Data Science Fundamentals Part 1: Unit 2

Seize the savings! Get 40% off 3 months of Coursera Plus and full access to thousands of courses.

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

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

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

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.

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

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Pearson

268 Courses 43,572 learners

Offered by

Pearson

Explore more from Data Analysis

Status: Free Trial
DeepLearning.AI
Data I/O and Preprocessing with Python and SQL
Course
Status: Free Trial
Pearson
Data Science Fundamentals Part 2: Unit 2
Course
Status: Free Trial
Pearson
Data Science Fundamentals Part 2: Unit 1
Course
Status: Free Trial
Pearson
Data Science Fundamentals Part 2: Unit 3
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Open new doors with Coursera Plus

Unlimited access to 10,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription

Learn more

Advance your career with an online degree

Earn a degree from world-class universities - 100% online

Explore degrees

Join over 3,400 global companies that choose Coursera for Business

Upskill your employees to excel in the digital economy

Learn more

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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.