Top 5 Data Science Material for Data Science Beginners That Can be Downloaded Free

The popularity of data science, big data jobs in India and globally has leapfrogged over the past five years or so. Its successful run in the industry can be attributed to better research, project implementations and the general growth in big data and data science. These developments have called out for techies trying to make a career in data science.

While paid courses, full-fledged graduate programmes, or even online courses like Coursera, EdX, Udemy or Udacity, among others, are excellent resources for learning, these can be expensive for many. Even if most of the online courses mentioned above might be free, you might need to enroll for these courses beforehand or even require a membership.

For all of those who are looking for other alternatives, we bring to you few resources which are free to use in your own free time. This article lists five best courses and reference materials available on the internet, which are not only free but are also are downloadable without any strain on your pocket.

The Open Source Data Science Masters by Clare Corthell

Rather than being a straightforward course, this site presents a comprehensive collection of useful data science resources. The reason it is listed here is that most of the links present in the site cover a large array of topics ranging from data science basics, mathematics, statistics, machine learning, programming, and data visualization.

This repository of resources also tells why a solid foundation of data science through open-source tools is essential to bridge the talent gap in the industry.

You can find the link here.

CS109 Data Science by Harvard University

Harvard's CS109 Data Science is an exhaustive resource for preliminary data science. Mainly aimed at computer science students, it proposes the science of data in the form of five key facets:

Data Transformation
Data Management
Exploratory Data Analysis
Prediction methods
Data Visualisation

Python is the key language used for implementation. Since this is a course material for undergraduates, most of the content is presented in the form of lecture videos. With an emphasis on gaining insights from data, this course follows a top-down approach to understanding critical concepts in data science.

You can download the course details here.

Introduction to Computational Thinking and Data Science by MIT OpenCourseWare

An introductory course by Massachusetts Institute of Technology (MIT), this content material contains all the finer distinctions in data science for beginners. Being an actual course for computer science undergraduates, it covers concepts from statistics and machine learning from scratch. It has a strong emphasis on Python programming - the go-to language for data science implementations. On the other hand, optimization and statistical concepts are also covered to focus on computational thinking for solving problems.

You can download the course details here.

A Course in Machine Learning By Hal Daume III

A stand-alone resource for machine learning, this introductory course by Professor Hal Daume III of the University of Maryland covers major topics in ML such as supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory and so on, in detail.

The approach taken by Daume in presenting the learning material follows on ideas rather than relying extensively on math. Backed by examples, this course material is also pedagogically organized for better understanding.

You can download the course detail here.

Learning From Data by California Institute of Technology

This machine learning online course by CalTech has a comprehensive take on the subject. With the content having a stern focus on theory as well as practice, it follows a storyline approach. ML has emerged as the top favorite among data science enthusiasts and this course will definitely help them get through the fundamental concepts underlying in ML.

Tutored by Professor Yaser Abu-Mostafa, the lectures are in the form of videos broken into 18 sections. You can find the complete list of videos here.

Source: HOB

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