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SQL for Data Science

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About Course

Unlock the power of databases and boost your data science skills with our SQL for Data Science course. Designed for beginners and aspiring data scientists, this course will take you step-by-step through the essentials of Structured Query Language (SQL), a critical tool for data analysis, data manipulation, and database management.

Key Features:

  • Beginner-Friendly: No prior coding or database experience required. Start from the fundamentals and work up to advanced queries.

  • Practical, Hands-On Projects: Real-life case studies and interactive exercises to apply your skills to actual data.

  • Comprehensive Curriculum: Covering topics such as SELECT, JOIN, GROUP BY, subqueries, data filtering, aggregate functions, and database design.

  • Industry-Standard Tools: Gain experience with tools like SQLite, MySQL, and PostgreSQL commonly used in data science and analytics.

  • Data-Driven Problem Solving: Learn how to extract and analyze large datasets to uncover insights and support decision-making in business contexts.

What You Will Learn:

  • Basic SQL Syntax: Learn how to write simple queries, select data, and filter results.

  • Advanced Queries: Understand complex concepts such as joins, subqueries, and data aggregation techniques.

  • Database Relationships: Learn how tables are structured and how to relate them for efficient data retrieval.

  • Data Manipulation: Learn how to insert, update, and delete records while maintaining data integrity.

  • Data Analysis Skills: Use SQL for sorting, filtering, and aggregating data to gain insights for business or research purposes.

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What Will You Learn?

  • ✅ Fundamentals of SQL
  • Understand what SQL is and how it's used to manage and query data in relational databases.
  • ✅ Writing SQL Queries
  • Write basic to advanced SQL queries using SELECT, WHERE, GROUP BY, ORDER BY, and HAVING clauses.
  • ✅ Data Filtering and Sorting
  • Use logical conditions and operators to filter data, and organize query results for deeper insight.
  • ✅ Joining Multiple Tables
  • Learn to combine data from multiple related tables using INNER JOIN, LEFT JOIN, and other types of joins.
  • ✅ Aggregating Data
  • Use aggregate functions (COUNT, SUM, AVG, MIN, MAX) to summarize and analyze large datasets.
  • ✅ Subqueries and Nested Logic
  • Understand how to build and use subqueries within your SQL statements to answer more complex questions.
  • ✅ Modifying Databases
  • Gain the ability to insert, update, and delete data in relational databases responsibly.
  • ✅ Data Cleaning and Transformation
  • Apply SQL techniques to clean and format raw data, preparing it for further analysis.
  • ✅ Database Design Basics
  • Learn key concepts like primary keys, foreign keys, and normalization to design efficient databases.
  • ✅ Real-World Data Analysis Projects
  • Work on hands-on projects using real-world datasets to make data-driven decisions.
  • ✅ Query Optimization Techniques
  • Explore best practices for writing efficient SQL queries that scale with large datasets.
  • ✅ Confidence with SQL Tools
  • Get hands-on experience with tools such as MySQL, PostgreSQL, or SQLite—skills directly transferable to the workplace.

Course Content

Module 1: Introduction to Databases and SQL
Lesson 1.1: What is a Database? Understanding databases and their types Relational databases and non-relational databases Introduction to SQL and its role in data science Lesson 1.2: SQL Basics Overview of SQL syntax Writing basic SELECT queries Understanding SQL Data Types (text, numbers, dates, etc.)

Module 2: Retrieving Data from Databases
Lesson 2.1: The SELECT Statement Selecting columns and rows from a table Filtering data using the WHERE clause Sorting results using ORDER BY Lesson 2.2: Filtering and Query Conditions Using comparison operators (=, >, =, <=, , LIKE) Logical operators (AND, OR, NOT) Filtering by NULL values Lesson 2.3: Aggregating Data COUNT(), SUM(), AVG(), MIN(), MAX() Grouping data with GROUP BY Filtering aggregated data using HAVING

Module 3: Working with Multiple Tables
Lesson 3.1: Joining Tables INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN Combining data from multiple related tables Using JOINs with aggregate functions Lesson 3.2: Subqueries and Nested Queries Writing subqueries in SELECT, WHERE, and FROM clauses Using subqueries to filter and aggregate data

Module 4: Modifying Data
Lesson 4.1: Inserting Data Inserting records into a table Bulk insertion techniques Lesson 4.2: Updating Data Updating existing records Using conditional updates to modify data selectively Lesson 4.3: Deleting Data Deleting rows from a table Best practices for safe deletion

Module 5: Advanced SQL Topics
Lesson 5.1: Complex Queries Advanced filtering and sorting techniques Window functions (ROW_NUMBER(), RANK(), etc.) Working with dates and time in SQL Lesson 5.2: Database Design Understanding Primary Keys and Foreign Keys Database normalization and denormalization Indexing and optimizing queries

Module 6: Practical Data Analysis with SQL
Lesson 6.1: Using SQL for Data Analysis Analyzing large datasets using SQL queries Data cleaning and transformation using SQL Lesson 6.2: Real-World Case Studies Solving business problems using SQL Hands-on projects: Analyzing sales, customer, or product data

Module 7: Final Project
Lesson 7.1: Building a SQL-based Data Science Solution Applying your knowledge to a real-world problem Designing a database, writing complex queries, and analyzing results Presenting your findings

Module 8: Conclusion & Next Steps
Lesson 8.1: Advanced SQL Topics (Optional) Exploring database management systems (DBMS) like MySQL, PostgreSQL, or SQLite Lesson 8.2: Career Path and Resources How SQL fits into data science careers Additional resources for deepening SQL and data science expertise

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