Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

## Big Data 

Big Data is data with a huge size. It is not possible to analyze data and produce models out of it by using traditional data processing and analyzing techniques. People using Facebook, Instagram, Twitter, and other social media sites generate data every minute. The following image shows what is happening in a minute on the Internet.
 






# Big Data 

Big Data is data with a huge size. It is not possible to analyze data and produce models out of it by using traditional data processing and analyzing techniques. People using Facebook, Instagram, Twitter, and other social media sites generate data every minute. The following image shows what is happening in a minute on the Internet.
 






In this lesson, you’ll discover more about Big Data. It refers to the techniques used to extract and analyze information from large datasets that are difficult to process with traditional tools.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

What is Big Data ?

Big Data