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Python Data Analysis 2026: Master Pandas with Projects

Name: Python Data Analysis 2026: Master Pandas with Projects
Rating: 4.0 (8 reviews)

Learn Python fundamentals and Pandas for data analysis, cleaning, transformation, and real-world projects

Created byTech With Mala

Last updated 3/2026

English

What you'll learn

Perform data analysis and manipulation on 1D and 2D datasets using powerful Pandas techniques.
Apply key operations such as aggregation, grouping, pivoting, concatenation, merging, joining, sorting, and filtering.
Read and write datasets, and efficiently select rows and columns using conditions.
Clean and prepare data by handling missing values and improving data quality.
Build a strong foundation in Python, including variables, flow control, data types, and operators.

Course content

8 sections • 139 lectures • 16h 16m total length

Course Overview2:39
Prerequisites to learn Python0:36
Download and install Python, execute first Python program3:02
Download and install PyCharm, execute first Python program2:24
What is Python1:05
Where do we use Python1:13
Python History0:54
Explore Python's history from its 1989 development by Guido van Rossum to its 1991 public release, noting the Monty Python origin and its status as a popular beginner-friendly language.
Why is Python popular0:34
Python is simple and easy to read6:57
Python is dynamically typed programming language3:05
Python is platform independent1:27
Explore Python's platform independence, enabling write once and run anywhere across Mac, Windows, and Linux, reducing development time and budget compared with platform-dependent C programs.
Python is portable0:41
Python is High-level programming language0:18
Python is Freeware and Open Source Software (FOSS)3:48
Python is Procedure oriented and object oriented3:21
Python is Interpreted programming language0:37
Python is Extensible0:59
Python is Embedded0:29
Python has Extensive library support3:26
Limitations of Python0:51
Python versions backward compatibility2:11

Flow control Introduction0:37
Conditional or selection statements - if, if-else, if-elif-else, if-elif28:45
Iterative statements or loops - for loop and while loop28:48
Loops example 1: Program to display stars in a given row3:58
Learn how to display a given number of stars in a row using a for loop and the string repetition operator in Python, with input handling and end parameter behavior.
Loops example 2: Program to display square pattern with stars5:20
Loops example 3: Program to display square pattern with fixed number3:51
Loops example 4: Program to display square pattern with fixed alphabet2:48
Loops example 5: Program to display square pattern with given number2:51
Loops example 6: Program to display square pattern with fixed number in each row6:00
Loops example7:Program to display square pattern with fixed alphabet in each row4:28
Loops example 8: Program to display square pattern with numbers in each row3:30
Loops example 9: Display square pattern with numbers in descending order2:57
Loops example 10: Program to display right angle triangle with stars5:41
Loops example 11: Display right angle triangle with numbers in descending order4:45
Loops example 12: Program to display pyramid pattern with stars4:45
Transfer statements - break, continue11:21
Loops with else block - for-else, while-else4:19
pass and del keywords10:43

Data Types Introduction2:50
‘int’ data type9:40
‘float’ data type4:34
‘complex’ data type7:37
‘bool’ data type2:37
‘str’ datatype4:47
‘str’ datatype – access elements of a string using 'index'6:10
‘str’ datatype – slice operator23:06
‘str’ datatype – mathematical operations on a string4:01
‘str’ datatype – comparison operators on a string3:33
‘str’ datatype – remove space from a string4:10
‘str’ datatype – find 'substring' from a given string33:18
‘str’ datatype – example 1 : reverse a given string5:31
‘str’ datatype – example 2 : reverse order of words in a given string3:34
‘str’ datatype – example 3 : reverse internal content of words in a given string2:51
‘str’ datatype – example 4 :get characters at even and odd positions in a string6:07
‘str’ datatype – example 5 : print a string by merging characters of two strings5:06
‘str’ datatype – example 6 : sort characters present in a given string3:50
‘str’ datatype – example 7 : string as alphabet times digit8:07
‘str’ datatype– eg8:string as alphabet followed by digit char from that alphabet4:51
‘str’ datatype – example 9 : remove duplicate characters from a given string2:16
‘str’ datatype – eg10: find the number of occurrences of each character present6:01
‘str’ datatype – example 11 : Find if a string is Palindrome1:49
difference between str() and repr()8:52
Type casting or coercion18:35
Immutability8:08
‘range’ data type6:54
‘list’ data type Introduction5:50
‘list’ data type – Ways of creating a list5:36
‘list’ data type – Traverse elements of a list5:18
‘list’ data type – List API - functions of list39:45
‘list’ data type – Mathematical operations on a list9:20
‘list’ data type – Nested lists6:50
‘list’ data type – list comprehension23:07
‘tuple’ data type Introduction6:18
‘tuple’ data type – Ways of creating a tuple5:33
‘tuple’ data type - Mathematical operations on a tuple5:25
‘tuple’ data type – Tuple API - functions of tuple6:47
‘tuple’ data type – tuple packing and unpacking5:00
‘tuple’ data type – tuple comprehension1:33
‘tuple’ data type – print sum and average of a tuple of numbers2:59
difference between ‘tuple’ and ‘list’ datatypes5:37
‘set’ data type Introduction4:26
‘set’ data type – Ways of creating a set5:12
‘set’ data type – Set API - functions of set12:52
‘set’ data type - Mathematical operations on a set6:51
‘set’ data type – set comprehension2:14
‘set’ data type – remove duplicates present in a list w/ and w/o using set3:26
‘set’ data type – print different vowels present in a given word using set2:43
difference between ‘list’ and ‘set’ datatypes1:13
‘frozenset’ data type2:48
‘dict’ data type Introduction11:55
‘dict’ data type - demonstrate with an example12:59
‘dict’ data type – Dict API - functions of dict24:26
‘dict’ data type – sum of dict values2:14
‘dict’ data type – number of occurrences of characters in a string using dict6:03
‘dict’ data type – number of occurrences of vowels in a string using dict3:07
‘dict’ data type – sample application on how to apply dict functions9:12
‘dict’ data type – dict comprehension4:40
Merge collections - list, tuple, set, dict8:05
Nested collection6:06
Nested collection application16:18
‘bytes’ data type4:47
‘bytearray’ data type2:51
‘None’ data type5:37

