Description
Curriculum
What is the Course About?
The “Data Analytics: Tools and Techniques” course is designed to equip learners with practical skills required to succeed in today’s data-driven industry. Whether you are an aspiring data analyst or a working professional looking to upskill, this program provides structured, hands-on training in core data tools and technologies.
The course covers Advanced Excel, Python Programming, Database Management (SQL & intro to NoSQL), Power BI, and an optional introduction to Machine Learning. You will learn how to collect, clean, analyze, visualize, and interpret data to generate meaningful business insights.
What Will You Learn?
- Python Programming: Build a strong foundation in Python, including syntax, data structures, control flow, and data analysis using NumPy, Pandas, and Matplotlib.
- Advanced Excel: Master data cleaning, pivot tables, advanced formulas, lookup functions, data validation, Power Query, Macros, and AI-enabled Excel features.
- Database Management: Understand database fundamentals, data modeling, normalization, SQL queries, joins, subqueries, and advanced T-SQL concepts including stored procedures, triggers, and transactions. Gain exposure to MySQL, PostgreSQL, MongoDB, Redis, Cassandra, and Neo4J.
- Power BI: Learn data import, transformation, data modeling, DAX basics, dashboard development, performance optimization, and report publishing for business insights.
- Machine Learning (Optional Module): Introduction to Machine Learning fundamentals, ML workflow, scikit-learn basics, model training, prediction, and evaluation(basics).
Skills You Will Gain
- Data Analysis using Excel, SQL, and Python
- Data Visualization and Dashboard Development using Power BI
- Database Querying and Data Modeling
- Python-based Data Processing and Analysis
- Automation and Reporting Techniques
- End-to-End Data Analytics Workflow
Why Should I Enroll in This Course?
This course is designed for individuals seeking a strong foundation and practical exposure in data analytics. With structured learning, hands-on exercises, real-world datasets, and live project experience, you will gain industry-relevant skills that prepare you for real business environments.
Career Pathways, Average Salary, and Hiring Companies
Career Roles
- Data Analyst
- Business Intelligence Analyst
- Visualization Specialist
Average Salary
₹20,000 to ₹30,000 per month (varies based on experience and location)
Hiring Companies:
Microsoft, Amazon, Google, financial institutions, consulting firms, and other data-driven organizations.
Curriculum
- 7 Sections
- 135 Lessons
- 140 Hours
Expand all sectionsCollapse all sections
- Advanced ExcelModule 1: Advanced Excel25
- 1.1Overview of Data Analytics
- 1.2Introduction to Microsoft Excel
- 1.3Absolute and Relative References
- 1.4Keyboard Shortcuts
- 1.5Manipulating Rows and Columns
- 1.6Formatting Output
- 1.7Move or Copy Cell or Cell Content
- 1.8Number Formats in Excel
- 1.9Formulas in Excel
- 1.10Create and Format Tables
- 1.11Create Chart from Start to Finish
- 1.12Create a Pivot Table to Analyze
- 1.13Share Workbook with Others
- 1.14Date and Time Functions
- 1.15Advanced Paste Special Function
- 1.16Sorting and Filtering
- 1.17IF Analysis
- 1.18Logical Functions
- 1.19Data Validation
- 1.20Array Functions
- 1.21Lookup Functions (VLOOKUP/HLOOKUP, INDEX and MATCH, Nested VLOOKUP, Reverse Lookup, Worksheet Linking Using INDIRECT, VLOOKUP with Helper Columns)
- 1.22Macros in Excel
- 1.23Power Query in Excel
- 1.24AI in Excel
- 1.25Handling Excel using Python library
- Python ProgrammingModule 1: Python Programming11
