Data Science Fundamentals, Part 1

via Coursera Specialization

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

Designed for aspiring data scientists, engineers, and researchers, this hands-on program guides you through the entire data science process—from acquiring and transforming real-world data to building, validating, and deploying machine learning models. Through engaging, example-driven lessons and practical exercises using Python and its robust ecosystem of libraries, you'll gain the essential skills to analyze complex datasets, extract actionable insights, and create impactful data-driven applications—no advanced math or statistics background required.

Syllabus

Course 1: Data Science Fundamentals Part 1: Unit 1
Course 2: Data Science Fundamentals Part 1: Unit 2
Course 3: Data Science Fundamentals Part 1: Unit 3

Courses

0 reviews

View details

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.
0 reviews

View details

This course dives into real-world data sourcing, including making web requests, web scraping, and integrating diverse data types from APIs, files, and databases. You'll learn to parse and structure data in formats like XML and JSON, and leverage object-oriented programming to create robust data models. By the end of the course, you’ll be equipped to efficiently acquire, transform, and prepare data for advanced analysis.
0 reviews

View details

This course explores the fundamentals of relational databases and how to seamlessly map Python data structures to robust database tables using object-relational mappers (ORMs). You'll gain practical experience in building efficient ETL (Extract, Transform, Load) pipelines, ensuring your data is not only accessible but also reliable and persistent. You'll learn about data validation and quality control, leveraging powerful tools like Pandas to explore, clean, and analyze your datasets. By the end of the course, you’ll be equipped to uncover insights, identify biases, and apply best practices in data management.