Operators Introduction0:31
Arithmetic operators +, -, *, /, %, //, **9:02
Relational operators <, <=, >, >=11:35
Equality operators ==, !=8:06
Logical operators - and, or, not11:35
Assignment operators =, +=, -=, *=, /=, %=, //=, **=, &=, |=, ^=, >>=, <<=6:04
Ternary operator9:11
Special operators - Identity and membership operators5:56
Operator precedence4:14
Walrus operator or Assignment Expressions13:31

Input Statements Demo6:26
Explore input statements in Python, comparing raw_input from Python 2.x with input in Python 3.x, and demonstrate reading a single value, plus explicit typecasting to int, float, and bool.
Input statements-Sum Of Two Numbers Using Input and refactor4:33
Input statements-Print Employee Data Using Input and refactor7:38
Input statements-eval function demo6:05
Input statements-read multiple values10:15
Input statements – Command Line Arguments15:27
Output statements21:25

Pandas introduction1:27
Creating a DataFrame in Pandas6:56
Create a data frame in pandas by installing and importing pandas, then converting Python data structures—lists, dictionaries, and dictionary of lists—into data frames in a Jupyter notebook.
Pandas DataFrame: Reading and writing data7:34
Pandas DataFrame - useful functions - head, tail, describe, info and shape5:30
Pandas DataFrame: data selection17:17
Pandas DataFrame: more useful functions - isin, drop, drop_duplicates, rename10:29
Pandas DataFrame: groupby, pivot_table20:10
Pandas DataFrame: Missing value treatment13:51
Pandas DataFrame: sorting6:07
Pandas DataFrame: concat, merge, join18:52
Pandas Series5:35

Requirements

No prior programming experience required, just access to a computer with good internet connection.

Description

Learn Data Analysis with Python and Pandas through a practical, hands-on approach designed for beginners and aspiring data professionals.

This course takes you step by step—from setting up your environment to performing real-world data analysis using Pandas. You’ll start by installing Python (Anaconda), PyCharm, and Jupyter Notebook, then gradually build a strong foundation in Python before diving deep into data analysis.

By the end of this course, you’ll be confident in working with datasets, cleaning data, and extracting meaningful insights using Pandas.

What You’ll Learn

Set up Python using Anaconda, PyCharm, and Jupyter Notebook
Understand Python fundamentals (variables, data types, operators, loops, and more)
Create and work with Pandas DataFrames from real datasets
Read, write, and manipulate data efficiently
Use essential Pandas functions: head(), tail(), describe(), info(), shape
Perform data selection and filtering
Clean data and handle missing values
Apply powerful functions like isin(), drop(), drop_duplicates(), rename()
Group, sort, and transform data using groupby, pivot tables, and sorting
Combine datasets using concat, merge, and join
Work with Pandas Series with real examples

Python Fundamentals Included

Installation and setup
Indentation, comments, and syntax basics
Flow control: if/else, loops (for, while)
Pattern-based coding exercises
Data types: int, float, string, list, tuple, set, dictionary, and more
Operators: arithmetic, logical, relational, and beyond
Input/output and core programming concepts

Why This Course?

Beginner-friendly, step-by-step guidance
Hands-on learning with real datasets
Practical examples for every concept
Build job-ready data analysis skills
Lifetime access with future updates

Outcome

By the end of this course, you’ll have a solid understanding of Python and Pandas, enabling you to analyze data, build your own projects, and move toward roles in data analysis or software development.

Guarantee

This course comes with a 30-day money-back guarantee—so you can enroll with confidence.

Take the next step in your career and start mastering data analysis today.

Enroll now and start learning!

Who this course is for:

Absolute beginners who have never programmed before or trying to switch from other languages to Python

Python Data Analysis 2026: Master Pandas with Projects

What you'll learn

Explore related topics

Course content

Python Introduction, Installation and Setup21 lectures • 41min

Python Indentation and comments, Escape characters, Constants and Identifiers4 lectures • 17min

Python Flow control18 lectures • 2hr 15min

Python Data types65 lectures • 8hr 14min

Python Operators10 lectures • 1hr 20min

Python Statements7 lectures • 1hr 12min

Python Anaconda Distribution and Jupyter notebook overview3 lectures • 25min

Python for Data Science - Data Analysis or Data Manipulation Using Pandas11 lectures • 1hr 54min

Requirements

Description

Who this course is for: