Getting Started with Python | Python & Anaconda Installation, Jupyter Notebook | k2analytics.co.in

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Python Programming is the most in-demand programming language. Data Scientist is the most sought-after resources in the job market.
Here comes our small contribution to help you learn Python Programming for Data Science.


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Python Programming for Data Science training video duration: 10 Hours Content.

Our video provides detailed Python Programming knowledge. It starts from Step-by-Step Installation Guide, Basics of Python Programming to Advanced Python Programming like Programming Structures & User Defined Functions.

The Table of Content of the 11 part video series is given below:

1) Step-by-step instructions on the installation of Anaconda.
2) Basic introduction to the World of Python.
3) Efficiently working on Jupyter Notebook and Spyder IDE
4) Data Types & Data Structures in Python
-- Variables
-- Strings
-- Numbers
-- Boolean
-- List (Create, Add, Delete elements from a list)
-- Tuple
-- Dictionary
-- Set
-- Mutable & Immutable Objects
-- Type Conversion (Casting)
-- del (Delete) objects

5) Modules and Packages in Python
-- Creating your own module
-- Importing packages
-- math, os, sys packages
-- NumPy, Pandas, Scikit-learn
-- input() function to take user keyboard input
-- print()

6) Numpy & Pandas
-- 1D, 2D, 3D, n-dimension arrays
-- Series
-- DataFrame
-- Cell, Column, and Row referencing
-- iloc and loc function
-- attributes in DataFrame
-- Adding / Dropping rows and columns
-- Reset Index
7) Data Import & Export
-- Read .csv file
-- Read Fixed Width File
-- Read Excel File
-- Import from Database
-- Pickle to save and load Python Objects
-- Export DataFrame to csv file format

8) Programming Structures (Loops & Conditional Statements)
-- if, if-else, if-elif-else
-- For loop, While loop
-- Break & Continue

9) Data Manipulations in Python
-- drop column / row
-- create derived columns
-- filter
-- sort
-- merge
-- aggregate
-- rename columns
-- reset index

10) Functions
-- in-built functions
-- user defined functions
-- anonymous lambda function
-- local and global variables

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