Course curriculum

  • 1

    Student Guide Book

    • Student Guide Book

    • Schedule/curriculum Time Table

    • Jupyter NoteBook Installation Guide
  • 2

    Live Sessions

    • Data Science _1st Live Session _09th Feb 2021 _ 6:00 pm

    • Data Science_ 2nd Live Session_11th Feb 2021_6:00 PM

    • Data Science _3rd Live Session_ Feb 13, 2021 06:00 PM

    • Data Science_4th Session _Feb 16,2021 06:00PM

    • Data Science_5th Session_ Feb 18,2021 06:00PM

    • Data Science_6th Session_Feb 20, 2021 06:00 PM

    • Data Science_7th Session_ Feb 23, 2021 06:00 PM

    • Data Science_8th Session_ Feb 25, 2021 06:00 PM

    • Data Science_9th Session_ Feb 27, 2021 06:00 PM

    • Data Science _10th Live Session_ Mar 2, 2021 08:00 PM

    • Data Science_11th Live Session_ Mar 4, 2021 06:00 PM

    • Data Science_12th Live Session_ Mar 6, 2021 06:00 PM Cancelled

    • Data Science_12th Live Session_ Mar 9, 2021 06:00 PM

    • Data Science_13th Live Session_ Mar 11, 2021 06:00 PM

    • Data Science_14th Live Session_ Mar 13, 2021 06:00 PM

    • Data Science _15th Live Session_Mar 16, 2021 06:00 PM

    • Data Science _16th Live Session_ Mar 18, 2021 06:00 PM

    • Data Science_17th Live Session_ Mar 20, 2021 06:00 PM

    • Data Science _17th Session_Mar 24, 2021 06:00 PM

    • Data Science_18th Live Session_ Mar 25, 2021 06:00 PM

    • Data Science_19th Live Session_ Mar 27, 2021 06:00 PM

    • Data Science_20th Live Session_ Mar 29, 2021 06:00 PM Cancelled

    • Data Science_21st Live Session_ Mar 30, 2021 06:00 PM

    • Data Science -22nd Live Session _Mar 31, 2021 06:00 PM
  • 3

    Recorded Sessions

    • DS_1st Sessions_9/02/2021

    • DS_ 2nd Session_11/02/2021

    • DS_3rd Session_13-02-2021

    • DS_4t Session_16-02-2021

    • DS_5th Session_18-02-2021

    • DS_6th Session_23-02-2021

    • DS_7th Session_25-02-2021

    • DS_8th Session_27-02-2021

    • DS_9th Session_02-03-2021

    • DS_10th Session_04-03-2021

    • ds_11th Session_09-03-2021

    • DS_12th Session_11-03-2021

    • DS_13th Session_13-03-2021

    • DS_14th Session_16-03-2021

    • DS_15th Session _18-03-2021

    • DS_16th Session_20-03-2021

    • DS_17th Session_24-03-2021

    • DS_18th Session_25-03-2021

    • DS_19th Session_27-03-2021

    • DS_20th Session_30-03-2021

    • DS_21st Session_31-03-2021
  • 4

    Project

    • Mini Project

    • Major Project
  • 5

    Module 1: Installing and Starting Python

    • 1.1: Overview

    • 1.2: Installing Python

    • 1.3: Interactive Python

    • 1.4: Significant Whitespace

    • 1.5: Python Culture

    • 1.6: The Python Standard Library

    • 1.7: Summary

    • Installing and Starting Python Slides
  • 6

    Module 2: Scalar Types, Operators and Control Flows

    • 2.1: Overview

    • 2.2: Relational Operators

    • 2.3: Control Flow

    • 2.4: While Loops

    • 2.5: Summary

    • Scalar Type Operators and Control Flow Slides
  • 7

    Module 3: Introducing Strings, Collections and Iterations

    • 3.1: Overview

    • 3.2: String

    • 3.3: String Literals

    • 3.4: Bytes

    • 3.5: List

    • 3.6: Dictionary

    • 3.7: For Loop

    • 3.8: Putting It All Together

