#No.1 online platform for trending courses!
Data Analytics Engineering
The Data Analytics Engineering Course is designed to give learners a 360° understanding of modern data handling, analysis, and engineering. From foundational SQL to advanced tools like Power BI, Tableau, Apache Spark, and cloud platforms like GCP, this course helps you become a data professional who can analyze, model, visualize, and engineer data solutions.
✅ End-to-End Learning Path
Master a complete suite of tools and concepts from SQL, Power BI, Tableau, Python, Cloud Platforms, and Data Engineering in one course.
📊 Business Intelligence to Big Data
Whether you want to become a Data Analyst, BI Developer, or Data Engineer, this course builds your foundation with the latest industry tools and practices.
🔧 Hands-On Tools & Projects
Work with SQL Server, Power BI, Tableau, Python, Apache Spark, GCP, and more. Build dashboards, ETL pipelines, and visual analytics projects.
🎯 Job-Oriented Approach
Learn practical use cases, tools, and workflows to help you transition into roles like:
-
Data Analyst
-
BI Developer
-
Data Engineer
-
Reporting Analyst
-
Visualization Specialist
Course Prerequisites
About Course
-
Basic understanding of data and its role in business decision-making
-
Familiarity with Microsoft Excel or any spreadsheet tool
-
No prior programming experience required, but curiosity to learn is essential
-
A computer with internet access and willingness to explore tools hands-on
Curriculum
50 Lessons
What is SQL Server?
Why SQL?
DBMS vs RDBMS
SQL Syntax
SQL | WHERE Clause
SQL | SELECT Query
SQL | ORDER BY
SQL | GROUP BY
SQL | Aliases
HAVING Clause
Eliminating Duplicate Rows
Table /Column / Row/ Cell
Relationship
Types of Relationships
Data Types
String Data Type
Numeric Data Types
Date and Time Data Type
Constraints
NOT NULL Constraint
UNIQUE Constraint
DEFAULT Constraint
PRIMARY Key
FOREIGN Key
CHECK Constraint
Functions
Mathematical Functions
String Functions
Date and Time Functions
Conversion Functions
Operators
Assignment operator
Arithmetic operator
Comparison operator
Logical operator
Set operator
SQL Commands
DDL (Data Definition Language)
DML (Data Manipulation Language)
TCL (Transaction Control Language)
DCL(Data Control Language)
SQL Commands / Statements
Sub Query
Types of Sub Query
Single Row Sub Query
Multiple row sub query
Joins
Left Outer Join
Right Outer Join
Full Outer Join
Self Join
CROSS Join
Inner Join
Views
What is Visualization?
What is BI?
Why we need BI
What is Power BI?
Power BI Architecture
Installation of Power BI
Components of Power BI
Types of Data Loading
Import Mode
Direct Query Mode
Import vs Direct Query Mode
Composite Models
Most Commonly Used Data Sources
Excel
Text / CSV
MS Access
Web Link
PDF
SQL Server / Oracle
SSAS (Analysis Service)
SharePoint List / Online / Folder
Folder
Other Data Sources
TRANSFORMS
Remove Columns
First Row Header
Duplicate Columns
Split Columns
Remove Duplicate
Replace Value
Change Data Type
Group BY
Pivot and Unpivot
Formatting data
Add Custom Columns
Invoke Function
Append Queries
Merge Queries
Combine Files(Folder)
Parameters
Other Query Features
Introduction to Data Modeling
Deployment Cycle
Data Model Engine
What is Data Model
Relationships in Power BI
One to One
One to Many
Many to Many
Tables and Relationship
Grouping / Binning
Drill Through Reports
What IF Parameter
Bidirectional filters
Overview
Best Practice
Visualization types
Custom visuals
Creating power BI Visuals
Bar Chart and Line Chart
Combining charts
Adding slicers for filters
Tabular data / Matrix
Cards / Multi Cards
Categorical data
Data Trends
Categorical and Trends Data Together (Combo)
Geographical Data and Maps
Filters
Report Level
Page Level
Visual Level
Bidirectional filters
Synonyms
Custom Colors, Color Picker
What is Power BI SERVICE?
Licensing (Free Vs Pro Vs Premium)
App workspace
What is Report Server
Installing SQL Server with Tabular Mode
Installing Visual Studio
Installing SSDT
Creating a new Tabular Model
Data Modelling in SSAS Tabular
Deploy the Model
Processing Tabular Model
Schedule Processing (SQL Server Agent & SSIS)
Power BI Desktop vs SSAS Tabular
Mobile Power BI Overview
Designing the reports and Dashboard for Mobile
Interacting with Mobile Power BI App
Introduction to DAX
Calculated Columns
Calculated Tables
Calculated Measures
Calculated Columns vs Measures
Evaluation Contexts (ROW & FILTER Contexts)
Aggregated Functions (SUM,MIN,MAX,AVG,COUNT)
Relate Functions (RELATED & RELATED TABLE)
Iterative Functions (SUMX, AVGX, COUNTX)
Variables (VAR)
Logical functions (IF, SWITCH)
Table Functions
FORMAT FUNCTION, FIND, SEARCH, SUBSTITUE, VALUE, CALCULATE,UNION,ROW,ADDCOLUMNS,SUM MARIZE
Handling Blanks (ISBLANK,NOTISBLANK)
Rank Functions (RANKX)
Date Functions (DATEADD,DATEDIFF,TODAY Required Date Formats)
Calendar Functions (CALENDAR, CALENDAT AUTO)
Time Intelligence functions
SEMI ADDITIVE
Dynamic Measures, Dynamic Filters & Dynamic chart Axis
What is Business Intelligence (BI) ?
