SQL for Data Science course

SQL for Data Science course Course

A practical and essential course for anyone starting a career in data analytics using SQL.

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9.7/10 Highly Recommended

SQL for Data Science course on Coursera — A practical and essential course for anyone starting a career in data analytics using SQL.

Pros

  • Clear and structured introduction to SQL for analytics.
  • Focused on real-world data analysis use cases.
  • Beginner-friendly while still practical.

Cons

  • Does not cover advanced database design or indexing topics.
  • Requires practice beyond the course for full mastery.

SQL for Data Science course Course

Platform: Coursera

Instructor: University of California

What will you learn in SQL for Data Science Course

  • Write SQL queries to retrieve and analyze data from relational databases.

  • Use SELECT statements with filtering, sorting, and aggregation.

  • Apply JOIN operations to combine data from multiple tables.

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  • Understand subqueries and nested queries.

  • Perform data cleaning and transformation tasks using SQL.

  • Strengthen foundational skills for data analytics and business intelligence roles.

Program Overview

Introduction to SQL and Databases

⏳ 2–3 weeks

  • Learn how relational databases are structured.

  • Understand tables, rows, columns, and primary keys.

  • Write basic SELECT statements.

Filtering and Aggregating Data

⏳ 3–4 weeks

  • Use WHERE, ORDER BY, GROUP BY, and HAVING clauses.

  • Apply aggregate functions like COUNT, SUM, AVG, MIN, and MAX.

  • Analyze trends and summarize datasets.

Working with Multiple Tables

⏳ 3–4 weeks

  • Perform INNER JOIN and LEFT JOIN operations.

  • Combine datasets efficiently for deeper insights.

  • Use subqueries for advanced filtering.

Practical Data Analysis Applications

⏳ 2–3 weeks

  • Solve real-world data problems using SQL.

  • Extract insights for reporting and decision-making.

  • Strengthen readiness for analytics and BI tasks.

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Job Outlook

  • Essential skill for Data Analysts, Business Analysts, and Data Scientists.

  • SQL is widely used across industries including tech, finance, healthcare, and retail.

  • Strong foundation for advanced data analytics and data engineering roles.

  • Frequently required in entry-level analytics job descriptions.

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