About the Course

Corporations, governments, startups and small businesses must leverage insights buried beneath mountains of data. They need specially trained and highly skilled people who can turn endless streams of data into useful insight and actionable intelligence. Our Data Analytics program gives students highly valuable knowledge at the intersection of science, business, and engineering. This truly interdisciplinary program positions trainees to analyze — and improve — any industry.

Python- Programming Course Description

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

What you will learn

  • Importing Datasets
  • Cleaning the Data
  • Data frame manipulation 4) Summarizing the Data
  • Building machine learning Regression models
  • Building data pipelines


  • Certificate of Completion from Kaizen Technologies Inc

Associate Certified Analytics Professional

The Associate Certified Analytics Professional (aCAP) designation connotes an entry-level analytics professional who is educated in the analytics process but may not have experience in practice yet. This prestigious program is the beginning of a career pathway that leads to the elite CAP designation.


  • Basic understanding of statistics / math / programming / data structure

Course Curriculum

Section- 1

  • Python Installation and Pycharm
  • Basics(Windows)
  • Installing Python e.g. Anaconda, pycharm

Section- 2

  • Python Basics
  • Variables and Data Types
  • Variables, Strings, Integers and Floats
  • Comments and Math Operators
  • Indentation

Section- 3

  • Strings and Print
  • Strings and Escape Sequences
  • String Methods, Print, format

Section- 4 

  • Conditional statements and Flow Control statements
  • Flow Control and Comparators
  • Boolean Operators
  • If, Else, and Elif
  • Sequences, collections and iterations

Section- 5

  • Functions
  • Modularity and re-usability using Functions
  • Lambda
  • Packages

Section- 6 

  • Lists, For Loops, Tuples, and Dictionaries
  • Using Functions With Lists
  • List Comprehensions
  • List Slicing

Section- 7

  • Exceptions Handling
  • Try and Except

Section- 8

  • Working with files

Section- 9

  • Object Oriented Programming
  • Introduction
  • Class Object Attributes and Methods
  • Inheritance and Polymorphism

Section- 10

  • Advanced Python Modules
  • Collections Module – counter default dict, Ordered Dict, named tuple Date time
  • Regular Expressions -re

Section- 11

  • Advanced Python Objects and Data Structures
  • Numbers, Strings, Sets, Dictionaries, Lists

Section- 12

  • Numpy and Array manipulation
  • Slicing Arrays

Section- 13

  • Data Frames
  • Importing data
  • Parsing data
  • Renaming Columns of a Dataframe
  • Filtering a Data Frame
  • Basic operations

Section- 14

  • Pandas with Dataframes


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