Complete Sql For Data Analytics And Business Intelligence
Date: 1 September 2025 | Posted by: AD-TEAM
Complete Sql For Data Analytics And Business Intelligence
Published 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 8.66 GB | Duration: 20h 12m
Master SQL from Basics to Advanced with Visualized Concepts, Real-World Case Studies and Interview Qs from Top Companies
What you'll learn
Learn SQL from basics to advanced, focusing on data analytics and business intelligence.
Understand concepts with intuitive visualizations.
Access interview material inspired by real questions from top tech companies.
Work on case studies using e-commerce and retail sales data.
Learn advanced analytical techniques like window functions, data cleaning and Exploratory Data Analysis (EDA)
Learn how to handle complex data types like Date, Time, Array, and JSON data types effectively in SQL queries.
Explore Time Series Analysis and patterns with public datasets in a dedicated module.
Lecture notes for quick revision and interviews.
Strengthen your knowledge with a variety of exercises, quizzes, and challenges.
Get a Data Analyst roadmap and actionable career advice to help you land your dream role.
Requirements
A Mac, PC, or Linux-based Computer.
No prior technical/programming background required.
Description
Unlock the power of SQL with this Complete SQL for Data Analytics and Business Intelligence course! Designed for learners of all levels, this course takes you from SQL fundamentals to advanced analytical techniques. Through engaging visual explanations, real-world case studies, and hands-on practice, you'll master essential skills like SQL Joins, Subqueries, Grouping, Aggregation, handling complex data types, Time Series Analysis, Exploratory Data Analysis (EDA), window functions and more. Prepare for your dream job with interview-focused material inspired by top tech companies and benefit from lecture notes for quick revision. With quizzes, exercises, and a clear Data Analyst roadmap, this course equips you with the tools and confidence to excel in data-driven roles.Key Highlights of the CourseComprehensive SQL Learning: Master SQL from the basics to advanced topics, tailored for data analytics and business intelligence.Visual Learning: Grasp concepts easily with detailed explanations supported by intuitive visualizations.Interview Preparation: Gain access to specially curated material inspired by real interview questions from top tech companies like Tesla, Microsoft, Amazon, Uber, TikTok and more.Real-World Applications: Work on 2 case studies using e-commerce and retail sales datasets to apply your skills to practical scenarios.Advanced Analytical Techniques: Dive deep into advanced SQL concepts, including window functions and other analytical concepts like data cleaning and Exploratory Data Analysis (EDA).Complex Data Types: Learn how to handle Date, Time, Array, and JSON data types effectively in SQL queries.Time Series Analysis: Explore time-based trends and patterns with public datasets in a dedicated module.Comprehensive Study Materials: Includes lecture notes for quick revision and focused interview preparation.Interactive Learning: Strengthen your knowledge with a variety of exercises, quizzes, and challenges.Career Guidance: Get a Data Analyst/Data Scientist roadmap and actionable career advice to help you land your dream role.
