• Understand the basics of Pandas, its data structures, and how to install it.
• Work with different types of data structures in Pandas.
• Use descriptive and inferential statistics methods to analyze data.
• Apply element-wise, row or column-wise, and table-wise function application on data.
• Reindex, sort, and iterate through data using Pandas.
• Use string methods for data cleaning and manipulation.
• Customize display options and data types in Pandas.
• Perform indexing and selecting operations based on labels, integers, or Boolean values.
• Use window functions such as rolling, expanding, and ewm for data analysis.
• Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.
• Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.
• Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.
• Read and write data in different formats such as CSV, Excel, and JSON using Pandas.
• Work with sparse data and understand its features.

Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3

The “Introduction to The Pandas Bootcamp | Data Analysis with Pandas Python3” course is designed for anyone who wants to learn how to use Pandas, the popular data manipulation library for Python.

This course covers a wide range of topics, from the basics of Pandas installation and data structures to more advanced topics such as window functions and visualization.

Whether you are a beginner or an experienced programmer, this course will provide you with a comprehensive understanding of how to use Pandas to analyze and manipulate data efficiently.

Through practical programming examples, you will learn how to perform data cleaning and manipulation, aggregation, and grouping, as well as how to work with different data formats such as CSV, Excel, and JSON. By the end of the course, you will have gained the knowledge and skills necessary to work with large datasets and perform complex data analysis tasks using Pandas.

Instructors Experiences and Education:

Faisal Zamir is an experienced programmer and an expert in the field of computer science. He holds a Master’s degree in Computer Science and has over 7 years of experience working in schools, colleges, and university. Faisal is a highly skilled instructor who is passionate about teaching and mentoring students in the field of computer science.

As a programmer, Faisal has worked on various projects and has experience in multiple programming languages, including PHP, Java, and Python.

He has also worked on projects involving web development, software engineering, and database management. This broad range of experience has allowed Faisal to develop a deep understanding of the fundamentals of programming and the ability to teach complex concepts in an easy-to-understand manner.

As an instructor, Faisal has a proven track record of success. He has taught students of all levels, from beginners to advanced, and has a passion for helping students achieve their goals.

Faisal has a unique teaching style that combines theory with practical examples, which allows students to apply what they have learned in real-world scenarios.

Overall, Faisal Zamir is a skilled programmer and a talented instructor who is dedicated to helping students achieve their goals in the field of computer science. With his extensive experience and proven track record of success, students can trust that they are learning from an expert in the field.

What you will learn from  Course Data Analysis with Pandas Python3

1. Understand the basics of Pandas, its data structures, and how to install it.
2. Work with different types of data structures in Pandas.
3. Use descriptive and inferential statistics methods to analyze data.
4. Apply element-wise, row or column-wise, and table-wise function application on data.
5. Reindex, sort, and iterate through data using Pandas.
6. Use string methods for data cleaning and manipulation.
7. Customize display options and data types in Pandas.
8. Perform indexing and selecting operations based on labels, integers, or Boolean values.
9. Use window functions such as rolling, expanding, and ewm for data analysis.
10. Group data based on single or multiple columns, apply aggregation functions, and filter or transform data.
11. Work with categorical data, perform methods such as reorder, remove, add, and rename categories, and visualize categorical data using Pandas.
12. Visualize data using different types of plots such as line, bar, histogram, scatter, box, area, and heatmap.
13. Read and write data in different formats such as CSV, Excel, and JSON using Pandas.
14. Work with sparse data and understand its features.

Outlines for Pandas Course for Data Science

Chapter 01

• Introduction
• What is Pandas
• Why need of Pandas
• What we can do with Pandas
• Pandas Installation
• Pandas Basic Program

Chapter 02

• Data Structures
• Types of Data Structure

Chapter 03

• Series
• Series different OperationS
• Series Attributes
• Series methods
• DataFrame
• Panel

Chapter 04

• DataFrame
• DataFrame different OperationS
• DataFrame Attributes
• DataFrame methods
• Panel

Chapter 05

• Descriptive Statistics
• Descriptive Statistics Methods & Programming Examples
• Inferential statistics functions

Chapter 06

• Function Application
• Element-wise
• Row or Column-wise
• Table wise

Chapter 07

• Reindexing
• Reindexing Method with Programming Examples
• Iteration
• Iteration Method with Programming Examples
• Sorting
• Sorting Method with Programming Examples

Chapter 08

• String Methods
• lower
• upper
• title
• capitalize
• swapcase
• strip
• lstrip
• rstrip
• split
• rsplit
• join
• replace
• contains
• startswith
• endswith
• find
• rfind
• count
• len

Chapter 09

• Customization Options
• Customizing display options
• Customizing data types
• Customizing data cleaning and manipulation
• Indexing & Selecting
• Label-based or integer-based indexing
• Boolean indexing
• Based on a string (.query)

Chapter 10

• Window Function
• Rolling window
• Expanding window
• Exponentially Weighted window
• Weighted window

Chapter 11

Groupby operations

• Splitting Data
• Appling function on that data
• Combining the results

Operations on subset data

• Aggregation
• Transformation
• Filtration

Chapter 12

• Categorical Data
• Benefits
• Purpose
• Methods used in Categorial Data
• astype
• value_counts
• unique
• reorder_categories
• set_categories
• remove categories
• add categories
• rename categories
• remove unused categories

Chapter 13

• Visualization
• Line plot
• Bar plot
• Histogram
• Scatter plot
• Box plot
• Area plot
• Heatmap
• Density plot

Chapter 14

• I/O Tools
• Reading CSV
• Writing CSV
• Reading Excel
• Writing CSV
• Reading JSON
• Writing CSV

Chapter 16

• Date Time Functions
• to datetime
• date range
• strftime
• Timestamp

30-day money-back guarantee for The Pandas Bootcamp | Data Analysis with Pandas Python3

We are confident that The Pandas Bootcamp | Data Analysis with Pandas Python3 course will provide you with the skills and knowledge needed for successful data analysis using Pandas.

That’s why we offer a 30-day money-back guarantee, giving you peace of mind as you embark on this learning journey.

With our expert instructors and a comprehensive curriculum, you’ll gain a solid understanding of data structures, descriptive statistics, function applications, customization options, and more.

Our course is designed for anyone looking to enhance their data analysis skills, including students, data analysts, business professionals, and aspiring data scientists. Join us today and take the first step towards becoming a proficient Pandas user!

Thank you

Faisal Zamir

Who this course is for:

• Aspiring data analysts who want to learn how to use Pandas for data analysis
• Data scientists who want to add Pandas to their skillset
• Business analysts who need to analyze data using Pandas
• Programmers who want to learn about data manipulation and analysis using Python and Pandas
• Anyone interested in learning about Pandas and data analysis with Python