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Getting Started with Pandas

This Pandas course prepares teaches the fundamentals of data analysis. It shows how to load data, inspect it, deal with missing values, use statistical summaries, plot, pivot and more. This course contains 18 hours of materials, taught by Matt Harrison, a Python and Pandas expert and author.

Matt Harrison

Data Science | core | 5 minutes | Mar 26 | 10:38 AM Eastern (USA)

Overview

3.3KSTUDENTS^*

97%RECOMMEND^*

This course includes:

9+ hours of on-demand video
5 modules
Core level
Direct access/chat with the instructor
100% self-paced online
Many downloadable resources
Shareable certificate of completion

This Pandas course teaches us the fundamentals of data analysis. It shows how to load data, inspect it, deal with missing values, use statistical summaries, plot, pivot and more. This course contains 18 hours of materials, taught by Matt Harrison, a Python and Pandas expert and author. Following this course students will be able to leverage their skills with pandas to perform powerful data analysis, create plots, clean data, and prepare data for machine learning. Pandas is a powerful library, but it can be confusing. Fill in the knowledge gaps and understand how Pandas works under the covers. You will come out of this class grokking the syntax, and the best practices for creating beautiful Pandas code. We will also show how to use Jupyter and install additional packages.

Skills You Will Gain

Jupyter

Pandas

Learning Outcomes (At the end of this program you will be able to)

Getting Started – Installation, Jupyter, and Example
- Understanding Jupyter and editors
- Installing libraries
- Overview of functionality of Pandas
Series objects
- Basic operations
- Aggregation methods
- Index manipulation
- String manipulation
DataFrame objects
- Slicing data
- Cleaning data
- Boolean arrays and filtering
- Plotting
Grouping
- Grouping with a column
- Grouping with multiple columns, functions, and dates
- Pivoting and stacking
Time series
- Shifting data
- Window operations
- Date grouping
- Dealing with missing data
- Plotting
Tips and Tricks
- Apply tricks
- Debugging
- Testing
- Seaborn plotting

Prerequisites

Basic computer knowledge is assumed
Prior programming experience with Python is strongly preferred

Who Should Attend

New Python developers looking to quickly develop and keen understanding of the power of Pandas
Individuals who are familiar with data science and need to understand Pandas

Instructors

Matt Harrison

With a rich background in Python and Data Science spanning over a decade, I bring a passion for empowering learners to unravel the complexities of programming and data analysis. My journey began with a deep dive into Computer Science at Stanford University, where I honed foundational skills that underpin my expertise today. From designing predictive models that classify job postings to architecting scalable systems on AWS and Docker, my hands-on experience extends across diverse domains, including machine learning, natural language processing, and web application development. My commitment to education extends beyond traditional classrooms. As an instructor, I've crafted engaging courses that demystify Python for Finance and Essentials of Stats with Python, ensuring students grasp both theoretical concepts and practical applications. At the heart of my teaching philosophy lies a belief in fostering critical thinking and problem-solving skills essential for today's data-driven world. I thrive on creating environments where learners of all backgrounds can explore, experiment, and ultimately excel in Python programming, data analysis, and beyond. In addition to my role as an educator, I'm deeply invested in community engagement and knowledge sharing. Whether speaking at renowned conferences like PyCon and SciPy or contributing to esteemed platforms such as O'Reilly Media and Pluralsight, I continuously seek to push the boundaries of what's possible in Python and Data Science education. My dedication to this field is underscored by a commitment to authenticity and a genuine desire to bridge the gap between theoretical learning and practical application.

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^*Where courses have been offered multiple times, the “# Students” includes all students who have enrolled. The “%Recommended” shown is also based on this data.