10 Best Pandas Courses and Certifications Online

Pandas is a popular data manipulation and analysis library in the Python programming language. As such, there has been a growing demand for quality online courses that cater to beginners and advanced learners alike. In this article, we will explore some of the best online Pandas courses available and evaluate their features, content, and overall value for learners looking to improve their skills in Pandas.

Here’s a look at the Best Pandas Courses and Certifications Online and what they have to offer for you!

10 Best Pandas Courses and Certifications Online

1. Data Analysis with Pandas and Python by Boris Paskhaver (Udemy) (Our Best Pick)

The “Data Analysis with Pandas and Python” course is designed to teach students how to analyze data quickly and easily using Python’s pandas library. The course is suitable for both beginners and experts and offers 19+ hours of in-depth video tutorials on various data analysis methods. The course covers installing, sorting, filtering, grouping, aggregating, de-duplicating, pivoting, munging, deleting, merging, visualizing, and more.

Pandas is a powerful tool built on top of the Python programming language that allows users to analyze, organize, sort, filter, pivot, aggregate, munge, clean, calculate and more with colossal data sets. The course introduces learners to Pandas and takes them through its various functionalities and features over the course of more than 19 hours. The course is bundled with dozens of datasets to enable students to follow along easily.

The course starts with installation and setup and includes a bonus Python crash course. It then covers various topics like series, data frames, filtering data, data extraction, working with text data, multi-index, the groupby object, merging, joining, and concatenating data frames, working with dates and times in datasets, input and output in pandas, visualization, options and settings in pandas, and conclusion.

The course has received overwhelmingly positive reviews from students. They have praised the instructor for his detailed explanations, logical approach, and clarity. The lessons are well-constructed, and the instruction is excellent. Some students have called it the best Udemy course they have ever taken. The course is highly recommended to anyone wanting to learn pandas, whether a new data analyst or someone who has spent years in Excel.

2. Data Manipulation in Python: A Pandas Crash Course by Samuel Hinton, Ligency I Team, Ligency Team (Udemy)

This course, titled “Data Manipulation in Python: A Pandas Crash Course”, is designed to teach individuals how to use Python and Pandas for analyzing and manipulating data. With data manipulation accounting for up to 80% of a data scientist’s work, this course aims to teach participants advanced data munging techniques to turn raw data into a final product for analysis quickly and efficiently. The course instructor, Ph.D. Samuel Hinton, provides a comprehensive curriculum that covers basic and advanced Pandas data manipulation techniques, loading and creating Pandas DataFrames, displaying data with basic plots and visualizations, and more. Participants will also receive a cheatsheet and practical exercises to gain hands-on experience.

Pandas, the most popular Python library in data science, is used by data scientists at major companies like Google, Facebook, and JP Morgan. However, it has a steep learning curve and inadequate documentation when it comes to advanced functions, making it difficult for users to grasp complex techniques. This course, taught by an experienced instructor, aims to guide beginners and intermediate users through every aspect of Pandas with ease.

The course curriculum covers topics like basic and advanced DataFrame manipulations, multiIndexing, stacking, hierarchical indexing, pivoting, melting, and more. Participants will also learn how to perform grouping, aggregation, imputation, and time series manipulations. With this course, individuals will learn how to efficiently utilize Pandas to manipulate, transform, pivot, stack, merge, and aggregate data for visualization, statistical analysis, or machine learning.

Upon completing the course, individuals will feel confident in analyzing complex and heterogeneous datasets and producing useful results for the next stage of data analysis. The course provides a practical approach with real-life examples of data manipulation techniques, which enables participants to gain hands-on experience.

3. The Ultimate Pandas Bootcamp: Advanced Python Data Analysis by Andy Bek (Udemy)

The Ultimate Pandas Bootcamp is an advanced Python data analysis course taught by instructor Andy Bek. This course focuses on mastering the pandas library, which allows users to analyze, manipulate, and visualize data. The course covers over 32 hours of content, including more than 10 datasets and 50+ skill challenges.

