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.
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Data Science Fundamentals Part 1: Unit 1
This course is part of Data Science Fundamentals, Part 1 Specialization
Instructor: Pearson
Included with 
Recommended experience
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
Details to know
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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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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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