Pandas for Data Science
Learn to leverage Pandas for data science projects: Clean, manipulate, and optimize data efficiently using best practices and advanced techniques.
Learn to leverage Pandas for data science projects: Clean, manipulate, and optimize data efficiently using best practices and advanced techniques.
This course teaches how to effectively use Python's Pandas library for data science tasks. Students will learn to clean, sort, and store data using Pandas, understanding when and how to leverage this powerful library. The curriculum covers file operations, data cleaning techniques, and advanced data manipulation methods. By the end, learners will be proficient in using Pandas for various data science projects, preparing them for more complex software development in Python.
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What you'll learn
Understand when and how to use Pandas for data science projects
Master file operations and data cleaning techniques in Pandas
Learn to manipulate and optimize data using Pandas best practices
Gain proficiency in working with tabular data using Series and DataFrames
Develop skills in combining datasets from different sources
Understand efficient querying techniques for large datasets
Skills you'll gain
This course includes:
2.3 Hours PreRecorded video
1 quiz,8 assignments
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FullTime access
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There are 4 modules in this course
This course provides a comprehensive introduction to using Pandas for data science in Python. Students will learn how to read and write data from various file formats, clean and manipulate large datasets, and perform advanced data operations. The curriculum covers Pandas Series and DataFrames, indexing and subsetting techniques, handling missing data, and combining datasets from different sources. Practical skills are emphasized through hands-on exercises and programming assignments, preparing students for real-world data science tasks.
Intro to Pandas For Data Science + Strings and I/O
Module 1 · 16 Hours to complete
Module 2: Tabular Data with Pandas
Module 2 · 6 Hours to complete
Module 3: Loading and Cleaning Data
Module 3 · 8 Hours to complete
Module 4: Data Manipulation
Module 4 · 10 Hours to complete
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Instructors
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.
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.
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