The curriculum is designed to provide hands-on mastery of pandas 1.x, as well as tens of computer science, statistics, and programming concepts. The course includes pandorable and pythonic solutions to real-world data problems and diverse datasets such as wine servings, video game sales, SAT scores, stock prices, and college salaries.

Each section includes dedicated skill challenges that allow users to practice what they’ve learned and compare alternative solutions. Data analysis is a highly sought-after skill in all industries, and pandas is the de facto library for data analysis in the Python data science community.

The course curriculum is broken down into 12 sections that cover everything pandas has to offer, including manipulating series and dataframes, merging datasets, handling time series, aggregations, filtering, sorting, and much more. The course also includes a full-length introduction to the Python programming language.

Overall, The Ultimate Pandas Bootcamp is a comprehensive course that teaches advanced data analysis skills using pandas and Python. It is designed for individuals who want to master data analysis and improve their understanding of computer science, statistics, and programming concepts.

4. Pandas Masterclass 2022: Advanced Data Analysis with Pandas by Data Is Good Academy (Udemy)

The Pandas Masterclass 2022: Advanced Data Analysis with Pandas course is offered by Data Is Good Academy for individuals interested in mastering the Pandas library for data analysis. Pandas is an open-source data analysis and data manipulation library built on top of the Python programming language, offering data structures and many operations for manipulating data. The course covers various topics such as handling series and data frames, working with arrays, time data, index objects, window functions, styling data frames, and visualization and plotting using Pandas.

The course is aimed at data analysts and data scientists working on any project and is suitable for individuals who are learning Python and data science and are looking for new challenges. Upon completion of the course, individuals will have comprehensive knowledge of the Pandas library and will be able to efficiently work on any data analysis project using Pandas.

The course includes instructor support for any queries and provides access to all the resources used in the course. The course is structured into several sections, including an introduction to Pandas, general functions in Pandas, handling series and data frames, working with arrays, time data, index objects, window functions, styling data frames, visualization and plotting, an outro section, and a bonus section. Enrolling in this course will enable individuals to become a Pandas pro and gain the necessary skills to analyze, manipulate, and visualize big data.

5. The Complete Pandas Bootcamp 2022: Data Science with Python by Alexander Hagmann (Udemy)

The Complete Pandas Bootcamp 2022: Data Science with Python is a comprehensive course with 34 hours of video content, 150+ exercises, and two large final projects. The course is designed to bring data handling skills to the next level for those interested in pursuing a career in Data Science, Machine Learning, Finance, and related fields. The course is divided into five parts, covering Pandas Basics, the complete data workflow with Pandas, two comprehensive project challenges, Pandas for Finance and Investing, and Machine Learning with Pandas and scikit-learn.

The Pandas library is an essential tool for Python Data Science, enabling users to import, clean, join, manipulate, and prepare data for further statistical analysis, machine learning, or data presentation. Data scientists typically spend up to 85% of their time manipulating data in Pandas. Python beginners can start working with Pandas without becoming an expert in Python coding. This course covers fundamental statistical concepts and provides a Python crash course tailored for data science purposes.

This course is the most relevant and comprehensive course on Pandas and covers all relevant methods, attributes, and workflows for real-world projects. It is also the most up-to-date course and covers Pandas Version 1.x, which has experienced massive improvements in recent months. The course covers other libraries used in conjunction with Pandas, including Matplotlib and Seaborn for data visualization, Numpy, Scipy, and Scikit-Learn for machine learning, scientific, and statistical computing.

In addition to in-depth Pandas coding, the course emphasizes big-picture thinking and covers both coding and the business side of things. It also provides real-world data examples and teaches common mistakes and errors to avoid. Exercises are provided with optional hints and guidance/instruction, allowing learners to practice and code on their own.

