Prerequisites: Enthusiast to learn data science

Target Audience: Student/Proffsional from different background

Syllabus: The total syllabus is divided into four major parts

1)Python as data science tool:- This section covers all the features of python.It includes basic syntex,data types,decorators etc.It also covers the implementation of essential packages for data science numpy,pandas,matplotlib etc

2)Statistics:- The foundation of data science is Statistics. All the basic statistical concepts and their implementation required for data science are explained in this section.Central tendency,hypothesis testing etc.

3)Machine Learning  Along With Use Case:- This section contains mutilple algorithms(Supervised,Unsupervised) along with their use case with different dataset.Some are logistic regression,random forest

4)Basic Of Natural Language Processing:- This section covers core technique of processing textual data.We introduce here very basic but important concept of text mining. Data collection, data preprocessing of textual data are the primary focus of this section. Some of the techniques discussed here are stemming,tokenization,webscrapping etc