Most real-world data isn鈥檛 clean, it鈥檚 messy, incomplete, and spread across sources like websites, APIs, and databases. In this course, you鈥檒l learn how to collect that data, clean it, and prepare it for analysis using Python and SQL.
Data I/O and Preprocessing with Python and SQL
Data I/O and Preprocessing with Python and SQL
This course is part of DeepLearning.AI Data Analytics Professional Certificate
Instructor: Sean Barnes
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Access provided by New York State Department of Labor
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Gain insight into a topic and learn the fundamentals.
Beginner level
Recommended experience
24 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
You鈥檒l work with real-world data as it exists in practice: messy, unstructured, and spread across sources.
You鈥檒l learn to extract data from websites, APIs, and databases, and clean it using both Python and SQL, an essential step in any analysis pipeline.
Skills you'll gain
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Assessments
16 assignments
Taught in English
Recently updated!
May 2025
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This course is part of the DeepLearning.AI Data Analytics Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- 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 from DeepLearning.AI
There are 4 modules in this course
This module introduces techniques for acquiring data from a wide range of sources, with a focus on web scraping and text processing. You'll begin by exploring how data flows into analysis pipelines and gain hands-on experience using tools like Pandas and Beautiful Soup to extract, clean, and structure data. You'll apply text preprocessing methods to handle missing values and parse HTML. Plus, you鈥檒l consider the ethical implications of scraping data from the web.
What's included
22 videos3 readings4 assignments1 programming assignment3 ungraded labs
This module focuses on acquiring data using APIs, as well as applying numerical cleaning techniques. You鈥檒l learn how to retrieve data from web-based APIs, handle authentication securely, and transform raw JSON responses into usable dataframes. The module also covers techniques for cleaning and preparing numerical data, including scaling, binning, normalization, and outlier handling.
What's included
17 videos2 readings4 assignments1 programming assignment3 ungraded labs
This module introduces the fundamentals of data storage and retrieval using databases and SQL. You鈥檒l learn how data is structured in relational systems; explore core concepts like entities, relationships, and schemas; and gain hands-on experience writing SQL queries. You鈥檒l also explore how to query databases from a Python notebook, as well as how generative AI tools can support SQL-based tasks.
What's included
15 videos3 readings4 assignments1 programming assignment2 ungraded labs
In this module, you鈥檒l expand your SQL skills into data preprocessing, validation, and joins (combining tables). You鈥檒l learn how to use SQL for filtering, conditional logic, and handling missing values, and apply validation techniques using aggregation and grouping. The module also explores different types of joins and demonstrates how to use them to combine and analyze data across multiple tables鈥攅specially in real-world scenarios like analyzing sports performance data.
What's included
17 videos11 readings4 assignments2 programming assignments4 ungraded labs
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