00:05:50
5. Chapter 5 : Kernel Density Estimation
   • What Is Normal Distribution? 00:10:26
   • Kernel Density Estimation 00:06:42
   • Application of the KDE 00:09:22
6. Chapter 6 : Course Review
   • CSV Variations to SQLite3 00:08:40
   • SQLite3 SELECT and Descriptive Stats 00:06:19
   • Visualizations 00:05:25
   • KDE 00:11:51
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