This list consists of ~20 tutorials to learn data science. You can think of this list as a "Free Online Nano Book". You'll learn core data science concepts, algorithms and applications. Everything is 100% free.
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.
Data Science is an interdisciplinary field, and makes use of methods and technologies from different fields such as computer science, databases, mathematics, statistics and machine learning. It is involved with the collection, preparation, analysis, visualization, management and preservation of data.
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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. This is followed by a simple example illustrating the use of data science to solve a problem. The example provides context for how the concepts we'll learn in the rest of the course fit in.
We'll introduce the important Python libraries for Data Science. Numpy (numerical python) provides vector and matrix primitives in Python. Pandas introduces a Data Frame, which is basically a table (like a database table or an excel sheet, but in Python). Matplotlib is used for plotting and visualization of data.
The first three tutorials are a review of various topics in mathematics that data science relies on - linear algebra, statistics and probability. In the last tutorial, we will discuss Hypothesis Testing. Hypothesis Testing is 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. We'll also see 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).
This detailed example demonstrates the workflow of a data scientist. You'll see how the above techniques of data exploration, data visualization and machine learning come together.
