Ultimate Python Bootcamp For Data Science & Machine Learning

Ultimate Python Bootcamp For Data Science & Machine Learning

Ultimate Python Bootcamp For Data Science & Machine Learning

Learn How To Code Python For Data Science, ML & Data Analysis, With 100+ Exercises and 4 Real Life Projects !

Language: english

Note: 4.1/5 (851 notes) 175,016 students

Instructor(s): Pruthviraja L

Last update: 2020-03-16

What you’ll learn

  • Build a Solid Foundation in Data Analysis with Python
  • You will be able to work with the Pandas Data Structures: Series, DataFrame and Index Objects
  • Learn hundreds of methods and attributes across numerous pandas objects
  • You will be able to analyze a large and messy data files
  • You can prepare real world messy data files for AI and ML
  • Manipulate data quickly and efficiently
  • You will learn almost all the Pandas basics necessary to become a ‘Data Analyst’



  • Students must be willing to learn the Data Analysis with Python language
  • If you know basics of Python that is well and good
  • Basic Or intermediate experience with Microsoft Excel or another spreadsheet software, but not necessary
  • Basic knowledge of data types (strings, integers, floating points, Booleans) etc, but not necessary
  • Basic Programming knowledge Or knowing any other programming languages will also helps



Hi, dear learning aspirants welcome to “Ultimate Python Bootcamp For Data Science & Machine Learning ” from beginner to advanced level. We love programming. Python is one of the most popular programming languages in today’s technical world. Python offers both object-oriented and structural programming features. Hence, we are interested in data analysis with Pandas in this course. 

This course is for those who are ready to take their data analysis skill to the next higher level with the Python data analysis toolkit, i.e. “Pandas”.

This tutorial is designed for beginners and intermediates but that doesn’t mean that we will not talk about the advanced stuff as well. Our approach of teaching in this tutorial is simple and straightforward, no complications are included to make bored Or lose concentration. 

In this tutorial, I will be covering all the basic things you’ll need to know about the ‘Pandas’ to become a data analyst or data scientist.   

We are adopting a hands-on approach to learn things easily and comfortably. You will enjoy learning as well as the exercises to practice along with the real-life projects (The projects included are the part of large size research-oriented industry projects).

I think it is a wonderful platform and I got a wonderful opportunity to share and gain my technical knowledge with the learning aspirants and data science enthusiasts.

What you will learn:

You will become a specialist in the following things while learning via this course

“Data Analysis With Pandas”.

  • You will be able to analyze a large file

  • Build a Solid Foundation in Data Analysis with Python

After completing the course you will have professional experience on;

  • Pandas Data Structures: Series, DataFrame and Index Objects

  • Essential Functionalities

  • Data Handling

  • Data Pre-processing

  • Data Wrangling

  • Data Grouping

  • Data Aggregation

  • Pivoting

  • Working With Hierarchical Indexing

  • Converting Data Types

  • Time Series Analysis

  • Advanced Pandas Features and much more with hands-on exercises and practice works.


Who this course is for

  • Beginner Python developers – Curious to learn about Data Science Or Data Analysis
  • Data Analysis Beginners
  • Aspiring data scientists who want to add Python to their tool arsenal
  • Students and Other Professionals
  • AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects
  • Data Analyst job seekers who wants to update their Resume with Python’s data analysis toolkit


