Curriculum
7 Sections
135 Lessons
140 Hours
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Advanced Excel
Module 1: Advanced Excel
25
1.1
Overview of Data Analytics
1.2
Introduction to Microsoft Excel
1.3
Absolute and Relative References
1.4
Keyboard Shortcuts
1.5
Manipulating Rows and Columns
1.6
Formatting Output
1.7
Move or Copy Cell or Cell Content
1.8
Number Formats in Excel
1.9
Formulas in Excel
1.10
Create and Format Tables
1.11
Create Chart from Start to Finish
1.12
Create a Pivot Table to Analyze
1.13
Share Workbook with Others
1.14
Date and Time Functions
1.15
Advanced Paste Special Function
1.16
Sorting and Filtering
1.17
IF Analysis
1.18
Logical Functions
1.19
Data Validation
1.20
Array Functions
1.21
Lookup Functions (VLOOKUP/HLOOKUP, INDEX and MATCH, Nested VLOOKUP, Reverse Lookup, Worksheet Linking Using INDIRECT, VLOOKUP with Helper Columns)
1.22
Macros in Excel
1.23
Power Query in Excel
1.24
AI in Excel
1.25
Handling Excel using Python library
Python Programming
Module 1: Python Programming
11
2.1
History & Background
2.2
Basic Syntax, Variable Types
2.3
Data structures (lists, tuples, dictionaries, sets)
2.4
Operators and expressions
2.5
Control flow (if–else, loops)
2.6
Functions and basic program structure
2.7
Data Analysis with Python
2.8
NumPy for numerical computing
2.9
Pandas for data manipulation and analysis
2.10
Matplotlib for data visualization
2.11
Basic data exploration and visualization techniques
Database
44
3.1
Module 1: Introduction to Databases
3.2
Database Management Systems (DBMS)
3.3
Fundamental Database Concepts
3.4
Database Types
3.5
Joins and SQL Queries
3.6
Data Modeling
3.7
Normalization in Database Design
3.8
SQL Server and Tools
3.9
Module 2:Transact-SQL (T-SQL) for Data Analyst(Azure SQL)
3.10
Introduction to Transact-SQL (T-SQL)
3.11
Relational databases and T-SQL basics
3.12
SQL statement structure and SELECT statement
3.13
Data types and handling NULL values
3.14
Sorting and Filtering Data
3.15
Sorting results
3.16
Filtering data with WHERE clauses
3.17
Removing duplicates
3.18
Combining Data with Joins
3.19
Inner joins, outer joins, cross joins, and self joins
3.20
Subqueries in T-SQL
3.21
Scalar, multi-valued, and correlated subqueries
3.22
Built-in Functions and GROUP BY
3.23
Scalar and aggregate functions
3.24
Summarizing data with GROUP BY and HAVING
3.25
Data Modification with T-SQL
3.26
Inserting, updating, and deleting data
3.27
Merging data across tables
3.28
Advanced T-SQL Programming
3.29
Stored procedures and user-defined functions
3.30
Tables, Views, and Temporary Objects
3.31
Creating tables, views, temporary tables, and CTEs
3.32
Error Handling
3.33
TRY…CATCH for error handling
3.34
Transactions
3.35
Transactions with BEGIN, COMMIT, and ROLLBACK
3.36
Triggers
3.37
Triggers in SQL Server (Azure SQL Database)
3.38
Create, Alter, Drop Triggers
3.39
Overview of Databases
3.40
MySQL, PostgreSQL
3.41
Redis
3.42
Cassandra
3.43
Neo4J
3.44
MongoDB
Fundamentals of Power BI
41
4.1
Module 1: Introduction to Power BI
4.2
Overview of Data Analysis – Basics and importance.
4.3
Roles in Data – Understanding the role of a Data Analyst.
4.4
Introduction to Business Intelligence.
4.5
Key Tasks of a Data Analyst.
4.6
Power BI Desktop – Interface walkthrough and setup.
4.7
Building Blocks of Power BI – Dashboards, reports, and visualizations.
4.8
Module 2: Importing and Preparing Data
4.9
Importing Data – Connecting to sources like Excel, CSV, and SQL databases.
4.10
Data Loading Modes – Differences between Import and DirectQuery.
4.11
Data Transformation Basics – Cleaning, renaming, splitting columns, and filtering rows.
4.12
Combining Data – Merging and appending queries for a unified dataset.
4.13
Module 3: Data Modeling Fundamentals
4.14
Creating Relationships – Managing relationships between tables in Power BI.
4.15
Introduction to DAX – Basics of Data Analysis Expressions (SUM, COUNT, AVERAGE).
4.16
Calculated Columns and Measures – Writing formulas for specific calculations.
4.17
Date Tables – Creating and configuring a date table for time-based analysis.
4.18
Module 4: Building Visualizations
4.19
Visualizations – Bar, pie, line, and stacked charts.
4.20
Tables and Matrices – Presenting data in tabular form.
4.21
Filters and Slicers – Adding interactivity to reports.
4.22
Formatting Visuals – Customizing colors, themes, labels, and layouts.
4.23
Module 5: Report Design and Interactivity
4.24
Report Layouts – Designing structured and user-friendly layouts.
4.25
Drill-Through and Page Navigation – Setting up interactive elements.
4.26
KPIs and Cards – Highlighting key performance metrics.
4.27
Bookmarks and Buttons – Enhancing navigation and user experience.
4.28
Module 6: Advanced Analytics and Insights
4.29
DAX Time Intelligence – Year-to-date (YTD) and month-to-date (MTD) calculations.
4.30
Conditional Formatting – Highlighting trends and top-performing values.
4.31
Smart Narratives and Key Influencers – Deriving insights and explaining data trends.
4.32
Manual Data Refresh – Refreshing and updating data in Power BI Desktop.
4.33
Module 7: Performance Optimization and Troubleshooting
4.34
Optimizing Data Models – Reducing model size and improving performance.
4.35
Data Type Management – Choosing the correct data types for efficiency.
4.36
Identifying and Fixing Issues – Troubleshooting errors in Power BI.
4.37
Report Performance Tips – Best practices for creating efficient reports.
4.38
Module 8: Dashboards and Report Publishing
4.39
Creating Dashboards – Pinning visuals for quick insights.
4.40
Exporting Reports – Exporting reports to PDF and PowerPoint formats.
4.41
Sharing Options – Collaborating by sharing PBIX files or publishing to Power BI Service.
Introduction to Machine Learning(OPTIONAL)
14
5.1
Machine Learning Fundamentals
5.2
What is Machine Learning?
5.3
Relationship between Machine Learning and Data Analytics
5.4
Types of Machine Learning
5.5
Working with Data and ML Workflow
5.6
Scikit learn and Built-in datasets
5.7
Features and target variables
5.8
Data understanding and exploration
5.9
Train-test split
5.10
Model Building and Evaluation
5.11
Model training and prediction
5.12
Hands on
5.13
Model evaluation using accuracy
5.14
Comparing model performance
Review and Project Work
0
Internship - 2 Live projects
0
Data Analytics: Tools and Techniques
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