4.3 out of 5
4.3
276 reviews on Udemy

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 !
Instructor:
Pruthviraja L
75,894 students enrolled
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'

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.

Getting Started

1
Course Introduction
2
How To Get Most Out Of This Course
3
Better To Know These Things
4
How To Install Python IPython And Jupyter Notebook
5
How To Install Anaconda For macOS And Linux Users
6
How To Work With The Jupyter Notebook Part-1
7
How To Work With The Jupyter Notebook Part-2

Pandas Building Blocks

1
How To Work With The Tabular Data
2
How To Read The Documentation In Pandas

Pandas_Data Structures

1
Theory On Pandas Data Structures
2
How To Construct The Pandas Series
3
How To Construct The DataFrame Objects
4
How To Construct The Pandas Index Objects
5
Practice Part 01
6
Practice Part 01 Solution

Data Indexing And Selection

1
Theory On Data Indexing And Selection
2
Data Selection In Series Part 1
3
Data Selection In Series Part 2
4
Indexers Loc And Iloc In Series
5
Data Selection In DataFrame Part 1
6
Data Selection In DataFrame Part 2
7
Accessing Values Using Loc Iloc And Ix In DataFrame Objects
8
Practice Part 02
9
Practice Part 02 Solution

Essential Functionalities

1
Theory On Essential Functionalities
2
How To Reindex Pandas Objects
3
How To Drop Entries From An Axis
4
Arithmetic And Data Alignment
5
Arithmetic Methods With Fill Values
6
Broadcasting In Pandas
7
Apply And Applymap In Pandas
8
How To Sort And Rank In Pandas
9
How To Work With The Duplicated Indices
10
Summarising And Computing Descriptive Statistics
11
Unique Values Value Counts And Membership
12
Practice_Part_03
13
Practice_Part_03 Solution

Data Handling

1
Theory On Data Handling
2
How To Read The Csv Files Part - 1
3
How To Read The Csv Files Part - 2
4
How To Read Text Files In Pieces
5
How To Export Data In Text Format
6
How To Use Python's Csv Module
7
Practice_Part_04
8
Practice_Part_04 Solution

Data Cleaning And Preparation

1
Theory On Data Preprocessing
2
How To Handle Missing Values
3
How To Filter The Missing Values
4
How To Filter The Missing Values Part 2
5
How To Remove Duplicate Rows And Values
6
How To Replace The Non Null Values
7
How To Rename The Axis Labels
8
How To Descretize And Bin The Data Part - 1
9
How To Filter And Detect The Outliers
10
How To Reorder And Select Randomly
11
Converting The Categorical Variables Into Dummy Variables
12
How To Use 'map' Method
13
How To Manipulate With Strings
14
Using Regular Expressions
15
Working With The Vectorized String Functions
16
Practice_Part_05
17
Practice_Part_05 Solution

Data Wrangling

1
Theory On Data Wrangling
2
Hierarchical Indexing
3
Hierarchical Indexing Reordering And Sorting
4
Summary Statistics By Level
5
Hierarchical Indexing With DataFrame Columns
6
How To Merge The Pandas Objects
7
Merging On Row Index
8
How To Concatenate Along An Axis
9
How To Combine With Overlap
10
How To Reshape And Pivot Data In Pandas
11
Practice_Part_06
12
Practice_Part_06 Solution

Data Grouping And Aggregation

1
Thoery On Data Groupby And Aggregation
2
Groupby Operation
3
How To Iterate Over Groupby Object
4
How To Select Columns In Groupby Method
5
Grouping Using Dictionaries And Series
6
Grouping Using Functions And Index Level
7
Data Aggregation
8
Practice_Part_07
9
Practice_Part_07 Solution

Time Series Analysis

1
Theory On Time Series Analysis
2
Introduction To Time Series Data Types
3
How To Convert Between String And Datetime
4
Time Series Basics With Pandas Objects
5
Date Ranges Frequencies And Shifting
6
Date Ranges Frequencies And Shifting Part - 2
7
Time Zone Handling
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16 hours on-demand video
1 article
Full lifetime access
Access on mobile and TV
Certificate of Completion

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