Introduction to Data Science in Python
Master Python data science fundamentals: pandas, numpy, and data manipulation for effective analysis.
Master Python data science fundamentals: pandas, numpy, and data manipulation for effective 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 Applied Data Science with Python 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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What you'll learn
Master fundamental Python programming for data science
Learn data manipulation with pandas DataFrame and Series
Develop skills in data cleaning and processing
Understand statistical testing and distributions
Gain proficiency in NumPy and CSV file handling
Skills you'll gain
This course includes:
5 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 introduces learners to data science fundamentals using Python. Students learn essential Python programming techniques, including working with lambdas and CSV files, along with the NumPy library for numerical computing. The course focuses heavily on data manipulation and cleaning using the pandas library, teaching students how to work with Series and DataFrame structures. Topics covered include data grouping, merging, pivot tables, and basic statistical analysis. The curriculum combines theoretical knowledge with hands-on programming assignments, preparing students for real-world data analysis tasks.
Fundamentals of Data Manipulation with Python
Module 1 · 13 Hours to complete
Basic Data Processing with Pandas
Module 2 · 6 Hours to complete
More Data Processing with Pandas
Module 3 · 7 Hours to complete
Answering Questions with Messy Data
Module 4 · 6 Hours to complete
Fee Structure
Instructor
Associate Professor at the University of Michigan
Christopher Brooks is an Associate Professor in the School of Information at the University of Michigan, where he specializes in designing tools to enhance teaching and learning experiences in higher education. His research focuses on the application of learning analytics within human-computer interaction, utilizing methods from educational data mining, machine learning, and information visualization. Brooks has published extensively in these areas and is actively involved in directing the Educational Technology Collective, which includes postdoctoral scholars and students collaborating on innovative projects. He teaches various courses related to applied data science and has contributed to online education platforms such as Coursera. His work aims to leverage data to improve educational outcomes and foster better learning environments.
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4.5 course rating
27,048 ratings
Frequently asked questions
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