The Complete Data Analysis and Visualization in Python 2023

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Gulmira Iskendirova

3:34:04

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1. NumPy and Pandas

1. Introduction.mp4

01:45

2. Installation of Anaconda.mp4

01:49

3. NumPy Lecture 1.mp4

06:19

4. NumPy Lecture 2.mp4

05:54

5. NumPy Lecture 3.mp4

08:46

6. NumPy Lecture 4.mp4

06:41

7.1 numpy lecture.zip

7. NumPy Lecture 5.mp4

06:24

8.1 pandas lecture 1.zip

8.2 pandas lecture 2.zip

8. Pandas Lecture 1.mp4

05:39

9. Pandas Lecture 2.mp4

05:32

10. Pandas Lecture 3.mp4

05:03

11. Pandas Lecture 4.mp4

06:28

12. Pandas Lecture 5.mp4

05:51

13. Pandas Lecture 6.mp4

04:29

14.1 Expanded data with more features.csv

14.2 pandas exercise 1 answer.zip

14.3 pandas exercise 1.zip

14. Pandas Exercise 1.mp4

02:27

15. Pandas Exercise 1 Answer.mp4

04:24

16.1 pandas exercise 2 (important).zip

16. Pandas Exercise 2 1.mp4

06:25

17. Pandas Exercise 2 2.mp4

04:26

2. Matplotlib

1.1 matplotlib.zip

1. Matplotlib Lecture 1.mp4

03:40

2. Matplotlib Lecture 2.mp4

04:43

3. Matplotlib Lecture 3.mp4

05:33

4. Matplotlib Lecture 4.mp4

05:33

3. Seaborn

1.1 seaborn.zip

1. Seaborn Lecture 1.mp4

03:10

2. Seaborn Lecture 2.mp4

04:47

3. Seaborn Lecture 3.mp4

03:11

4. Seaborn Lecture 4.mp4

07:48

5. Seaborn Lecture 5.mp4

07:21

6. Seaborn Lecture 6.mp4

03:38

7. Seaborn Lecture 7.mp4

05:52

8. Seaborn Lecture 8.mp4

03:15

9. Seaborn Lecture 9.mp4

04:57

4. Data_Analysis_Visualization_1

1.1 data analysis exercise 1.zip

1.2 netfix cleaned.csv

1. Data Analysis 1.mp4

06:37

2. Data Analysis 2.mp4

07:28

3. Data Analysis 3.mp4

03:43

5. Data_Analysis_Visualization_2

1.1 data analysis exercise 2.zip

1.2 diamonds.csv

1. Data Analysis 2 1.mp4

07:56

2. Data Analysis 2 2.mp4

07:17

3. Data Analysis 2 3.mp4

08:10

4. Data Analysis 2 4.mp4

04:52

5. Data Analysis 2 5.mp4

10:28

6.1 pie chart.zip

6. Data Analysis 2 6.mp4

05:43

Description

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

What You'll Learn?

Python's different libraries: NumPy, Pandas, Matplotlib, Seaborn
scatter plot, bar plot, lmplot, lineplot, displot, boxplot, violinplot, pie chart and many others
Data preprocessing using Pandas
Jupyter Notebook
Seaborn
Reviewing basic statistics
Exploratory Data Analysis
Data Analysis on Netflix dataset, Diamond dataset, Test Score dataset

Who is this for?

Everyone interested in data analytics and data science

Business Professional interested in data visualization

Data analysis in Python

Data visualization in Python

Learning Pandas from scratch

Preprocessing of data

Learning Seaborn

What You Need to Know?

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

More details

Description
In this course, you will learn Python libraries from scratch. So, if you donâ€™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 Seabornâ€™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 Seabornâ€™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.

Who this course is for:
Everyone interested in data analytics and data science
Business Professional interested in data visualization
Data analysis in Python
Data visualization in Python
Learning Pandas from scratch
Preprocessing of data
Learning Seaborn

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Hello, My name is Gulmira. I graduated from Tokyo Institute of Technology with Master of Engineering in Materials Science where I worked to make new generation of polymers with thermal conductivity properties. Have conference paper publications in these field. I am very excited about data analysis and machine learning as it allows to harvest all these hidden insights in data and get ahead in chosen areas. I am teaching Python and Machine Learning for working professionals for almost 3 years and quite familiar with needs and struggles of these people. And, would be happy to share with you all these knowledge.

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