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.
Data Science Fundamentals Part 1: Unit 2
Data Science Fundamentals Part 1: Unit 2
This course is part of Data Science Fundamentals, Part 1 Specialization
Instructor: Pearson
Access provided by Mapua University
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
9 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Master the ETL (Extract, Transform, Load) process for seamless data acquisition and integration.
Acquire practical skills in sourcing data from APIs, web scraping, and managing data lineage.
Parse and transform diverse data formats (XML, JSON) for structured analysis.
Build and apply data models using object-oriented programming to streamline data workflows.
Skills you'll gain
Details to know
Shareable certificate
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Assessments
2 assignments
Taught in English
Recently updated!
August 2025
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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 module guides learners through the foundational steps of the data science process, focusing on data acquisition and transformation. Starting with methods for sourcing and extracting data from various platforms, students learn to manage data lineage and perform web requests. The module then covers parsing and structuring diverse data formats, such as XML and JSON, emphasizing the importance of data transformation and modeling. Through practical exercises, including working with APIs and relational databases, learners gain essential skills in the Extract, Transform, and Load (ETL) pipeline, preparing them for effective data exploration and analysis.
What's included
27 videos2 assignments
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