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.
Data Science Fundamentals Part 1: Unit 3
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Data Science Fundamentals Part 1: Unit 3
This course is part of Data Science Fundamentals, Part 1 Specialization
Instructor: Pearson
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
8 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Master the fundamentals of relational databases and persistent data storage.
Build and optimize ETL pipelines using Python and object-relational mappers.
Apply data validation techniques to ensure data quality and integrity.
Utilize Pandas for effective data exploration, transformation, and statistical analysis.
Skills you'll gain
- Data Cleansing
- Exploratory Data Analysis
- Data Manipulation
- Relational Databases
- Data Transformation
- SQL
- Extract, Transform, Load
- Data Quality
- Database Management
- Descriptive Statistics
- Data Validation
- Databases
- Pandas (Python Package)
- Data Integrity
- Data Pipelines
- Data Storage Technologies
- Object-Relational Mapping
- Data Processing
- Data Analysis
Details to know
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Recently updated!
August 2025
Assessments
3 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.
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- Gain a foundational understanding of a subject or tool
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There is 1 module in this course
This module guides learners through essential data handling skills, from storing and persisting data using relational databases and object-relational mappers, to validating, exploring, and transforming data for analysis. Emphasizing practical techniques with tools like Pandas, the lessons cover best practices for querying, managing missing values, and using descriptive statistics and visualizations to understand data quality and distribution. The module provides a systematic approach to the ETL process, equipping students to efficiently prepare data for deeper analytical modeling.
What's included
28 videos3 assignments
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