Python For Data Science A-Z: EDA With Real Exercises

Python For Data Science A-Z: EDA With Real Exercises, available at $39.99, has an average rating of 4.1, with 103 lectures, based on 1153 reviews, and has 209927 subscribers.

You will learn about 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' This course is ideal for individuals who are Beginner Python developers – Curious to learn about Data Science Or Data Analysis or Data Analysis Beginners or Aspiring data scientists who want to add Python to their tool arsenal or Students and Other Professionals or AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects or Data Analyst job seekers who wants to update their Resume with Python's data analysis toolkit It is particularly useful for Beginner Python developers – Curious to learn about Data Science Or Data Analysis or Data Analysis Beginners or Aspiring data scientists who want to add Python to their tool arsenal or Students and Other Professionals or AI and ML aspirants to upgrade their knowledge in Data Preprocessing before applying the machine learning algorithms to their projects or Data Analyst job seekers who wants to update their Resume with Python's data analysis toolkit.

Enroll now: Python For Data Science A-Z: EDA With Real Exercises

Summary

Title: Python For Data Science A-Z: EDA With Real Exercises

Price: $39.99

Average Rating: 4.1

Number of Lectures: 103

Number of Published Lectures: 103

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 103

Original Price: ₹799

Quality Status: approved

Status: Live

What You Will 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'

Who Should Attend

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

Target Audiences

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

Hi, dear learning aspirants welcome to “Python For Data Science A-Z: EDA With Real Exercises In 2024 ” 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.

Series at a Glance

Series Methods and Handling
Introducing DataFrames
DataFrames More In Depth
Working With Multiple DataFrames
Going MultiDimensional
GroupBy And Aggregates
Reshaping With Pivots
Working With Dates And Time
Regular Expressions And Text Manipulation
Visualizing Data
Data Formats And I/O

Pandas and python go hand-in-hand which is why this bootcamp also includes a Pandas Coding In full length to get you up and running writing pythonic code in no time.

This is the ultimate course on one of the most-valuable skills today. I hope you commit to mastering data analysis with Pandas.

See you inside!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Course Introduction

Lecture 2: How To Get Most Out Of This Course

Lecture 3: Better To Know These Things

Lecture 4: How To Install Python IPython And Jupyter Notebook

Lecture 5: How To Install Anaconda For macOS And Linux Users

Lecture 6: How To Work With The Jupyter Notebook Part-1

Lecture 7: How To Work With The Jupyter Notebook Part-2

Chapter 2: Pandas Building Blocks

Lecture 1: How To Work With The Tabular Data

Lecture 2: How To Read The Documentation In Pandas

Chapter 3: Pandas_Data Structures

Lecture 1: Theory On Pandas Data Structures

Lecture 2: How To Construct The Pandas Series

Lecture 3: How To Construct The DataFrame Objects

Lecture 4: How To Construct The Pandas Index Objects

Lecture 5: Practice Part 01

Lecture 6: Practice Part 01 Solution

Chapter 4: Data Indexing And Selection

Lecture 1: Theory On Data Indexing And Selection

Lecture 2: Data Selection In Series Part 1

Lecture 3: Data Selection In Series Part 2

Lecture 4: Indexers Loc And Iloc In Series

Lecture 5: Data Selection In DataFrame Part 1

Lecture 6: Data Selection In DataFrame Part 2

Lecture 7: Accessing Values Using Loc Iloc And Ix In DataFrame Objects

Lecture 8: Practice Part 02

Lecture 9: Practice Part 02 Solution

Chapter 5: Essential Functionalities

Lecture 1: Theory On Essential Functionalities

Lecture 2: How To Reindex Pandas Objects

Lecture 3: How To Drop Entries From An Axis

Lecture 4: Arithmetic And Data Alignment

Lecture 5: Arithmetic Methods With Fill Values

Lecture 6: Broadcasting In Pandas

Lecture 7: Apply And Applymap In Pandas

Lecture 8: How To Sort And Rank In Pandas

Lecture 9: How To Work With The Duplicated Indices

Lecture 10: Summarising And Computing Descriptive Statistics

Lecture 11: Unique Values Value Counts And Membership

Lecture 12: Practice_Part_03

Lecture 13: Practice_Part_03 Solution

Chapter 6: Data Handling

Lecture 1: Theory On Data Handling

Lecture 2: How To Read The Csv Files Part – 1

Lecture 3: How To Read The Csv Files Part – 2

Lecture 4: How To Read Text Files In Pieces

Lecture 5: How To Export Data In Text Format

Lecture 6: How To Use Python's Csv Module

Lecture 7: Practice_Part_04

Lecture 8: Practice_Part_04 Solution

Chapter 7: Data Cleaning And Preparation

Lecture 1: Theory On Data Preprocessing

Lecture 2: How To Handle Missing Values

Lecture 3: How To Filter The Missing Values

Lecture 4: How To Filter The Missing Values Part 2

Lecture 5: How To Remove Duplicate Rows And Values

Lecture 6: How To Replace The Non Null Values

Lecture 7: How To Rename The Axis Labels

Lecture 8: How To Descretize And Bin The Data Part – 1

Lecture 9: How To Filter And Detect The Outliers

Lecture 10: How To Reorder And Select Randomly

Lecture 11: Converting The Categorical Variables Into Dummy Variables

Lecture 12: How To Use 'map' Method

Lecture 13: How To Manipulate With Strings

Lecture 14: Using Regular Expressions

Lecture 15: Working With The Vectorized String Functions

Lecture 16: Practice_Part_05

Lecture 17: Practice_Part_05 Solution

Chapter 8: Data Wrangling

Lecture 1: Theory On Data Wrangling

Lecture 2: Hierarchical Indexing

Lecture 3: Hierarchical Indexing Reordering And Sorting

Lecture 4: Summary Statistics By Level

Lecture 5: Hierarchical Indexing With DataFrame Columns

Lecture 6: How To Merge The Pandas Objects

Lecture 7: Merging On Row Index

Lecture 8: How To Concatenate Along An Axis

Lecture 9: How To Combine With Overlap

Lecture 10: How To Reshape And Pivot Data In Pandas

Lecture 11: Practice_Part_06

Lecture 12: Practice_Part_06 Solution

Chapter 9: Data Grouping And Aggregation

Lecture 1: Thoery On Data Groupby And Aggregation

Lecture 2: Groupby Operation

Lecture 3: How To Iterate Over Groupby Object

Lecture 4: How To Select Columns In Groupby Method

Lecture 5: Grouping Using Dictionaries And Series

Lecture 6: Grouping Using Functions And Index Level

Lecture 7: Data Aggregation

Lecture 8: Practice_Part_07

Lecture 9: Practice_Part_07 Solution

Chapter 10: Time Series Analysis

Lecture 1: Theory On Time Series Analysis

Lecture 2: Introduction To Time Series Data Types

Lecture 3: How To Convert Between String And Datetime

Lecture 4: Time Series Basics With Pandas Objects

Lecture 5: Date Ranges Frequencies And Shifting

Lecture 6: Date Ranges Frequencies And Shifting Part – 2

Lecture 7: Time Zone Handling

Instructors

Pruthviraja L
Software Trainer and Lead Instructor – Team UpGraduate

Rating Distribution

1 stars: 56 votes
2 stars: 79 votes
3 stars: 208 votes
4 stars: 347 votes
5 stars: 463 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

Menu