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


# Statistics

Statistics is a form of mathematical analysis that uses different tools on a given data set to describe and make an inference out of it. 

# Types of statistics 

Statistics is further divided into two branches: Descriptive and Inferential

## Descriptive statistics

Descriptive statistics deals with summarizing and describing the data at hand. Descriptive statistics comes up with the following things to make sense out of the data.


### Elements of descriptive statistics

You'll learn about statistics in this lesson. Statistics deals with studying methods for collecting, analyzing, and describing data. 


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 ?

Statistics

Statistics

Types of statistics

Descriptive statistics