- Course Overview
Online
₹ 3,499
Quick facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
SQL is required for handling structured data by a data scientist. Considering that relational databases are used to store structured data. Therefore, a data scientist needs to be well-versed in SQL commands to query these databases. Master SQL For Data Science certification course is designed by Imtiaz Ahmad - Senior Software Engineer & Trainer, which is delivered by Udemy for learners who wish to master the fundamentals of data science using the features of the structured query language.
Master SQL For Data Science online classes encompasses 9 hours of video-based learning materials along with 4 downloadable study resources and assignments that are intended to offer learners a foundation in SQL and dealing with data so they can start analyzing it for data science applications. Master SQL For Data Science online course covers the fundamentals of SQL statements, SQL queries, CASE clauses, window functions, and advanced querying methods including aggregations, subqueries, rollups, cubes, joins, transposing & ranking data, and using conditional expressions.
The highlights
- Certificate of completion
- Self-paced course
- 9 hours of pre-recorded video content
- 11 articles
- 4 downloadable resources
- Assignments
Program offerings
- Online course
- Learning resources
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
certificate providing authority
What you will learn
After completing the Master SQL For Data Science online certification, learners will develop an in-depth understanding of the fundamentals of data science using the capabilities of SQL. in this data science course, learners will explore the functionalities of SQL statements, SQL queries, subqueries, CASE clauses, views, tables and window functions. In this SQL certification, learners will study strategies involved with aggregation and grouping data.
The syllabus
Course Overview
Database Basics
- What is a Database
- How to Proceed in this Course
- Dedicated TA Support
- Install Postgres Database on Mac
- Troubleshoot Connection
- Install Postgres on Windows
- Create Table and Insert Statements
- Data file that will be used in the course (IMPORTANT)
- Prepare the Database
- Assignment 1: Create More Tables and Populate Data
SQL Query Basics
- Introducing the SELECT Statement
- Filter Data Using the WHERE Clause + AND & OR
- Filtering Operators - IN, NOT IN, IS NULL, BETWEEN
- [EXERCISES]: WHERE Clause and Operators
- Using ORDER BY, LIMIT, DISTINCT and Renaming Columns
- Assignment 2: Practice Writing Basic Queries (5 Problems)
Using Functions
- UPPER(), LOWER(), LENGTH(), TRIM() + Boolean Expressions & Concatenation
- Career Advice
- String Functions: SUBSTRING(), REPLACE(), POSITION() and COALESCE()
- Grouping Functions: MIN(), MAX(), AVG(), SUM(), COUNT()
- Assignment 3: Practice with Functions, Conditional Expressions and Concatenation
Grouping Data and Computing Aggregates
- Understanding Grouping
- GROUP BY & HAVING Clauses
- [EXERCISES]: Using GROUP BY and HAVING Clauses
- Assignment 4: Practice Aggregation Queries
Using Subqueries
- Aliasing Sources of Data
- Introducing Subqueries
- Subqueries Continued + [EXERCISES]
- Subqueries with ANY and ALL Operators + [EXERCISES]
- [EXERCISES]: More Practice with Subqueries
- Assignment 5: Practice with Subqueries
Using the CASE Clause in Interesting Ways
- Conditional Expressions Using CASE Clause + [EXERCISES]
- Transposing Data using the CASE Clause + [EXERCISES]
- Assignment 6: Practice Using Case and Transposing Data
Advanced Query Techniques using Correlated Subqueries
- Understanding Correlated Subqueries
- [EXERCISES]: Correlated Subqueries Continued
Working with Multiple Tables
- Introducing Table Joins
- INNER and OUTER Joins + [EXERCISES]
- Using UNION, UNION ALL and EXCEPT Clauses + [EXERCISES]
- Cartesian Product with the CROSS JOIN
- [EXERCISES]: Joins and Subqueries Continued
- Creating Views vs. Inline Views
- Assignment 7: ADVANCED Problems using Joins, Grouping and Subqueries
Window Functions for Analytics
- Window Functions using the OVER() Clause
- Ordering Data in Window Frames
- RANK, FIRST_VALUE and NTILE Functions
- Working with LEAD and LAG Functions
- Working with Rollups and Cubes
Difficult Query Challenges
- Challenging Puzzles For the Brave
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Instructors
Mr Imtiaz Ahmad
Senior Software Engineer
Udemy