Data Science with Python - Innozant MIS & Data Analytics & Data Science

Administrator Intermediate


Data science is an interdisciplinary field that extracts valuable information from  data useful for business. From data analysis, cleansing and wrangling to the interpretation of patterns, it turns  noisy, structured and unstructured data with the use of statistics, algorithms and procedures & other tools. It combines tools, methods, and technology to generate meaningful information from data. Modern organizations are inundated with data; there is a proliferation of devices that can automatically collect and store information. Data science allows businesses to uncover new patterns and relationships that have the potential to transform the organization.


  1. Capture, (data acquisition, data extraction); 
  2. Maintain (data warehousing, data architecture); 
  3. Analyze (predictive analysis, regression, qualitative analysis); 
  4. Communicate (data reporting, data visualization, business intelligence, decision making).

Therefore data scientists need a strong  knowledge of computer programming, data analytics, artificial intelligence (AI), and predictive analytics. They create the applications and machine learning algorithms that transform raw data, assist with business decision making, and power scientific discovery.

This course has 4 Modules as follows:
Module1:   Python Basics
Module 2: Data Analytical Tools Using Python
Module 3: Machine Learning With Python
Module 4: Artificial Intelligence & Deep Learning With Python

In this course:

  • Describe common Python functionality and features used for data science

  • We will learn about various Data Science and Machine Learning skills.
  •  Query DataFrame structures for cleaning and processing
  • Utilize several data visualization tools, techniques and libraries in Python to present data visually.
  • Evaluate The Accuracy & Generality Of Machine Learning Models
  • Build Basic Neural Networks & Deep Learning Algorithms
  • Real-world Data Science projects


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Topics for this course

48 Lessons

Module1: Python Basics

Introduction to Python00:00:00
Features and Applications of Python00:00:00
Introduction to Anaconda/Jupyter00:00:00
Basics of Jupyter00:00:00
Data types & Variables in Python00:00:00
Types of Operators in Python00:00:00
Strings: String indexing/ Slicing, String methods, immutability00:00:00
Conditional Statements: If, If-else, If-elif, Nested if00:00:00
Loops: Iterators/Iterables, For loops, range function,while loops00:00:00
Pattern based problems : Number patterns, Alphabet patterns, Shapes patterns, Mixed patterns00:00:00
User Defined function : def keyword , creating a function, return keyword, Function inside a function, Recursion, *args , ** kwargs, Practice problems on Functions00:00:00
List in Python : List indexing and slicing, Mutable Lists, Finding min, max and sum for a given list, Iteration in Lists using for and while loops00:00:00
List methods: I — append, extend, pop, insert , List methods II — sort, reverse, clear, remove, List methods III — index, count , List comprehension00:00:00
Tuple in Python : Definition and usage, Tuple indexing and slicing, Immutable Tuple, Iteration in Tuple using for and while Loop, Tuple methods — index and count00:00:00
Set in Python: Use, Set Methods & Comprehension00:00:00
Dictionary in Python: Definition and usage , Iteration in dictionary using for and while loops, Dictionary methods & Comprehension, Practice problems on list, tuple, set and dictionary00:00:00
Inbuilt functions in Python: Enumerate, zip, mop, reduce, filter, lambda function, evalu00:00:00
Exception handing : Errors and Exception00:00:00
File Handling: Open, Close, Read, Write, Append, File operations00:00:00
Date Time Module in Python00:00:00

Module 2: Data Analytical Tools Using Python

Module 3: Machine Learning With Python

Module 4: Artificial Intelligence & Deep Learning With Python

Al (Deep Learning)

Introduction to Google Colab


Understanding different Activation Functions

Understanding different Optimizers

Understanding different Loss Functions

ANN : Artificial Neural Network

CNN : Convolutional Neural Network

  • Course level: Intermediate