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Data Visualization and Modeling in Python

Master Python data visualization with matplotlib and implement predictive models using classification and regression techniques for effective data analysis.

Master Python data visualization with matplotlib and implement predictive models using classification and regression techniques for effective data analysis.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Programming for Python Data Science: Principles to Practice Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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Data Visualization and Modeling in Python

This course includes

31 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Create professional visualizations for various types of data using matplotlib

  • Implement and evaluate K-Nearest Neighbors algorithms for classification

  • Apply regression techniques to analyze relationships between variables

  • Customize plots for effective data communication

  • Build predictive models from scratch in Python

  • Differentiate between prediction and inference in data science context

Skills you'll gain

Data Visualization
Matplotlib
K-Nearest Neighbors
Linear Regression
Classification
Python Programming
Predictive Modeling
Seaborn
Statistical Analysis
Machine Learning

This course includes:

2.07 Hours PreRecorded video

4 assignments

Access on Mobile, Tablet, Desktop

FullTime access

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There are 4 modules in this course

This comprehensive course bridges the gap between programming and data science by teaching advanced visualization and modeling techniques in Python. Students begin with an extensive exploration of plotting using matplotlib, learning to create and customize a variety of visualizations from basic line, bar, and scatter plots to more complex histograms and heatmaps. The second module introduces predictive modeling with a focus on K-Nearest Neighbors (KNN) algorithms for both classification and regression tasks, including implementation from scratch and evaluation methodologies. The third module covers statistical modeling with linear regression for both prediction and inference, teaching students to implement regression models and interpret relationships between variables. The course culminates in a capstone project where students integrate all learned skills to recreate a famous Gapminder visualization by merging multiple datasets to illustrate the relationship between countries' income and greenhouse gas emissions. Throughout, students gain hands-on experience through interactive assignments, live coding demonstrations, and real-world data analyses, building a foundation for a career in data science.

Plotting

Module 1 · 11 Hours to complete

Prediction

Module 2 · 9 Hours to complete

Regression

Module 3 · 5 Hours to complete

Final Project

Module 4 · 4 Hours to complete

Fee Structure

Instructors

Andrew D. Hilton
Andrew D. Hilton

4.7 rating

1,907 Reviews

10,59,309 Students

18 Courses

Associate Professor of the Practice

Andrew Hilton is an Associate Professor of the Practice in the Department of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he has been teaching since 2012. Before joining Duke, he worked as an advisory engineer at IBM. One of the key courses he teaches is ECE 551, an intensive introduction to programming designed to equip graduate students with no prior experience to master programming and tackle advanced courses. In 2015, Professor Hilton received the Klein Family Distinguished Teaching Award for his excellence in teaching. He holds a Ph.D. in Computer Science from the University of Pennsylvania.

Genevieve M. Lipp
Genevieve M. Lipp

4.7 rating

1,911 Reviews

2,65,562 Students

11 Courses

Assistant Professor of the Practice at Duke University

Dr. Genevieve M. Lipp is an Assistant Professor of the Practice in the Electrical and Computer Engineering and Mechanical Engineering and Materials Science departments at Duke University. She teaches a variety of courses, including programming in C++, dynamics, control systems, and robotics. Dr. Lipp is passionate about integrating technology into education to enhance learning outcomes and has previously worked in the Center for Instructional Technology at Duke. She holds a Ph.D. in mechanical engineering, focusing on nonlinear dynamics, as well as a B.S.E. in mechanical engineering and a B.A. in German, both from Duke University. In addition to her teaching responsibilities, she serves as the Director of the Duke Engineering First Year Computing program, where she focuses on improving computing education within the engineering curriculum and fostering students' self-efficacy in their studies.

Data Visualization and Modeling in Python

This course includes

31 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.