Learn the basics of data science and how to work with important libraries such as NumPy, pandas, Beautiful Soup, etc.

Become a Data Scientist

This module will get us familiar with the state of data science and its fundamental concepts. We'll be going through the applications of data science and a few libraries which are essential to solve any problem in this field, such as Beautiful Soup, Scrapy, NumPy, pandas, spaCy, etc.
We'll also learn about data manipulation with NumPy as well as probability and statistics.
By the end of this module, we'll have basic and essential knowledge of data science and its libraries.

Getting Started with Data Science

In this chapter, you'll learn how to perform data processing and manipulation using NumPy.

<h2>A. Data processing</h2>
When asked about Google's model for success, Peter Norvig, the director of research at Google, famously stated,
<blockquote>"We don't have better algorithms than anyone else; we just have more data."</blockquote>
Though probably an understatement (given the amount of talent employed at Google), the quote does provide a sense of just how vital data is to having successful outcomes.
People normally discuss the importance of data in the context of machine learning. No matter how sophisticated a machine learning model is, it will not perform well unless it has a reasonable amount of data to train on. On the other hand, given a large and diverse set of training data, a good deep learning model will significantly outperform non-deep-learning algorithms.
However, data is not just limited to machine learning. Companies use data to identify customer trends, political parties use data to determine which demographics they should target, sports teams use data to analyze players, etc.


 <img src="/api/collection/6083138522447872/5629499534213120/page/5689792285114368/image/5643440998055936">


> Example baseball data used in <a href="https://en.wikipedia.org/wiki/Sabermetrics" target="_blank">sabermetrics</a>. The concept was popularized by the 2011 film, Moneyball.</figcaption>
The universal usage of data makes data processing, the act of converting raw data into a meaningful form, an essential skill to have.

An overview of data processing and the NumPy library.

Introduction

A. Data processing