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⇱ Data Science Fundamentals Part 1: Unit 2 | Coursera

Data Science Fundamentals Part 1: Unit 2

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Data Science Fundamentals Part 1: Unit 2

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Beginner level

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9 hours to complete
Flexible schedule
Learn at your own pace
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.

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Assessments

2 assignments

Taught in English

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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 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.

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

27 videos•Total 486 minutes
  • Topics•2 minutes
  • The Data Science Mindset•15 minutes
  • The Data Science Technology Stack•15 minutes
  • Where to Get Data: Sources and Services•17 minutes
  • How the Web Works•20 minutes
  • Making HTTP Requests with Python•15 minutes
  • Adding Context with Open Data•9 minutes
  • Parsing Data with Python--JSON and XML•28 minutes
  • Data and File Formats•15 minutes
  • Working with APIs•24 minutes
  • Parametric API Requests with Python•28 minutes
  • Exploring the Foursquare API•14 minutes
  • Downloading Foursquare Venues•22 minutes
  • Topics•1 minute
  • Introduction to the ETL Pipeline•11 minutes
  • Data Models--Adding Structure to Data•24 minutes
  • Building Abstractions--Object Oriented Programming•12 minutes
  • Creating Classes in Python•20 minutes
  • Defining Methods and Updating State•21 minutes
  • Magic Methods, Class Attributes, and Introspection•25 minutes
  • Exploring and Structuring the Foursquare Response•24 minutes
  • Data Models Applied--Representing Foursquare Entities with Classes•22 minutes
  • Modeling Behavior with Methods•15 minutes
  • Customizing Model Interfaces with Setter Methods and Virtual Attributes•24 minutes
  • Keeping Things DRY with Inheritance•29 minutes
  • Object-Oriented Programming Use Cases•21 minutes
  • The Case for (and against) OOP•13 minutes
2 assignments•Total 60 minutes
  • Acquiring Data: Sources and Methods Quiz•30 minutes
  • Adding Structure: Parsing Data and Data Models Quiz•30 minutes

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Instructors

Pearson
268 Courses•65,339 learners

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