BI Process
What is KPI?
Visual Analysis
Data Visualization Tools
Tableau vs Power Bi
Why Tableau
Loading tableau
About Tableau
Tableau Products
Building Visualization
Mark Field
Color Palette
Clear – undo
Share – filters
Group – hierarchy
Sorting Data
Dashboard and share your work
Data Aggregations and calculations
Quick table calculations
Joins
Parameters
Analytics Pane
Introduction to Data Engineering
What is Data Engineering and why is it important?
Difference between Data Engineer, Data Scientist, and Data Analyst.
The role of Data Engineers in ETL, data pipelines, and data warehousing.
Fundamentals of Databases
Types of Databases
Relational Databases (SQL) – MySQL, PostgreSQL, SQL Server
NoSQL Databases – MongoDB, Cassandra, DynamoDB
Database Concepts
Primary Key, Foreign Key, Indexing, Normalization
ACID (Atomicity, Consistency, Isolation, Durability) vs. BASE principles
OLTP (Online Transaction Processing) vs. OLAP (Online Analytical Processing)
SQL for Data Engineering
Writing Basic Queries (SELECT, INSERT, UPDATE, DELETE).
Joins & Subqueries (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).
Aggregation Functions (SUM, AVG, COUNT, GROUP BY).
Window Functions (RANK, DENSE_RANK, ROW_NUMBER).
Query Optimization & Performance Tuning (Indexing, Execution Plans)
Programming for Data Engineering (Python & SQL)
Python Basics: Variables, Loops, Functions, Exception Handling.
Working with Pandas & NumPy for data manipulation.
Writing SQL Queries in Python using SQLAlchemy, psycopg2
Data serialization formats: JSON, Avro, Parquet, ORC.
Introduction to GCP
Introduction to Getting Started with GCP (1 min)
Essential Skills Required for GCP Data Analytics Course (2 min)
Understanding Cloud & GCP Fundamentals
Introduction to Cloud Platforms (4 min)
Overview of Google Cloud Platform (GCP) (3 min)
Creating a GCP Account
Signing Up for a GCP Account (2 min)
Creating a Google Account with a Non-Gmail ID (2 min)
Signing Up for GCP Using a Google Account (3 min)
GCP Account & Project Setup
Understanding GCP Credits (4 min)
Introduction to GCP Projects and Billing (2 min)
Exploring Google Cloud Shell (3 min)
Installing Google Cloud SDK on Windows (5 min)
Initializing gcloud CLI with a GCP Project (3 min)
Reinitializing Google Cloud Shell with a Project ID (3 min)
Introduction to GCP Analytics Services
Overview of Analytics Services on GCP (2 min)
Final Thoughts
Conclusion: Getting Started with GCP for Data Engineering
Extract, Transform, Load (ETL) Concepts
Batch vs. Real-time ETL and when to use each
Popular ETL Tools
Apache Airflow (Python-based workflow scheduler)
Talend, Informatica (GUI-based ETL tools).
Writing custom ETL scripts in Python.
Data Warehousing Basics
What is a Data Warehouse and how is it different from a Database?
Data Warehouse Architectures
Star Schema vs. Snowflake Schema
Fact & Dimension Tables
Popular Data Warehouses: Amazon Redshift, Google BigQuery, Snowflake
Partitioning & Clustering for performance improvement.
Big Data & Distributed Systems
Introduction to Hadoop & HDFS
How Hadoop stores and processes big data
Understanding the MapReduce framework
Introduction to Apache Spark
Spark vs. Hadoop (Why Spark is faster?)
PySpark for Data Engineering
Batch vs. Streaming Processing
Kafka vs. Flink vs. Spark Streaming.
Use cases for real-time data processing
Data Modeling & Schema Design
What is Data Modeling?
Schema Design for Data Warehousing
Normalized vs. Denormalized Data
Star Schema vs. Snowflake Schema
Slowly Changing Dimensions (SCD) for historical data tracking
Cloud Technologies for Data Engineering
Overview of Cloud Computing and its benefits
Key Cloud Providers
AWS: S3 (Storage), Glue (ETL), Lambda (Serverless), Redshift (Data Warehouse)
Azure: Azure Data Factory, Azure Databricks, Synapse Analytics.
Google Cloud: BigQuery, Cloud Storage, DataFlow.
Building scalable data pipelines on Cloud
Data Engineering DevOps Practices
CI/CD (Continuous Integration/Continuous Deployment) for Data Pipelines
Infrastructure as Code (IaC): Terraform, CloudFormation
Containerization & Orchestration: Docker, Kubernetes
Monitoring & Logging
Prometheus, Grafana for monitoring
AWS CloudWatch for logging
Data Security & Governance
Understanding Data Security Best Practices
Role-Based Access Control (RBAC) in Cloud Platforms
Data Privacy & Compliance (GDPR, HIPAA)
Data Lineage & Metadata Management (Tracking data sources & transformations)