6. Python Pandas Library Full Tutorial by Diptam Paul (Udemy)

Course Title: Python Pandas Library Full Tutorial

Course Instructors: Diptam Paul

Course Short Description: This course provides an in-depth understanding of pandas, a software library written for the Python programming language, designed for data manipulation and analysis. It offers efficient data structures and operations for manipulating numerical tables and time series. Basic knowledge of NumPy is required to perform tasks using NumPy.

Course Long Description: The pandas library is a popular tool in the data science community for data analysis and manipulation. The library can handle various types of data, including tabular data, time-series data, and data with missing values. This comprehensive course aims to provide an in-depth understanding of pandas, from the basics to the advanced level.

The course is divided into three sections: Introduction, Intermediate Level, and Miscellaneous. The Introduction section covers the basics of pandas, including its data structures, functions, and operations. The Intermediate Level section delves deeper into data analysis and manipulation using pandas, including data cleaning, merging, and visualization. The Miscellaneous section covers some additional topics, such as working with dates and times, grouping data, and handling missing data.

Throughout the course, Jupyter Notebook is used to write all the codings. However, if you do not have Jupyter Notebook or do not know how to use it, you can simply run these codes in any IDE or even in Python Default IDLE.

In summary, this course provides a comprehensive understanding of pandas and its various functionalities. It is suitable for anyone interested in data analysis and manipulation using Python.

7. Complete Data Analysis with Pandas : Hands-on Pandas Python by Ankit Mistry, Data Science & Machine Learning Academy (Udemy)

The Complete Data Analysis with Pandas course is taught by Ankit Mistry, who is associated with the Data Science & Machine Learning Academy. The course focuses on teaching in-demand skills like Pandas, Sci-kit Learn, and Numpy for Data Science and Machine Learning. The course has been highly rated by over 40,000 students who have already enrolled in it. The course covers concepts like exploratory data analysis, data transformation, data wrangling, time-series data analysis, analysis through visualization, and many more. The course also includes a new section on the Matplotlib and Seaborn data visualization libraries.

The course is designed for those who want to learn Pandas, which is considered one of the most powerful data processing tools and an in-demand skill for data analysts, data scientists, and data engineers. The course is designed to get students started with the Pandas library at the beginner level and make them feel confident about data processing tasks with Pandas at the advanced level. The course covers basics of the Pandas library, Python crash course, Python anaconda and Pandas installation, data structures like Series and Data Frame, text processing, Pandas inbuilt visualization tool, Multi-level index in Pandas, time-series analysis, and more.

The course provides students with 150+ HD quality video lectures, 16+ hours of content, discussion forums to resolve queries, and quizzes to test their understanding. The course is still in draft mode, with more content, quizzes, and projects related to data processing with different functionalities of Pandas being added. The course has sections on data visualization with Matplotlib and Seaborn and machine learning workflow with Scikit-learn. The course also includes a bonus lecture.

8. 2022 Python Data Analysis & Visualization Masterclass by Colt Steele (Udemy)

The 2022 Python Data Analysis & Visualization Masterclass course is a comprehensive offering from instructor Colt Steele. The course covers Pandas, Matplotlib, Seaborn, and more, and is aimed at individuals seeking to elevate their data skills for careers in Data Science, Machine Learning, Finance, Web Development, or similar fields. The content is broken down into digestible portions, and includes exercises and projects to reinforce topics covered. The course works with a variety of real-world datasets, including Amazon bestsellers, Rivian stock prices, Presidential Tweets, Bitcoin historic data, and UFO sightings.

The course curriculum covers a range of topics, including working with Jupyter Notebooks, using Pandas to read and manipulate datasets, working with DataFrames and Series objects, organizing, filtering, cleaning, aggregating, and analyzing DataFrames, extracting and manipulating date, time, and textual information from data, mastering Hierarchical Indexing, merging datasets together in Pandas, and creating complex visualizations with Matplotlib and Seaborn. The course stands out from other offerings in that it integrates visualizations early on and uses real datasets from the beginning.