Overview
Section 1: Introduction to Data Analytics
Lecture 1 Data Roles and Responsibilities
Section 2: Setting up Local Environment
Lecture 2 Section Introduction
Lecture 3 Database Servers and Clients
Lecture 4 Local Set up - Mac
Lecture 5 Local Set up - Windows
Lecture 6 Getting Familiar with pgAdmin
Lecture 7 Lecture Notes
Section 3: Database Terminologies
Lecture 8 Database and Schema
Lecture 9 Tables
Lecture 10 Multiple databases and schemas
Lecture 11 Creating a Database
Lecture 12 Lecture Notes
Lecture 13 Interview Questions
Section 4: Creating Table and Inserting Data into the Table
Lecture 14 Section Introduction
Lecture 15 Numeric Data Types
Lecture 16 Character Data Types
Lecture 17 Date and Time Data Types
Lecture 18 Boolean Data Type
Lecture 19 Other Important Data Types
Lecture 20 Creating a Table
Lecture 21 Inserting Data into Table
Lecture 22 Lecture Notes
Section 5: Reading Data From Table
Lecture 23 Section Introduction
Lecture 24 The SELECT statement
Lecture 25 Common String Operators and Functions
Lecture 26 Using Comments in SQL
Lecture 27 Lecture Notes
Section 6: Filtering Records
Lecture 28 Section Introduction
Lecture 29 The WHERE Clause
Lecture 30 Comparison operators in WHERE Clause
Lecture 31 Using WHERE with a List of Values
Lecture 32 Using WHERE within a specified range
Lecture 33 Using WHERE with pattern matching
Lecture 34 Compound WHERE Clause
Lecture 35 Lecture Notes
Section 7: Updating and Deleting Records
Lecture 36 Section Introduction
Lecture 37 Updating Records in a Table
Lecture 38 Deleting Records from a Table
Lecture 39 Dropping a Table from Database
Lecture 40 Lecture Notes
Section 8: Table Relationships and Constraints
Lecture 41 Section Introduction
Lecture 42 One-to-Many and Many-to-One Relationships
Lecture 43 One-to-One and Many-to-Many Relationships
Lecture 44 Primary Keys
Lecture 45 Foreign Keys
Lecture 46 Foreign Key Constraint on Delete
Lecture 47 Foreign Key Constraint on Insert
Lecture 48 Other Column Constraints
Lecture 49 Getting Table Schema Information
Lecture 50 Lecture Notes
Section 9: Joining Tables (SQL Joins)
Lecture 51 Section Introduction
Lecture 52 Preparing Dataset
Lecture 53 Why do we need Joins?
Lecture 54 Inner Join
Lecture 55 Left Outer Join
Lecture 56 Right Outer Join
Lecture 57 Full Outer Join
Lecture 58 Exercise 1
Lecture 59 Table and Column Alias
Lecture 60 Exercise 2
Lecture 61 Exercise 3
Lecture 62 Exercise 4
Lecture 63 Join with Filter
Lecture 64 Lecture Notes
Section 10: Grouping and Aggregating Records
Lecture 65 Section Introduction
Lecture 66 Aggregate Functions
Lecture 67 Why do we group records?
Lecture 68 Visualizing GROUP BY Operation
Lecture 69 Combining Grouping and Aggregation
Lecture 70 Exercise: Using Aggregation with Grouping
Lecture 71 Filtering Groups
Lecture 72 Using WHERE and HAVING Together
Lecture 73 Lecture Notes
Section 11: Sorting Records
Lecture 74 Section Introduction
Lecture 75 Sorting Records
Lecture 76 Limiting and Skipping Records
Lecture 77 Lecture Notes
Section 12: Subqueries
Lecture 78 Section Introduction
Lecture 79 What is a Subquery?
Lecture 80 Where is a Subquery Used?
Lecture 81 Subquery in SELECT Clause
Lecture 82 Subquery in FROM Clause
Lecture 83 Subquery in JOIN Clause
Lecture 84 Subquery in WHERE Clause
Lecture 85 Correlated Subquery
Lecture 86 Lecture Notes
Section 13: Case Study 1: Extracting Insights from E-commerce Dataset
Lecture 87 Quick Recap!