    • 3.9: Summary

    • Introducing Strings, Collections and Iterations Slide
  • 8

    Module 4: Modularity

    • 4.1: Overview

    • 4.2: Modules

    • 4.3: Functions

    • 4.4: Name

    • 4.5: The Execution Model

    • 4.6: Command Line Arguments

    • 4.7: Docstrings

    • 4.8: Comments

    • 4.9: Shebang

    • 4.10: Summary

    • Modularity Slides
  • 9

    Module 5: Objects and Types

    • 5.1: Overview

    • 5.2: Passing Arguments and Returning Values

    • 5.3: Function Arguments

    • 5.4: Python's Type System

    • 5.5: Scopes

    • 5.6: Everything is an Object

    • 5.7: Summary

    • Objects and Types
  • 10

    Module 6: Built-in Collections

    • 6.1: Overview

    • 6.2: Tuples

    • 6.3: Strings

    • 6.4: Ranges

    • 6.5: Lists

    • 6.6: Dictionaries

    • 6.7: Sets

    • 6.8: Protocols

    • 6.9: Summary

    • Built-in Collections Slides

    • Codes for Built-in Collections

  • 11

    Module 7: Exceptions

    • 7.1: Overview

    • 7.2: Exceptions and Control Flow

    • 7.3: Handling Exceptions

    • 7.4: Exceptions and Programmer Errors

    • 7.5: Re-raising Exceptions

    • 7.6: Exceptions and Part of the API

    • 7.7: Exceptions and Protocols

    • 7.8: Avoid Explicit Type Checks

    • 7.9: It's Easier to Ask Forgiveness Than Permission

    • 7.10: Cleanup Actions

    • 7.11: Platform Specific Code

    • 7.12: Summary

    • Exceptions Slide
  • 12

    Module 8: Iterations and Iterables

    • 8.1 Overview

    • 8.2: List and Set Comprehensions

    • 8.3: Dictionary Comprehensions

    • 8.4: Filtering Comprehensions

    • 8.5: Iteration Protocols

    • 8.6: Generator Functions

    • 8.7: Maintaining State in Generators

    • 8.8: Laziness and the Infinite

    • 8.9: Generator Expressions

    • 8.10: Iteration Tools

    • 8.11: Summary

    • Iteration and Iterables Slides
  • 13

    Module 9: Classes

    • 9.1: Overview

    • 9.2: Classes

    • 9.3: Defining Classes

    • 9.4: Instance Methods

    • 9.5: Instance Initializers

    • 9.6: A Second Class

    • 9.7: Collaborating Classes

    • 9.8: Booking Seats

    • 9.9: Methods for Implementation Details

    • 9.10: Object Oriented Design with Function Objects

    • 9.11: Polymorphism and Duck Typing

    • 9.12: Inheritance and Implementation Sharing

    • 9.13: Summary

    • Classes Slides
  • 14

    Module 10: File IO and Resource Managements

    • 10.1: Overview

    • 10.2: Opening Files

    • 10.3: Writing Text

    • 10.4: Reading Text

    • 10.5: Appending Text

    • 10.6: Iterating Over Files

    • 10.7: Closing Files with Finally

    • 10.8: With Blocks

    • 10.9: Binary Files

    • 10.10: Bitwise Operators

    • 10.11: Pixel Data

    • 10.12: Reading Binary Data

    • 10.13: File-like Objects

    • 10.14: Context Manager

    • 10.15: Summary

    • File IO and Resource Managements Slide
  • 15

    MTA Exam Objectives

    • MTA Exam Objectives

  • 16

    Internship Project 1 - Linear discriminant analysis

    • Wine Classification

    • Code
  • 17

    Internship project -2 _Hierarchical Clustering

    • Part-1

    • Part-2

    • Data Set

    • Internship Project Submission Link