The Ultimate Beginners Guide to Data Analysis with Pandas 
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 5h 51m | 2.36 GB

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

[b]What you'll learn[/b]

[list]
[*]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
[/list]

[b]Requirements[/b]

[list]
[*]Programming logic
[*]Basic Python programming
[/list]

[b]Description[/b]

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:

[list]
[*]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
[/list]

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