Course content

  • Getting Started
    • Course Introduction
    • How To Get Most Out Of This Course
    • Better To Know These Things
    • How To Install Python IPython And Jupyter Notebook
    • How To Install Anaconda For macOS And Linux Users
    • How To Work With The Jupyter Notebook Part-1
    • How To Work With The Jupyter Notebook Part-2
  • Pandas Building Blocks
    • How To Work With The Tabular Data
    • How To Read The Documentation In Pandas
  • Pandas_Data Structures
    • Theory On Pandas Data Structures
    • How To Construct The Pandas Series
    • How To Construct The DataFrame Objects
    • How To Construct The Pandas Index Objects
    • Practice Part 01
    • Practice Part 01 Solution
  • Data Indexing And Selection
    • Theory On Data Indexing And Selection
    • Data Selection In Series Part 1
    • Data Selection In Series Part 2
    • Indexers Loc And Iloc In Series
    • Data Selection In DataFrame Part 1
    • Data Selection In DataFrame Part 2
    • Accessing Values Using Loc Iloc And Ix In DataFrame Objects
    • Practice Part 02
    • Practice Part 02 Solution
  • Essential Functionalities
    • Theory On Essential Functionalities
    • How To Reindex Pandas Objects
    • How To Drop Entries From An Axis
    • Arithmetic And Data Alignment
    • Arithmetic Methods With Fill Values
    • Broadcasting In Pandas
    • Apply And Applymap In Pandas
    • How To Sort And Rank In Pandas
    • How To Work With The Duplicated Indices
    • Summarising And Computing Descriptive Statistics
    • Unique Values Value Counts And Membership
    • Practice_Part_03
    • Practice_Part_03 Solution
  • Data Handling
    • Theory On Data Handling
    • How To Read The Csv Files Part – 1
    • How To Read The Csv Files Part – 2
    • How To Read Text Files In Pieces
    • How To Export Data In Text Format
    • How To Use Python’s Csv Module
    • Practice_Part_04
    • Practice_Part_04 Solution
  • Data Cleaning And Preparation
    • Theory On Data Preprocessing
    • How To Handle Missing Values
    • How To Filter The Missing Values
    • How To Filter The Missing Values Part 2
    • How To Remove Duplicate Rows And Values
    • How To Replace The Non Null Values
    • How To Rename The Axis Labels
    • How To Descretize And Bin The Data Part – 1
    • How To Filter And Detect The Outliers
    • How To Reorder And Select Randomly
    • Converting The Categorical Variables Into Dummy Variables
    • How To Use ‘map’ Method
    • How To Manipulate With Strings
    • Using Regular Expressions
    • Working With The Vectorized String Functions
    • Practice_Part_05
    • Practice_Part_05 Solution
  • Data Wrangling
    • Theory On Data Wrangling
    • Hierarchical Indexing
    • Hierarchical Indexing Reordering And Sorting
    • Summary Statistics By Level
    • Hierarchical Indexing With DataFrame Columns
    • How To Merge The Pandas Objects
    • Merging On Row Index
    • How To Concatenate Along An Axis
    • How To Combine With Overlap
    • How To Reshape And Pivot Data In Pandas
    • Practice_Part_06
    • Practice_Part_06 Solution
  • Data Grouping And Aggregation
    • Thoery On Data Groupby And Aggregation
    • Groupby Operation
    • How To Iterate Over Groupby Object
    • How To Select Columns In Groupby Method
    • Grouping Using Dictionaries And Series
    • Grouping Using Functions And Index Level
    • Data Aggregation
    • Practice_Part_07
    • Practice_Part_07 Solution
  • Time Series Analysis
    • Theory On Time Series Analysis
    • Introduction To Time Series Data Types
    • How To Convert Between String And Datetime
    • Time Series Basics With Pandas Objects
    • Date Ranges Frequencies And Shifting
    • Date Ranges Frequencies And Shifting Part – 2
    • Time Zone Handling
    • Periods And Period Arithmetic’s
    • Practice_Part_08
    • Practice_Part_08 Solution
  • How To Analyse With The Part of Real Life Projects
    • A Brief Introduction To The Pandas Projects
    • Project_1 Description
    • Project_1 Solution Part – 1
    • Project_1 Solution Part – 2
    • Project_2 Description
    • Project_2 Solution
    • Project_3 Description
    • Project_3 Solution Part – 1
    • Project_3 Solution Part – 2
    • Project Assignment


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