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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

Instructors: Pearson

Included with

Learn more

1 module

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

9 hours to complete

Flexible schedule

Learn at your own pace

1 module

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments

Taught in English

91% of learners achieved a positive career outcome

See how employees at top companies are mastering in-demand skills

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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 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.

Module details

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 videosTotal 486 minutes

Topics2 minutes
The Data Science Mindset15 minutes
The Data Science Technology Stack15 minutes
Where to Get Data: Sources and Services17 minutes
How the Web Works20 minutes
Making HTTP Requests with Python15 minutes
Adding Context with Open Data9 minutes
Parsing Data with Python--JSON and XML28 minutes
Data and File Formats15 minutes
Working with APIs24 minutes
Parametric API Requests with Python28 minutes
Exploring the Foursquare API14 minutes
Downloading Foursquare Venues22 minutes
Topics1 minute
Introduction to the ETL Pipeline11 minutes
Data Models--Adding Structure to Data24 minutes
Building Abstractions--Object Oriented Programming12 minutes
Creating Classes in Python20 minutes
Defining Methods and Updating State21 minutes
Magic Methods, Class Attributes, and Introspection25 minutes
Exploring and Structuring the Foursquare Response24 minutes
Data Models Applied--Representing Foursquare Entities with Classes22 minutes
Modeling Behavior with Methods15 minutes
Customizing Model Interfaces with Setter Methods and Virtual Attributes24 minutes
Keeping Things DRY with Inheritance29 minutes
Object-Oriented Programming Use Cases21 minutes
The Case for (and against) OOP13 minutes