This course demystifies core data science concepts and techniques through engaging Python lessons and real datasets. You’ll gain practical experience working with the Python ecosystem, including pandas, NumPy, scikit-learn, and more, as you analyze authentic data and build meaningful applications from scratch. From setting up your programming environment to building your first recommendation engine, each lesson emphasizes intuition, best practices, and the computational skills needed to tackle “undomesticated” data problems. No advanced math or statistics background required—just a willingness to learn and a basic familiarity with programming. By the end of the course, you’ll have built real projects, mastered essential data science workflows, and developed the confidence to apply machine learning algorithms to real-world challenges.



Data Science Fundamentals Part 1: Unit 1
This course is part of Data Science Fundamentals, Part 1 Specialization

Instructor: Pearson
Access provided by Girls in Tech
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What you'll learn
Develop a strong foundation in data science concepts, theory, and the practical application of Python’s data ecosystem.
Acquire, manipulate, and analyze real-world datasets using industry-standard tools and libraries.
Build and evaluate machine learning models, including recommendation engines, with hands-on projects.
Master the end-to-end data science process, from data acquisition to visualization and effective communication of results.
Skills you'll gain
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August 2025
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There is 1 module in this course
This module introduces the fundamentals of data science using Python, emphasizing that valuable insights can be achieved with simple programming and openly available data. It begins with an overview of data science concepts, its history, and real-world applications, followed by setting up a Python environment and a crash course in the language. The module then guides learners through the data science process by building an Airbnb listing recommender, teaching data manipulation with Python’s standard library and the basics of recommendation engines, while highlighting the importance of a structured workflow.
What's included
26 videos2 assignments
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