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".
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.
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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The first 3 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.
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.
