Master Pandas For Data Handling

Learn to Master the worlds most powerful software for Advanced Data Handling

What you’ll learn
Master the Pandas library for advanced Data Handling
The fundamental concepts and language of the Pandas DataFrame object
All aspects of changing, modifying and selecting Data from a Pandas DataFrame
File handling with the Pandas library
Use the .concat(), .join(), and .merge() functions/methods to combine Pandas DataFrame objects
Scale and Standardize data
Advanced Data Preparation with Pandas, including model-based imputation of missing data
Make advanced Data Descriptions with Pandas, including cross-tabulations, groupings, and descriptive statistics
Make Data Visualizations with Pandas, Matplotlib, and Seaborn
Cloud Computing – use Anaconda Cloud Notebook (Jupyter Notebook). Learn to use Cloud Computing resources
Optional: use Anaconda Distribution’s Jupyter Notebook and Conda package management system

Requirements
Everyday experience using a computer with Windows, MacOS, Ios, Android, ChromeOS, or Linux is recommended
Basic Python knowledge is recommended
Access to a computer with an internet connection
The course only uses costless software
Walk-you-through installation and setup videos for Windows 10/11 is included

Description
This video course will teach you to master Pandas, the most powerful, efficient, and useful Data Handling library in existence. You will learn to master the Pandas library and to use powerful Data Handling techniques with the intention of making you able to use the powerful Pandas library for Data Science and Machine Learning Data Handling tasks.With the Pandas library you get a fast, powerful, flexible and easy to use, open-source data analysis and data manipulation tool, directly usable with the Python programming language and able to use any data source with the incredibly powerful Pandas DataFrame object.This video course is updated to Pandas 2 and the announced upcoming Pandas 3 version.You will learn:Master the Pandas library for advanced Data HandlingThe fundamental concepts and language of the Pandas DataFrame objectAll aspects of changing, modifying and selecting Data from a Pandas DataFrameFile handling with the Pandas libraryUse the .concat(), .join(), and .merge() functions/methods to combine Pandas DataFrame objectsScale and Standardize dataAdvanced Data Preparation with Pandas, including model-based imputation of missing dataMake advanced Data Descriptions with Pandas, including cross-tabulations, groupings, and descriptive statisticsMake Data Visualizations with Pandas, Matplotlib, and SeabornCloud Computing: To use the web browser-based Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud Computing resources in this course.Option: To use the Anaconda Distribution (Windows, Mac, Linux, and more)Option: Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages – golden nuggets to improve your quality of work life.And much more…This course is an excellent way to learn to Master Pandas and Data Handling! Data Handling is the process of making data useful and usable for data analysis. Most Data Scientists and Machine Learners spends about 80% of their working efforts and time on Data Handling tasks. Being good at Data Handling and Pandas is extremely useful and time-saving skills that functions as a force multiplier for productivity.This course is designed for anyone who wants toAnyone who knows the basics of Python programming and want to learn the Pandas library!Anyone who is a new student at the University level and want to learn Data Handling skills that they will have use for in their entire data science, engineering or academic careers!Anyone who knows Python and wants to extend your knowledge of the Pandas library and Data Handling!Anyone who knows about Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know!Anyone who wants to learn advanced Data Handling and improve their capabilities and productivityRequirements:Everyday experience using a computer with Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedBasic Python knowledge is recommendedAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Windows 10/11 is includedThis course is the course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to Master Pandas and Data Handling.Enroll now to receive 12+ hours of detailed video tutorials with manually edited English captions, and a certificate of completion after completing the course!

Overview
Section 1: Introduction

Lecture 1 Introduction to Master Pandas for Data Handling

Lecture 2 Setup of the Anaconda Cloud Notebook

Lecture 3 Download and installation of the Anaconda Distribution (optional)

Lecture 4 The Conda Package Management System (optional)

Section 2: Master Pandas for Data Handling

Lecture 5 Master Pandas for Data Handling: Overview

Lecture 6 Pandas theory and terminology

Lecture 7 Creating a DataFrame from scratch

Lecture 8 Pandas File Handling: Overview

Lecture 9 Pandas File Handling: The .csv file format

Lecture 10 Pandas File Handling: The .xlsx file format

Lecture 11 Pandas File Handling: SQL-database files and Pandas DataFrame

Lecture 12 Pandas Operations & Techniques: Overview

Lecture 13 Pandas Operations & Techniques: Object Inspection

Lecture 14 Pandas Operations & Techniques: DataFrame Inspection

Lecture 15 Pandas Operations & Techniques: Column Selections

Lecture 16 Pandas Operations & Techniques: Row Selections

Lecture 17 Pandas Operations & Techniques: Conditional Selections

Lecture 18 Pandas Operations & Techniques: Scalers and Standardization

Lecture 19 Pandas Operations & Techniques: Concatenate DataFrames

Lecture 20 Pandas Operations & Techniques: Joining DataFrames

Lecture 21 Pandas Operations & Techniques: Merging DataFrames

Lecture 22 Pandas Data Preparation I: Overview & workflow

Lecture 23 Pandas Data Preparation II: Edit DataFrame labels

Lecture 24 Pandas Data Preparation III: Duplicates

Lecture 25 Pandas Data Preparation IV: Missing Data & Imputation

Lecture 26 Pandas Data Description I: Overview

Lecture 27 Pandas Data Description II: Sorting and Ranking

Lecture 28 Pandas Data Description III: Descriptive Statistics

Lecture 29 Pandas Data Description IV: Crosstabulations & Groupings

Lecture 30 Pandas Data Visualization I: Overview

Lecture 31 Pandas Data Visualization II: Histograms

Lecture 32 Pandas Data Visualization III: Boxplots

Lecture 33 Pandas Data Visualization IV: Scatterplots

Lecture 34 Pandas Data Visualization V: Pie Charts

Lecture 35 Pandas Data Visualization VI: Line plots

Anyone who knows the basics of Python programming and want to learn the Pandas library,Anyone who is a new student at the University level and want to learn Data Handling skills that they will have use for in their entire data science, engineering or academic careers,Anyone who knows Python and wants to extend your knowledge of the Pandas library and Data Handling,Anyone who knows about Data Science or Machine Learning and want to learn Data Handling skills that work as a force multiplier with the skills you already know,Anyone who wants to learn advanced Data Handling and improve their capabilities and productivity