Data Analysis Crash Course For Beginners (Pandas + Python)

Take First Step Toward Data Analysis With Pandas - Learn about DataFrames, Jupyter Notebook, iPython and Pandas Commands

What you'll learn
Fundamentals of Data Analysis.
Working with Pandas, iPython, Jupyter Notebook.
Important Jupyter Notebook Commands.
Working with CSV, Excel, TXT, JSON Files and API Responses.
Working with DataFrames (Indexing, Slicing, Adding and Deleting).
Requirements
Basics of Python
Description
Welcome to Data Analysis Basics with Pandas and Python - For Beginners,This course will help you to understand the fundamentals of Data Analysis with Python and Pandas library. You will learn,1. Fundamentals of Data Analysis.2. Working with Pandas, iPython, Jupyter Notebook.3. Important Jupyter Notebook Commands.4. Working with CSV, Excel, TXT, JSON Files and API Responses. 5. Working with DataFrames (Indexing, Slicing, Adding and Deleting).Pandas is an open-source library providing high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modelling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.After completing this course you will have a good understanding of Pandas and will be ready to explore Data Analysis in-depth in future.

Overview

Section 1: Course Introduction

Lecture 1 Course Introduction

Lecture 2 Welcome - Lets Get Started!

Section 2: What is Pandas?

Lecture 3 What is Pandas?

Lecture 4 Starting With Pandas And iPython

Section 3: Jupyter Notebooks

Lecture 5 Working with Jupyter Notebooks

Lecture 6 Important Jupyter Notebook Commands

Section 4: Working on Data

Lecture 7 Working with CSV, Excel, TXT and JSON Files

Lecture 8 Working with API Response

Lecture 9 Indexing and Slicing Dataframe Tables [Part 1]

Lecture 10 Indexing and Slicing Dataframe Tables [Part 2]

Lecture 11 Deleting Columns and Rows

Lecture 12 Adding and Updating new Columns and Rows

Section 5: Thank You For Being Here!

Lecture 13 Thank You For Being Here!

Python Programmers and Developers,Student interested in learning Pandas