Course content is organized into sections, including Introduction, Setup & Installation, Working With Jupyter Notebook, Dataframes & Datasets, Basic DataFrame Methods & Computations, Series & Columns, Indexing & Sorting, Filtering DataFrames, Adding & Removing Columns, Updating Values, Working With Types and NA Values, Working With Dates & Times, Matplotlib, Revisiting Pandas Plotting, Grouping & Aggregating, Hierarchical Indexing, Working With Text, Apply, Map, & Applymap, Combining Series & DataFrames, and Seaborn.

Overall, the course offers an approachable and engaging way to learn data analysis and visualization skills, and is suited for individuals with even a passing interest in the topics covered.

9. Manage Finance Data with Python & Pandas: Unique Masterclass by Alexander Hagmann (Udemy)

The Manage Finance Data with Python & Pandas course, instructed by Alexander Hagmann, aims to equip individuals with the necessary skills to analyze stocks and financial data through the use of Pandas, Numpy, Seaborn, and Plotly. The course is designed to assist individuals in managing financial data more efficiently and effectively, keeping up with the industry’s increasing demand for data-driven business.

The course begins with an introduction to Pandas, which is presented as the Excel for Python. The library offers more functionalities than Excel, which enables users to carry out their regular workflows more efficiently. The course emphasizes the importance of understanding Pandas thoroughly and being familiar with its best practices and pitfalls.

The Manage Finance Data with Python & Pandas course comprises four parts, with the second part regarded as the core of the course. This section focuses on importing financial data from various sources, calculating risks and returns, creating financial indexes and portfolios, and understanding modern portfolio theory, risk diversification, and the Capital Asset Pricing Model.

The third part of the course presents a capstone project that challenges learners to apply what they have learned by creating and analyzing a client’s portfolio. Part four of the course is designed for advanced users who want to learn how to handle time-series data with Pandas. The course also includes two optional appendices that cover Python and Numpy crash courses.

The course instructor, Alexander Hagmann, boasts of over ten years of experience in the finance and investment industry and is a master of science in finance. He has led a company-wide transformation from Excel to Python/Pandas, and his real-world projects, code, and models have been proven to be effective. Additionally, he is an instructor of the highest-rated and most trending general course on Pandas.

Overall, the Manage Finance Data with Python & Pandas course is a practical course that features two hundred coding exercises and a final project.

10. Data Analysis in Python with Pandas by Bill Chambers (Udemy)

The “Data Analysis in Python with Pandas” course is designed to provide an introduction to data analysis with the Pandas library in Python. The course is taught by Bill Chambers and includes hours of video content and code.

The course is designed for those who want to advance their data analysis skills beyond basic Excel analysis and those who want to learn how to do data analysis in Python and Pandas. The course offers access to all the code for future reference, new updated videos, and future additions for free.

By taking the course, you will master the fundamental data analysis methods in Python and Pandas. The course covers popular Python data analysis technologies and teaches you how to analyze data sets using the Pandas data analysis library. You will also learn how to create basic plots of data using MatPlotLib and analyze real datasets to better understand techniques for data analysis.

The course is designed for individuals with an intermediate programming ability who are ready to take their data analysis skills to the next level. It includes tips and tricks that can cut down the learning curve for business analysts and Master’s students at UC Berkeley doing data analysis.

The course covers cutting-edge techniques used by data analysts, data scientists, and other data researchers in Silicon Valley. It includes working files and code samples, over 5 hours with 40+ lectures, and teaches all that is needed to apply data analysis strategies to the data that one works with.

The course is divided into several sections, including an introduction to the course, IPython Notebooks and Raw Python Data Analysis, The Basics of NumPy, Pandas Basics, pandas Series, pandas DataFrame, and Advanced pandas Topics (Bonus).