Lecture 88 Section Introduction
Lecture 89 Analytics Data Setup
Lecture 90 Exercise 1: Calculate the total revenue
Lecture 91 Solution - Exercise 1
Lecture 92 Exercise 2: Identify the top selling products
Lecture 93 Solution - Exercise 2
Lecture 94 Exercise 3: Calculate the Average Order Value (AOV)
Lecture 95 Solution - Exercise 3
Lecture 96 Exercise 4: Calculate the Customer Lifetime Value (CLV)
Lecture 97 Solution - Exercise 4
Lecture 98 Exercise 5: Identify customers who have not made a purchase in a while
Lecture 99 Solution - Exercise 5
Lecture 100 Exercise 6: Show the most recent review for each product
Lecture 101 Solution - Exercise 6
Lecture 102 Exercise 7: Identify the top rated products
Lecture 103 Solution - Exercise 7
Lecture 104 Exercise 8: Calculate discount based on order value
Lecture 105 Solution - Exercise 8
Section 14: Type Casting
Lecture 106 Section Introduction
Lecture 107 Explicit Type Casting
Lecture 108 Implicit Type Casting
Lecture 109 Lecture Notes
Section 15: Working with Date and Time Data Types
Lecture 110 Section Introduction
Lecture 111 Current Date and Time
Lecture 112 Time Zone Conversions
Lecture 113 Truncating Date and Timestamp
Lecture 114 Example: Truncating Date and Timestamp
Lecture 115 Extracting Parts of Date and Timestamp
Lecture 116 Formatting Date and Timestamp
Lecture 117 Date Math
Lecture 118 Lecture Notes
Section 16: Working with Array Data Type
Lecture 119 Section Introduction
Lecture 120 What is an Array?
Lecture 121 Adding Columns of Array Data Type
Lecture 122 Inserting Data into Array Columns
Lecture 123 Filtering Records Based on Array Values
Lecture 124 Array Indexing
Lecture 125 Array Functions
Lecture 126 Array Aggregation
Lecture 127 Exercise: Array Aggregation
Lecture 128 Flattening of Arrays
Lecture 129 Lecture Notes
Section 17: Working with JSON Data Type
Lecture 130 Section Introduction
Lecture 131 What is JSON?
Lecture 132 Adding Columns of JSON Data Type
Lecture 133 Inserting Data into JSON Columns
Lecture 134 Accessing JSON Fields
Lecture 135 Filtering Records Based on JSON Fields
Lecture 136 Constructing JSON Objects
Lecture 137 JSON Aggregation
Lecture 138 Lecture Notes
Section 18: Window Functions
Lecture 139 Section Introduction
Lecture 140 Data Setup
Lecture 141 The RANK() Window Function
Lecture 142 The DENSE_RANK() Window Function
Lecture 143 The ROW_NUMBER() Window Function
Lecture 144 The LAG() and LEAD() Window Function
Lecture 145 LAG() Function - A Practical Use Case
Lecture 146 Lecture Notes
Section 19: Common Table Expressions (CTEs)
Lecture 147 Section Introduction
Lecture 148 Common Table Expressions (CTEs) In Detail
Lecture 149 Lecture Notes
Section 20: SQL for Cleaning Data for Analysis and Reporting
Lecture 150 Section Introduction
Lecture 151 Data Setup
Lecture 152 Handling Missing Values
Lecture 153 Handling Duplicates
Lecture 154 Handling Outliers
Lecture 155 Standardizing Values in Categorical Columns
Lecture 156 Standardizing Text Data
Lecture 157 Consistent Format for Numeric Data
Lecture 158 Lecture Notes
Section 21: Case Study 2: Time Series Analysis with Retail Sales Dataset
Lecture 159 Section Introduction
Lecture 160 Importing Data From CSV to PostgreSQL Database
Lecture 161 Exploratory Data Analysis (EDA)
Lecture 162 Identifying Sales Trends
Lecture 163 Date Dimension Table and Fact Table
Lecture 164 A Generic and Reusable Date Dimension Table
Lecture 165 Top Business Categories By Sales
Lecture 166 Year-over-Year (YoY) Sales Analysis
Lecture 167 Quick Recap of the Key Concepts
Section 22: Some More Interview Questions From Top Companies
Section 23: Next Steps : Roadmap and Career Guidance
Lecture 168 Data Analyst / Data Scientist Roadmap
Job Seekers: Prepare effectively for Data Analyst interviews.,Non-IT Professionals: Transition into IT as a Data Analyst.,Software Developers: Enhance your skillset with data analytics expertise.,Data Scientists: Apply SQL to real-world analytics and business scenarios.,Junior to Intermediate Data Analysts: Master advanced concepts like Time Series Analysis.
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