Data Science Basic | Python for Data Science by Diljeet Singh | Learn Smarter
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Python for Data Science

Python's simplicity and robust ecosystem make it a fundamental tool for data science. The chapter covers basic programming concepts, essential libraries for data manipulation and visualization, and the setup of a Python environment using Jupyter Notebook.

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Sections

  • 1

    Why Python For Data Science?

    Python is the preferred programming language for data science due to its simplicity, versatility, and strong community support.

    Learning Practice

  • 2

    Setting Up The Environment

    This section covers the essential steps to set up a Python programming environment for data science.

    Learning Practice

  • 2.1

    Install Options

    This section discusses how to install Python and set up an integrated environment suitable for data science.

    Learning Practice

  • 3

    Python Basics For Data Science

    This section introduces the fundamental concepts of Python, including variables, data types, loops, and functions essential for data science.

    Learning Practice

  • 3.1

    Variables And Data Types

    This section introduces the basic building blocks of Python programming: variables and the different types of data they can hold.

    Learning Practice

  • 3.2

    Lists And Loops

    This section introduces Python lists and loops, which are essential for handling data in Python programming.

    Learning Practice

  • 3.3

    Functions

    This section introduces the concept of functions in Python, focusing on their definition, syntax, and key characteristics.

    Learning Practice

  • 4

    Essential Libraries

    This section introduces essential Python libraries pivotal for data science, including NumPy, Pandas, and Matplotlib.

    Learning Practice

  • 4.1

    Numpy (Numerical Python)

    NumPy is a fundamental library for numerical operations in Python, particularly in data science.

    Learning Practice

  • 4.2

    Pandas (Data Manipulation)

    This section introduces Pandas, a powerful library for data manipulation and analysis in Python, focusing on DataFrames and their key functionalities.

    Learning Practice

  • 4.3

    Matplotlib (Data Visualization)

    This section focuses on Matplotlib, a core library in Python for creating visualizations.

    Learning Practice

  • 5

    Working With Files

    This section introduces how to read and write CSV files using the Pandas library in Python, essential for handling data in data science projects.

    Learning Practice

  • 5.1

    Reading Csv

    This section covers the basics of reading CSV (Comma-Separated Values) files using Python's Pandas library.

    Learning Practice

  • 5.2

    Writing Csv

    This section covers how to write data into a CSV file using Python's Pandas library.

    Learning Practice

  • 6

    Chapter Summary

    This chapter summary highlights Python's significance in data science, covering its syntax, key data types, and vital libraries.

    Learning Practice

References

Chapter 3_ Python for Data Science.pdf

Class Notes

Memorization

What we have learnt

  • Python is essential for dat...
  • Key data types include stri...
  • Python structures like loop...

Revision Tests