Data Visualization with Python & R for Engineers
Learn data visualization techniques using Python and R for engineering applications.explores various static visualization charts and techniques.
Learn data visualization techniques using Python and R for engineering applications.explores various static visualization charts and techniques.
This course offers engineers a comprehensive introduction to data visualization using Python and R. Students will learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision-making in engineering, healthcare operations, manufacturing, and related applications. The curriculum covers the basics of data mining and visualization, introduces Python programming, and explores various static visualization charts and techniques. By the end of the course, students will be able to effectively analyze and present complex data, enhancing their ability to make data-driven decisions in engineering contexts.
Instructors:
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What you'll learn
Understand fundamental concepts of data and Big Data in engineering contexts
Learn data preprocessing techniques for effective analysis
Master various data visualization methods and their applications in engineering
Develop proficiency in Python programming for data analysis
Gain hands-on experience with NumPy and Pandas libraries
Create compelling data visualizations for engineering applications
Skills you'll gain
This course includes:
18 Minutes PreRecorded video
14 assignments,1 programming assignment,5 discussion prompts
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to data visualization techniques using Python and R, tailored for engineers. It begins with an exploration of data fundamentals, covering types of data, the data workflow, and Big Data concepts. Students then learn about data preprocessing, including finding and cleaning data for analysis. The course delves into various visualization techniques, teaching students how to create and interpret different types of statistical and geographical graphs. A significant portion of the course is dedicated to Python programming basics, covering variables, data types, conditional statements, loops, and functions. Students also gain hands-on experience with essential Python libraries like NumPy and Pandas for data manipulation and analysis. Throughout the course, practical assignments and discussions help reinforce learning and apply concepts to real-world engineering scenarios.
Introduction to Data - Part 1
Module 1 · 2 Hours to complete
Introduction to Data - Part 2
Module 2 · 2 Hours to complete
Introduction to Visualization
Module 3 · 3 Hours to complete
Basics of Python
Module 4 · 9 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Associate Teaching Professor at Northeastern University
Sivarit (Tony) Sultornsanee is an Associate Teaching Professor in the Department of Mechanical and Industrial Engineering at Northeastern University, where he also serves as the Assistant Program Advisor for the Data Analytics Engineering program. With over a decade of academic experience, he focuses on teaching courses such as "Foundations for Data Analytics Engineering" and is involved in developing online education, including a course on Data Visualization with Python & R for Engineers offered on Coursera. Dr. Sultornsanee's research interests include expert systems and knowledge-based systems, contributing to the field through various publications and academic initiatives. His expertise in data analytics and engineering positions him as a key figure in advancing educational practices within these domains at Northeastern University.
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