Complete Data Analysis And Visualization In Python

Learn Python libraries: NumPy, Pandas, Matplotlib and Seaborn for data analysis and visualization

What you'll learn

Data analysis and visualization skill using Python and its different libraries: NumPy, Pandas, Matplotlib, Seaborn

Different formatting methods for charts

Data processing

Statistical methods for visualization

Requirements

No programming experience needed. You will learn everything you need to know.

Description

In this course, you will learn Python libraries from scratch. So, if you don’t have coding experience, that is very fine.NumPy and Pandas are necessary libraries to do data analysis and preprocessing. In these course, most important concepts will be covered and after completing Pandas lectures, you will do Data Analysis exercise using Pandas for test score dataset. This is important step and aims to polish up your data preprocessing skill.Then, we will learn Matplotlib which is fundamental package for data visualization. In these lectures, we will learn all necessary concepts for data visualization.After, we will dive into Seaborn, statistical package with beautiful charts. First we will explore most important and used charts using Seaborn’s built-in dataset - tips. After completing these lectures, we will dive into full data analysis and visualization exercise using complex datasets.Our first full data analysis exercise will be done using Netflix dataset where you will see how to do complex data preprocessing and applying Matplotlib functions to draw charts on progression and history.For second data analysis, dataset about diamond was used where you will explore Seaborn’s full possibility.After completing this course, you will learn not only how to do everything correct statistically, but also common mistakes people often do during their analysis work.

Overview

Section 1: NumPy and Pandas

Lecture 1 Introduction

Lecture 2 Installation of Anaconda

Lecture 3 NumPy_Lecture_1

Lecture 4 NumPy_Lecture_2

Lecture 5 NumPy_Lecture_3

Lecture 6 NumPy_Lecture_4

Lecture 7 NumPy_Lecture_5

Lecture 8 Pandas_Lecture_1

Lecture 9 Pandas_Lecture_2

Lecture 10 Pandas_Lecture_3

Lecture 11 Pandas_Lecture_4

Lecture 12 Pandas_Lecture_5

Lecture 13 Pandas_Lecture_6

Lecture 14 Pandas_Exercise_1

Lecture 15 Pandas_Exercise_1_Answer

Lecture 16 Pandas_Exercise_2_1

Lecture 17 Pandas_Exercise_2_2

Section 2: Matplotlib

Lecture 18 Matplotlib_Lecture_1

Lecture 19 Matplotlib_Lecture_2

Lecture 20 Matplotlib_Lecture_3

Lecture 21 Matplotlib_Lecture_4

Section 3: Seaborn

Lecture 22 Seaborn_Lecture_1

Lecture 23 Seaborn_Lecture_2

Lecture 24 Seaborn_Lecture_3

Lecture 25 Seaborn_Lecture_4

Lecture 26 Seaborn_Lecture_5

Lecture 27 Seaborn_Lecture_6

Lecture 28 Seaborn_Lecture_7

Lecture 29 Seaborn_Lecture_8

Lecture 30 Seaborn_Lecture_9

Section 4: Data_Analysis_Visualization_1

Lecture 31 Data_Analysis_1

Lecture 32 Data_Analysis_2

Lecture 33 Data_Analysis_3

Section 5: Data_Analysis_Visualization_2

Lecture 34 Data_Analysis_2_1

Lecture 35 Data_Analysis_2_2

Lecture 36 Data_Analysis_2_3

Lecture 37 Data_Analysis_2_4

Lecture 38 Data_Analysis_2_5

Lecture 39 Data_Analysis_2_6

Everyone interested in data analytics and data science,Business Professional interested in data visualization,Data analysis in Python,Data visualization in Python