Book Image

Getting Started with Haskell Data Analysis [Video]

By : James Church
Book Image

Getting Started with Haskell Data Analysis [Video]

By: James Church

Overview of this book

<p>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.</p> <h1>Style and Approach:</h1> <p>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.</p>
Table of Contents (6 chapters)
Chapter 1
Descriptive Statistics
Content Locked
Section 4
Data Mean and Standard Deviation
We have a collection of data. We would like to quickly identify the mean and standard deviation of the data. - Define what is meant by "mean" and "standard deviation." The mean is the sum of all values divided by the number of values. The standard deviation is the mean of the distance of each value from the original mean - We find the sum values in a dataset using "sum". We find the number of values in a dataset using "length". We write a quick function to return this value, as well as the standard deviation - We drop that function into a module file and show how it can be called from IHaskell.