The Ultimate Beginners Guide to Data Analysis with Pandas

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Jones Granatyr,AI Expert Academy

5:51:22

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1. Introduction

1. Course content.mp4

06:43

2. Course materials.html

2. Series

1.1 Source code - Google Colab.html

1. Installation.mp4

04:45

2. Creating series.mp4

13:14

3. Slicing.mp4

04:32

4. Copy, conversion, and concatenation.mp4

04:39

5. Accessing elements with iloc.mp4

10:52

6. Accessing elements with loc.mp4

11:23

7. Ordering.mp4

03:52

8. Counting.mp4

04:18

9. Filtering.mp4

05:44

10. Mathematical operations.mp4

03:27

11. String operations.mp4

05:51

12. Numerical grouping.mp4

03:37

13. Categorical grouping.mp4

02:39

14. Missing values.mp4

06:46

15. Functions.mp4

08:03

16.1 Homework - solved.html

16.2 Homework - unsolved.html

16. HOMEWORK.mp4

03:07

17. Homework solution.mp4

08:06

3. Dataframes

1.1 Source code - Google Colab.html

1. Creating dataframes.mp4

07:54

2. Exploring dataframes.mp4

06:48

3. Accessing elements with iloc and loc.mp4

07:21

4. Deleting rows and columns.mp4

05:10

5. Duplicated rows.mp4

05:20

6. Missing values.mp4

11:31

7. Counting.mp4

11:31

8. Ordering.mp4

04:54

9. Filtering.mp4

06:50

10. Rename and reorder columns.mp4

05:23

11. Creating new columns.mp4

10:19

12. Categorical features.mp4

03:55

13. Aggregation.mp4

03:51

14. Grouping.mp4

12:36

15. Grouping with aggregation.mp4

07:34

16. Aggregation with transform.mp4

04:03

17. Pivot tables.mp4

07:15

18. Concatenation and joining.mp4

14:11

19. Date conversions.mp4

11:43

20. Date indexes.mp4

06:34

21. Importation and exportation.mp4

20:18

22.1 Homework - solved.html

22.2 Homework - unsolved.html

22. HOMEWORK.mp4

03:53

23. Homework solution.mp4

14:37

4. Data visualization

1.1 Source code - Google Colab.html

1. Line plot.mp4

04:28

2. Formatting.mp4

06:11

3. Subplots.mp4

04:53

4. Bar and pizza plots.mp4

11:04

5. Scatter plot.mp4

06:52

6. Histogram.mp4

05:21

7.1 Homework - solved.html

7.2 Homework - unsolved.html

7. HOMEWORK.mp4

01:18

8. Homework solution.mp4

03:29

5. Final remarks

1. Final remarks.mp4

01:05

2. BONUS.mp4

01:32

Description

Python for Data Science: Develop essential skills with Pandas, with practical exercises solved step by step

What You'll Learn?

Create, slice, and manipulate Series in Pandas, exploring from basic operations to grouping
Develop advanced skills in creating and manipulating DataFrames, mastering techniques for accessing and performing complex operations
Visualize data, create plots, and explore essential formatting techniques
Put your knowledge to the test with practical challenges, strengthening your skills in data manipulation and analysis
Explore the power of grouping in numerical and categorical data, as well as perform advanced operations for more sophisticated analyses

Who is this for?

Individuals who are taking their first steps in Python programming and wish to delve into the world of data analysis in a practical manner

Students or early-career professionals in the field of data science seeking a solid understanding of data manipulation with Pandas

Professionals who already have basic knowledge in Python and want to enhance their skills in data manipulation and analysis using Pandas

Students looking for a practical introduction to data manipulation to complement their studies in statistics or related disciplines

Developers aiming to expand their skills to include data analysis, using Pandas as an essential tool in their projects

What You Need to Know?

Programming logic

Basic Python programming

More details

Description
Welcome to the "Ultimate Beginners Guide to Pandas for Data Analysis" course, a comprehensive journey designed for beginners interested in exploring the Pandas library in the context of data analysis. This course has been carefully structured to provide a solid understanding of Pandas fundamentals and advanced techniques, empowering students to manipulate data with confidence and efficiency. Check out the modules and main topics below:
Section 1: Series
We start with Pandas installation and the creation of Series, the essential one-dimensional structure for storing data. Throughout the module, we explore fundamental concepts such as slicing, copying, accessing with iloc and loc, sorting, filtering, mathematical operations, and string manipulations. We also cover advanced topics, including numerical and categorical grouping, handling missing values, functions, and practical challenges.
Section 2: Dataframe
Continuing on, we delve into the creation and exploration of Dataframes, vital structures for analyzing more complex datasets. This module covers topics such as accessing with iloc and loc, manipulation of rows and columns, handling duplicate data and missing values, sorting, advanced filtering, creating and manipulating columns, aggregation, pivot tables, concatenation, joining, and import/export techniques. We include practical challenges to reinforce learning.
Section 3: Data Visualization
In the final module, we explore data visualization with Pandas. We cover the creation of line, bar, pie, scatter, and histogram plots, as well as formatting techniques and subplots. The module includes a practical challenge to apply the newly acquired skills in visualizing data.
Upon completing this course, participants will be equipped with the practical skills necessary to effectively use Pandas in data analysis. Get ready for an hands-on learning experience, empowering you to tackle real-world challenges in data manipulation and interpretation.
Who this course is for:
Individuals who are taking their first steps in Python programming and wish to delve into the world of data analysis in a practical manner
Students or early-career professionals in the field of data science seeking a solid understanding of data manipulation with Pandas
Professionals who already have basic knowledge in Python and want to enhance their skills in data manipulation and analysis using Pandas
Students looking for a practical introduction to data manipulation to complement their studies in statistics or related disciplines
Developers aiming to expand their skills to include data analysis, using Pandas as an essential tool in their projects

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Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.

AI Expert Academy

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We are an on-line platform focused on courses on Artificial Intelligence, Machine Learning and Data Science. Our goal is to offer easy-to-understand theoretical and practical content, so that professionals from all areas can understand the benefits that AI can bring to their businesses. We are established in Brazil since 2018 and we have already published more than 90 courses in English and Portuguese on the Udemy platform.

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