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Haskell: Data Analysis Made Easy

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Haskell: Data Analysis Made Easy, available at $64.99, has an average rating of 4.55, with 64 lectures, 11 quizzes, based on 70 reviews, and has 809 subscribers.

You will learn about Understand the basic concepts of data analysis Create Haskell functions for the common descriptive statistics functions Learn to apply regular expressions in large-scale datasets Plot data with the gnuplot tool and the EasyPlot library Reduce the size of data without affecting the data’s effectiveness using Principal Component Analysis Master the techniques necessary to perform multivariate regression using Haskell code This course is ideal for individuals who are If you are new to the field of data analysis and wish to polish your data analysis skills by using Haskell, this course is all that you need. It is particularly useful for If you are new to the field of data analysis and wish to polish your data analysis skills by using Haskell, this course is all that you need.

Enroll now: Haskell: Data Analysis Made Easy

Summary

Title: Haskell: Data Analysis Made Easy

Price: $64.99

Average Rating: 4.55

Number of Lectures: 64

Number of Quizzes: 11

Number of Published Lectures: 64

Number of Published Quizzes: 11

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 75

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Understand the basic concepts of data analysis
  • Create Haskell functions for the common descriptive statistics functions
  • Learn to apply regular expressions in large-scale datasets
  • Plot data with the gnuplot tool and the EasyPlot library
  • Reduce the size of data without affecting the data’s effectiveness using Principal Component Analysis
  • Master the techniques necessary to perform multivariate regression using Haskell code

Who Should Attend

  • If you are new to the field of data analysis and wish to polish your data analysis skills by using Haskell, this course is all that you need.

Target Audiences

  • If you are new to the field of data analysis and wish to polish your data analysis skills by using Haskell, this course is all that you need.

A staggering amount of data is created everyday; analyzing and organizing this enormous amount of data can be quite a complex task. Haskell is a powerful and well-designed functional programming language that is designed to work with complex data. It is trending in the field of data science as it provides a powerful platform for robust data science practices.

This course will introduce the basic concepts of Haskell and move on to discuss how Haskell can be used to solve the issues by using the real-world data.

The course will guide you through the installation procedure, after you have all the tools that you require in place, you will explore the basic concepts of Haskell including the functions, and the data structures.

It will also discuss the various formats of raw data and the procedures for cleaning the data and plotting them.

With a good hold on the basics of Haskell and data analysis, you will then be introduced to advanced concepts of data analysis such as Kernel Density Estimation, Hypothesis Testing, Regression Analysis, Text Analysis, Clustering, Naïve Bayes Classification, and Principal Component Analysis.

Why go for this course?

We’ve spent the last decade working to help developers stay relevant. The structure of this course is a result of deep and intensive research into what real-world developers need to know in order to be job-ready. We don’t spend too long on theory, and focus on practical results so that you can see for yourself how things work in action.

This course follows an example-based approach that will take you through learning Haskell initially, and then learning to manipulate data and visualizing it, and then gradually building your skill level where you can perform advanced algorithms on the data, such that you can make more sense of the data and interpret the future, or give suggestions. It’s a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of learning Haskell.

After completing this course, you will be equipped to analyze data and organize them using advanced algorithms.

This course is authored by some of the best in the field.

We have combined the following best Haskell products by Packt:

  • Learning Haskell Programming by Hakim Cassimally
  • Getting Started with Haskell Data Analysis by James Church
  • Learning Haskell Data Analysis by James Church
  • Advanced Data Analysis with Haskell by James Church

Meet your expert instructions:

James Church is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work.

HakimCassimallylearned the basics of Lisp 15 years ago and has been interested in functional programming ever since. He has written, spoken, and evangelised about learning and writing Haskell since 2006.

What are the requirements?

You do not need any programming knowledge, or knowledge in data science before you take up this course.

What am I going to get from this course?

Learn the basics of Haskell

Learn how to clean data

Learn how to plot data on a graph and to draw conclusions based on the graphs

Apply advanced algorithms on the data to extract more information from the data.

Course Curriculum

Chapter 1: How do I get Started with Haskell Data Analysis?

Lecture 1: Introduction

Lecture 2: Why Haskell

Lecture 3: Installing Haskell

Lecture 4: Installation Instructions for OS X

Lecture 5: Installation Instructions for Windows

Lecture 6: Installation Instructions for Linux

Lecture 7: What Else Would You Need?

Chapter 2: Getting Started with Haskell

Lecture 1: Discovering Haskell with ghci

Lecture 2: Built-in Data Structures

Lecture 3: Editing Haskell Source Code

Lecture 4: Introduction to Functions

Lecture 5: Building Your Own Data Structures

Lecture 6: Pattern Matching

Chapter 3: Working with CSV and SQLite3

Lecture 1: CSV Files

Lecture 2: Data Range

Lecture 3: Data Mean and Standard Deviation

Lecture 4: Data Median

Lecture 5: Data Mode

Lecture 6: Converting CSV files to the SQLite3 format

Chapter 4: Cleaning Our Datasets

Lecture 1: Structured Versus Unstructured Datasets

Lecture 2: Creating Your Own Structured Data

Lecture 3: Counting the Number of Fields in Each Record

Lecture 4: Regular Expressions – Dot and Pipe

Lecture 5: SQLite3 and Descriptive Statistics

Lecture 6: Character Classes

Lecture 7: Regular Expressions in CSV files

Lecture 8: SQLite3 and Regular Expressions

Chapter 5: Visualization

Lecture 1: Line Plots of a Single Variable

Lecture 2: Plotting a Moving Average

Lecture 3: Publication – Ready Plots

Lecture 4: Feature Scaling

Lecture 5: Scatter Plots

Chapter 6: Kernel Density Estimation

Lecture 1: What Is Normal Distribution?

Lecture 2: Kernel Density Estimation

Lecture 3: Application of the KDE

Chapter 7: Hypothesis Testing

Lecture 1: Data in a Coin

Lecture 2: Does a Home-Field Advantage Really Exist?

Chapter 8: Regression Analysis

Lecture 1: Linear Regression

Lecture 2: Correlation Coefficients

Lecture 3: Drawbacks of Linear Regression

Lecture 4: Logarithmic Regression

Lecture 5: Polynomial Regression

Chapter 9: Multiple Regression

Lecture 1: Building Matrices

Lecture 2: Performing Multivariate Regression

Lecture 3: Calculating the Adjusted R^2

Lecture 4: Improving the Adjusted R^2 Score

Chapter 10: Text Analysis

Lecture 1: Preparing Our Text

Lecture 2: Finding the Set of N-Grams

Lecture 3: Cosine Similarity

Lecture 4: Overview of TF-IDF

Lecture 5: Applying TF-IDF

Chapter 11: Clustering

Lecture 1: Clustering: An Overview

Lecture 2: Random Cluster Generation

Lecture 3: Distances between Clusters

Lecture 4: Performing K-Means Clustering

Lecture 5: Performing Hierarchical Clustering

Chapter 12: Naïve Bayes Classification

Lecture 1: Bayes: A Discussion

Lecture 2: Bayes: The Code

Lecture 3: Bayes on Full Documents

Chapter 13: Principal Component Analysis

Lecture 1: PCA: A Discussion

Lecture 2: Preparing Our Dataset

Lecture 3: Eigendecomposition

Lecture 4: Dimensionality Reduction

Chapter 14: Recommendation Engine

Lecture 1: Building a Recommendation Engine

Instructors

Rating Distribution

  • 1 stars: 4 votes
  • 2 stars: 8 votes
  • 3 stars: 12 votes
  • 4 stars: 24 votes
  • 5 stars: 22 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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