Haskell: Data Analysis Made Easy
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
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Packt Publishing
Tech Knowledge in Motion
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
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You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
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