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Python for Data Science - Jupyter, NumPy, Pandas, Matplotlib
Rating: 4.6 out of 5(4 ratings)
314 students
Created byUday Academy
Last updated 3/2026
English

What you'll learn

  • Introduction of Python
  • Installation of Anaconda & Comments
  • Variables in Python
  • Python Outputs
  • Fundamental Data Types
  • String Methods
  • Indexing & Slicing
  • Inputs in Python
  • List in Python
  • List Methods in Python
  • Tuples in Python
  • Sets in Python
  • Dictionary and Range in Python
  • Bytes & Bytearray in Python
  • Frozenset & NoneType in Python
  • Operators in Python
  • Conditional Statements
  • For Loop
  • While Loop
  • Introduction of Functions
  • Functions with Arguments
  • Introduction to NumPy
  • Modules in Python
  • Accessing Elements in NumPy
  • Introduction of Pandas
  • Accessing Elements in Pandas
  • Introduction of Matplotlib
  • Bi-Variate Plots in Matplotlib

Course content

1 section • 32 lectures • 15h 3m total length

  • Introduction of Python30:59

    This course introduces Python as the base language for data science and AI, and explains its open source, high-level, interpreter nature, with libraries like NumPy, Pandas, and Matplotlib.

  • Installation of Anaconda & Comments34:38

    Install and configure Anaconda for Python data science, launch Jupyter Notebook, and learn Python comments (single-line and multi-line) to explain code and improve readability.

  • Variables In Python37:14

    Learn how to create and manage variables in Python, including dynamic typing, data storage, and memory location concepts. Follow naming rules, memory addresses, and multi-variable assignments.

  • Python Outputs31:35

    Demonstrate how to produce Python outputs using print, variables, and strings, covering single and multiple values, multiple variables, and value-variable combinations.

  • Fundamental DataTypes --- part 141:09

    Introduces Python's fundamental data types—integer, float, complex, boolean, and string (part 1)—and demonstrates basic type casting and inspecting types via outputs.

  • Fundamental Data Types --- part 236:02

    Explore Python strings, indexing and slicing fundamentals, and distinguish mutable versus immutable objects using lists and tuples, highlighting how string characters and data types interact.

  • String Methods21:16

    Explore string methods in Python, including strip, capitalize, title, upper, lower, index, count, split, join, and replace, and distinguish string methods from functions while understanding mutable vs immutable strings.

  • Indexing & Slicing37:55

    Explore indexing and slicing in Python strings, including forward and reverse indexing from zero, start-end-step formulas, and practical examples for data science workflows.

  • Inputs of Python16:32

    Learn how Python inputs work with input() and the default string type, and how to convert to integers or floats when needed. Print and concatenate inputs to form outputs.

  • List in Python26:51

    Learn about Python's list, a mutable, ordered collection of items with square-bracket notation, supporting heterogeneous data, length, nesting, and indexing.

  • List Methods in Python23:12

    Explore Python list methods and how to apply them to data manipulation within Jupyter notebooks, aligning with data science workflows using NumPy, Pandas, and Matplotlib.

  • Tuples in python25:06

    Explore tuples in Python, compare them with lists, and understand mutable versus immutable behavior, indexing, slicing, and fast iteration, including empty and nested tuples.

  • Sets in python20:21

    Explore python sets as unordered collections of unique elements, created with set() or braces, showing duplicate removal, indexing absence, operations such as add, update, discard, remove, clear, union, intersection.

  • Dictionary And Range in Python20:15
  • Bytes & bytearray in Python22:12
  • Frozenset & Nonetype in python22:54

    Explore frozenset and nuntype in Python, compare immutability and ordering with sets, learn how frozensets store unique values without indexing, and see how nuntype represents a none value.

  • Operators in Python(part 1)25:51

    Explore Python operators, including arithmetic, assignment, logical, membership, and identity, with precedence and parentheses, and learn about floor division, modulo, exponents, booleans, and division by zero.

