By Scientists for scientists

Data Analysis with Pandas for Scientists & Engineers

Enthought Academy

Data Analysis with Pandas for Scientists & Engineers

Track Data Analysis Track

From an overview of a typical data analysis workflow and foundational ideas, apply your understanding of major concepts through exercises with NumPy, Pandas, and XArray. Through a practicum, develop a data analysis workbook that can be applied in real-world projects.

Course Hours20 hours

Course Overview

Data Analysis for Scientists and Engineers is designed in two parts. The first part presents a typical data analysis workflow with the foundational ideas behind each step. Here students will be presented with major concepts and given short exercises to practice those ideas with NumPy, Pandas, and XArray.

The second part of the course is a practicum in which the workflow is used in conjunction with Pandas to work through a small data analysis project from beginning to end. Here, each session will remind students of the main workflow, teach how Pandas approaches that specific step, and then allow the students to put what they have learned into practice. In the end, the students will have built an end-to-end data analysis workbook that can be used as a basic template for other data analysis projects.


This course requires basic proficiency with Python and the scientific Python stack. Some practical experience with Jupyter Notebooks,  NumPy (ndarrays), Pandas (DataFrames), and scientific visualization in Python using Matplotlib are essential to working with the code and  concepts presented in this course.

If you have taken Enthought’s Python Foundations for Scientists and Engineers, you have the requisite background knowledge for this course.


Data Analysis Workflow – Introduction, Examples of Use Cases
Data Sources – Finding & Storing Data, Scraping Web, Databases, Formats
Preparing Data – Tidy Data, Missingness, Filling Gaps
Exploring Data – Summary Statistics, Visualization
Analysis & Modeling – Analysis & Modeling Use Cases
Pandas Practicum I – Practicum with Various Data Sources
Pandas Practicum II – Reshape, Pivot, Join, Merge
Pandas Practicum III – Dates & Times, Text Data, Categorical Data
Pandas Practicum IV – Multi-Level Indexing, Computations, Chaining
Pandas Practicum V – Automation, Building Analysis Notebooks


Enthought instructors have advanced degrees in scientific fields such as physics, engineering, computer science, and mathematics, and all have extensive experience through research and consulting in applying Python to solve complex problems across a range of industries, allowing them to bring their real world experience to the classroom every day.


numpy, pandas, xarray


Download the syllabus for this course here.

Our Scientific Python Experts

Enthought Academy instructors are scientists and engineers themselves and have deep knowledge and understanding of the strategies and technologies covered in each track, and extensive practical experience applying Python to solve complex challenges across a range of science-based industries.

Alexandre Chabot-Leclerc

Director, Operations

Mark Dickinson

Principal Engineer, Software Architecture

Sandhya Govindraraju

Senior Scientific Software Developer

Kuya Takami

Senior DTX Services Consultant and Instructor

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