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⇱ Data Science Fundamentals Part 1: Unit 1 | Coursera


Data Science Fundamentals Part 1: Unit 1

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Data Science Fundamentals Part 1: Unit 1

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Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Beginner level

Recommended experience

7 hours to complete
Flexible schedule
Learn at your own pace

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.

Details to know

Shareable certificate

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Assessments

2 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Data Science Fundamentals, Part 1 Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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 videosTotal 367 minutes
  • Welcome to the Course3 minutes
  • Topics2 minutes
  • Why Data Science and Why Now?8 minutes
  • The Potential of Data Science24 minutes
  • Getting Set Up with a Data Science Development Environment8 minutes
  • A Python (3) Primer22 minutes
  • Python 2 versus Python 38 minutes
  • Test Your Knowledge: Wordbuzz28 minutes
  • Wordbuzz: Putting it all Together12 minutes
  • Python Review and Resources9 minutes
  • Python for Data Science15 minutes
  • What’s to Come10 minutes
  • Topics1 minute
  • Introduction to the Data Science Process7 minutes
  • Defining Your Problem7 minutes
  • Acquiring Data21 minutes
  • Wrangling Data28 minutes
  • Exploring Data29 minutes
  • Recommendations through Triangle Closing20 minutes
  • Python Development Workflow14 minutes
  • Triadic Closure in Python28 minutes
  • Challenges of Recommendation Systems11 minutes
  • Obtaining an Evaluation Baseline19 minutes
  • Inspecting and Evaluating Results12 minutes
  • Present and Disseminate15 minutes
  • The Data Science Process Applied--Cheaper Beds, Better Breakfasts6 minutes
2 assignmentsTotal 60 minutes
  • Introduction to Data Science with Python Quiz30 minutes
  • The Data Science Process: Building Your First Application Quiz30 minutes

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Instructors

Pearson
268 Courses65,144 learners

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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.

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

This course is currently available only to learners who have paid or received financial aid, when available.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,