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

Instructors: Pearson

Included with

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1 module

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

8 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

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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.

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 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.

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

28 videos Total 394 minutes

Topics 1 minute
Introduction to Databases with SQLite 27 minutes
Inspecting Databases with the SQLite shell 13 minutes
The Database Landscape 12 minutes
What's in a Schema? Mapping Data Models to Data Tables 23 minutes
Introduction to Object Relational Mappers 7 minutes
ORMs in Python with peewee 19 minutes
Creating and Querying Records with peewee 25 minutes
End-to-end ETL in Python 9 minutes
Advantages and Disadvantages of ORMs 5 minutes
Extract, Transform, Load--Putting It All Together 10 minutes
Topics 1 minute
Introduction to Exploratory Data Analysis 17 minutes
Understanding your Data Quickly with Graphical Tools 17 minutes
Inspecting Databases and Building Schemas with peewee 20 minutes
Data Quality Checks with peewee 18 minutes
Finding Missing Data and Null Values with peewee 12 minutes
Dealing with Missing Data 11 minutes
EDA for Insight--Describing Data 5 minutes
Inspecting Queries and Displaying Results in peewee 18 minutes
Groups and Aggregates with peewee 14 minutes
Ranking and Sorting Venues 20 minutes
SQL Relations and Joins 8 minutes
Joins with peewee 28 minutes
Querying Across Datasets with Joins 26 minutes
Translating peewee to SQL 7 minutes
A Visual Introduction to Joins with SQL 15 minutes
Data Science Fundamentals Part 1: Sunmary 3 minutes

3 assignments Total 90 minutes

Storing Data: Persistence with Relational Databases Quiz 30 minutes
Validating Data: Provenance and Quality Control Quiz 30 minutes
End of Course Assessment 30 minutes

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Instructors

Pearson

268 Courses 43,572 learners

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Pearson

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