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



Data Science is about unearthing insights from data that can impact future decisions. It involves the part of predictive analytics using Machine Learning and related fields. 

## Required skills

* Python language, R Programming, and SQL
* Machine Learning
* Deep Learning
* Natural Language Processing
* Computer Vision
* Information Retrieval
* Python libraries for Machine Learning like scikit-learn, Ttensorflow, keras, and PyTorch.
* NoSQL Databases and ElasticSearch




Data analysis deals with extracting the information out of data by cleaning, and transforming it. It has many overlapping areas with Data Science like presenting the insights to the team and making visualizations. But it misses the Machine Learning Part.

## Required Skills

* Any open source tool which is provided with complete documentation
* SAS
* Rapid Miner
* Microsoft Excel
* Python
* Tableau
* PowerBI
* OpenRefine






In this lesson, you'll learn about Data Science, Data Analysis, and Data Engineering. These fields are differentiated based on the activities that we do in them.


Data Science vs. Data Analysis vs. Data Engineering

In this lesson, you'll learn about Data Science, Data Analysis, and Data Engineering. These fields are differentiated based on the activities that we do in them.