Data Science Fundamentals Part 1: Unit 1
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Data Science Fundamentals Part 1: Unit 1
This course is part of Data Science Fundamentals, Part 1 Specialization
Instructors: Pearson
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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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There is 1 module in this course
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.
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
26 videos•Total 367 minutes
- Welcome to the Course•3 minutes
- Topics•2 minutes
- Why Data Science and Why Now?•8 minutes
- The Potential of Data Science•24 minutes
- Getting Set Up with a Data Science Development Environment•8 minutes
- A Python (3) Primer•22 minutes
- Python 2 versus Python 3•8 minutes
- Test Your Knowledge: Wordbuzz•28 minutes
- Wordbuzz: Putting it all Together•12 minutes
- Python Review and Resources•9 minutes
- Python for Data Science•15 minutes
- What’s to Come•10 minutes
- Topics•1 minute
- Introduction to the Data Science Process•7 minutes
- Defining Your Problem•7 minutes
- Acquiring Data•21 minutes
- Wrangling Data•28 minutes
- Exploring Data•29 minutes
- Recommendations through Triangle Closing•20 minutes
- Python Development Workflow•14 minutes
- Triadic Closure in Python•28 minutes
- Challenges of Recommendation Systems•11 minutes
- Obtaining an Evaluation Baseline•19 minutes
- Inspecting and Evaluating Results•12 minutes
- Present and Disseminate•15 minutes
- The Data Science Process Applied--Cheaper Beds, Better Breakfasts•6 minutes
2 assignments•Total 60 minutes
- Introduction to Data Science with Python Quiz•30 minutes
- The Data Science Process: Building Your First Application Quiz•30 minutes
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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.
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