By Scientists for scientists

Data Analysis for Scientists & Engineers

Data Analysis 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 DateMarch 6, 2023 Course LocationVirtual

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.

Prerequisite

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.

Lectures

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
Workflow Practicum I – Practicum with Various Data Sources
Workflow Practicum II – Reshape, Pivot, Join, Merg
Workflow Practicum III – Dates & Times, Text Data, Categorical Data
Workflow Practicum IV – Multi-Level Indexing, Computations, Chaining
Workflow Practicum V – Automation, Building Analysis Notebooks

Instructors

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.

Packages

numpy, pandas, xarray

Interested in this course?

For more information, contact the Enthought Academy team.

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

Vice President, Digital Transformation Solutions

Tim Diller

Director, Digital Transformation Services

Mark Dickinson

Director, Software Architecture

Eric Olsen

Director, Training Solutions

Glen Granzow

Scientific Software Technical Trainer

Sogo Shiozawa

Scientific Software Developer

Kuya Takami

Senior DTX Services Consultant and Instructor

Logan Thomas

Senior DTX Services Consultant and Instructor