  • Operators in Python(part 2)24:14

    Explore Python operators, including arithmetic, assignment, and comparison, then master logical, membership, and identity operators with practical examples and precedence rules.

  • Conditional Statements(part 1)27:46

    Explore conditional statements in Python, including if, if-else, and if-elif-else, with nested conditions, test expressions, and proper indentation to control program flow.

  • Conditional Statements(part 2)23:34

    Explore Python conditional statements, including if, elif, else, and nested ifs. Practice finding the largest of two or three numbers and applying grade logic.

  • For loop (part 1)19:25

    Learn how for loops replace repetitive code by iterating over sequences in Python, compare them with conditional statements and while loops, and study examples with i, j, k and range.

  • For Loop (part 2)30:29
  • While Loop34:30

    Explore how the while loop drives condition-based iteration in Python, contrasting it with for loops, including syntax, indentation, true/false evaluation, and common controls like break and else.

  • Introduction of Functions29:00

    Explore how functions improve code reusability and reduce repetition by defining and calling user-defined and library functions in Python, contrasting with conditionals and loops.

  • Functions with Arguments23:04

    Explore functions with various argument types in Python, including no arguments, positional, default, keyword, and arbitrary arguments, then apply recursion with a factorial example and build a simple calculator.

  • Introduction of Numpy23:45

    Learn NumPy, the numerical Python library, and use arrays for mathematical and statistical operations, creating 0d–2d arrays with np.array, arange, zeros, and ones for machine learning and deep learning.

  • Modules in Python25:22
  • Accessing Elements in Numpy33:37

    Master how to access elements in NumPy to enable efficient data analysis in Python. Learn array indexing basics essential for data science workflows.

  • Introduction of Pandas31:36

    Learn how Pandas builds on NumPy to create data frames and series, manipulate data, merge frames, and handle missing values for data science.

  • Accessing the elements in Pandas46:19
  • Introduction of Matplotlib23:46

    Explore Matplotlib to visualize data with univariate, bivariate, and multivariate plots, including histograms and box plots, using Pandas data frames with customizable labels for client visuals.

  • Bi-Variate plots in Matplotlib33:03

Requirements

  • No prior programming experience is required — this course starts from absolute basics.
  • A computer or laptop
  • Basic knowledge of using a computer and the internet.
  • Willingness to learn and practice Python programming.
  • Internet connection to install required software such as Python / Anaconda.
  • No prior knowledge of Data Science, NumPy, Pandas, or Matplotlib is needed.

Description

Python is one of the most popular and powerful programming languages in the world, widely used in software development, automation, data analysis, artificial intelligence, and data science. This course is designed to help beginners learn Python programming step by step, starting from the absolute basics and gradually moving toward practical data analysis concepts.

In this course, you will begin with an introduction to Python and learn how to install and set up the development environment using Anaconda. You will then understand core programming concepts such as variables, data types, inputs and outputs, string operations, indexing, and slicing. As you progress, you will master Python collections including lists, tuples, sets, and dictionaries along with their real-world usage.

The course also covers operators, conditional statements, loops, and functions, helping you build strong programming logic and problem-solving skills. After building a solid foundation, you will be introduced to powerful Python libraries such as NumPy, Pandas, and Matplotlib, which are essential tools for data analysis and visualization.

This course focuses on clear explanations, practical demonstrations, and beginner-friendly learning so that anyone can start coding confidently. By the end of this course, you will have a strong understanding of Python programming and the ability to write programs, analyze data, and create visualizations.

Whether you are a student, working professional, or someone looking to start a programming career, this course will provide the skills needed to begin your Python journey with confidence.

Who this course is for:

  • Beginners who want to learn Python programming from scratch.
  • Students and graduates looking to start a career in software development or data science.
  • Working professionals who want to add Python skills to their profile.
  • Developers from other technologies who want to transition into Python.
  • Anyone interested in learning programming, automation, or data analysis.
  • Data science and analytics beginners who want to learn NumPy, Pandas, and Matplotlib.
  • IT professionals preparing for Python interviews.

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