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Learn Data Science

Overview

This 27-part course consists of tutorials, quizzes, hands-on assignments and real-world projects to learn data science. You can think of this list as a "Free Online Nano Book".

What is Data Science? Why Data Science?

Trillions of gigabytes of data is being produced yearly, and the number is still growing exponentially. It is estimated that for every person, 1.7 megabytes of data will be produced every second by 2020.

Our society is increasingly becoming data dependent. Data is only a raw material and extracting information from it requires further work. Data Science helps us make sense of data.

Growth of Data. Source: Patrick Cheesman

Primary Objectives of the Course

Understand what data science is and it's key components - programming, data, statistics and probability, machine learning, big data technologies.
Understand the probability and statistics concepts for data science.
Understand how to perform hypothesis testing.
Understand techniques for performing analysis on networks (graphs), and implement the famous PageRank algorithm.
Understand what machine learning is and learn some popular machine learning algorithms.
Implement some machine learning algorithms and apply them to solve problems like image classification, sentiment classification and provide movie recommendations.
Understand databases and systems used to store, manage and retrieve data.
Understand and implement the map-reduce framework used to perform computation in a large-scale distributed setting.
Implement a real-world project using data science techniques.

Related: In addition, if you'd like to learn advanced python libraries for data science, see the Learn Data Science with Python course.

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Syllabus

Introduction to Data Science

An introduction to data science - what it is and examples of data science around us. The introduction article also introduces key components of data science, namely - programming, data, statistics, machine learning and big data.

An Overview of Data Science
Free
12 mins

Mathematics background and Hypothesis Testing

The first 4 tutorials (+2 quizzes) are a review of various topics in mathematics that data science relies on - linear algebra, statistics and probability. In the next tutorial, we discuss Hypothesis Testing, probably the most common use of Data Science. Given multiple options (say different drugs for a disease, or different user interface for a website), it allows us to pick which one is better and by how much. In the last tutorial (+quiz), we cover techniques for network analysis. This will include understanding and implementing the famous PageRank algorithm, which powers Google Search.

Machine Learning

In this section, we'll introduce machine learning and take a look at a number of machine learning algorithms. Algorithms will include regression, classification and clustering algorithms commonly used in data science projects. We'll also introduce recommendation systems (such as those used by Netflix and Amazon). After learning each algorithm, we'll do hands-on projects and apply them to various applications such as hand-written digit recognition, sentiment classification, diabetes diagnosis, and movie recommendations.

List of Project Ideas

This article contains a list of 10 project ideas (including datasets and suggested algorithms). It is recommended that you do at-least one end-to-end project as part of the course.

Start this course

An Overview of Data Science

Category: Data Science and Big Data