- 2.1History & Background
- 2.2Basic Syntax, Variable Types
- 2.3Data structures (lists, tuples, dictionaries, sets)
- 2.4Operators and expressions
- 2.5Control flow (if–else, loops)
- 2.6Functions and basic program structure
- 2.7Data Analysis with Python
- 2.8NumPy for numerical computing
- 2.9Pandas for data manipulation and analysis
- 2.10Matplotlib for data visualization
- 2.11Basic data exploration and visualization techniques
- Database44
- 3.1Module 1: Introduction to Databases
- 3.2Database Management Systems (DBMS)
- 3.3Fundamental Database Concepts
- 3.4Database Types
- 3.5Joins and SQL Queries
- 3.6Data Modeling
- 3.7Normalization in Database Design
- 3.8SQL Server and Tools
- 3.9Module 2:Transact-SQL (T-SQL) for Data Analyst(Azure SQL)
- 3.10Introduction to Transact-SQL (T-SQL)
- 3.11Relational databases and T-SQL basics
- 3.12SQL statement structure and SELECT statement
- 3.13Data types and handling NULL values
- 3.14Sorting and Filtering Data
- 3.15Sorting results
- 3.16Filtering data with WHERE clauses
- 3.17Removing duplicates
- 3.18Combining Data with Joins
- 3.19Inner joins, outer joins, cross joins, and self joins
- 3.20Subqueries in T-SQL
- 3.21Scalar, multi-valued, and correlated subqueries
- 3.22Built-in Functions and GROUP BY
- 3.23Scalar and aggregate functions
- 3.24Summarizing data with GROUP BY and HAVING
- 3.25Data Modification with T-SQL
- 3.26Inserting, updating, and deleting data
- 3.27Merging data across tables
- 3.28Advanced T-SQL Programming
- 3.29Stored procedures and user-defined functions
- 3.30Tables, Views, and Temporary Objects
- 3.31Creating tables, views, temporary tables, and CTEs
- 3.32Error Handling
- 3.33TRY…CATCH for error handling
- 3.34Transactions
- 3.35Transactions with BEGIN, COMMIT, and ROLLBACK
- 3.36Triggers
- 3.37Triggers in SQL Server (Azure SQL Database)
- 3.38Create, Alter, Drop Triggers
- 3.39Overview of Databases
- 3.40MySQL, PostgreSQL
- 3.41Redis
- 3.42Cassandra
- 3.43Neo4J
- 3.44MongoDB
- Fundamentals of Power BI41
- 4.1Module 1: Introduction to Power BI
- 4.2Overview of Data Analysis – Basics and importance.
- 4.3Roles in Data – Understanding the role of a Data Analyst.
- 4.4Introduction to Business Intelligence.
- 4.5Key Tasks of a Data Analyst.
- 4.6Power BI Desktop – Interface walkthrough and setup.
- 4.7Building Blocks of Power BI – Dashboards, reports, and visualizations.
- 4.8Module 2: Importing and Preparing Data
- 4.9Importing Data – Connecting to sources like Excel, CSV, and SQL databases.
- 4.10Data Loading Modes – Differences between Import and DirectQuery.
- 4.11Data Transformation Basics – Cleaning, renaming, splitting columns, and filtering rows.
- 4.12Combining Data – Merging and appending queries for a unified dataset.
- 4.13Module 3: Data Modeling Fundamentals
- 4.14Creating Relationships – Managing relationships between tables in Power BI.
- 4.15Introduction to DAX – Basics of Data Analysis Expressions (SUM, COUNT, AVERAGE).
- 4.16Calculated Columns and Measures – Writing formulas for specific calculations.
- 4.17Date Tables – Creating and configuring a date table for time-based analysis.
- 4.18Module 4: Building Visualizations
- 4.19Visualizations – Bar, pie, line, and stacked charts.
- 4.20Tables and Matrices – Presenting data in tabular form.
- 4.21Filters and Slicers – Adding interactivity to reports.
- 4.22Formatting Visuals – Customizing colors, themes, labels, and layouts.
- 4.23Module 5: Report Design and Interactivity
- 4.24Report Layouts – Designing structured and user-friendly layouts.
- 4.25Drill-Through and Page Navigation – Setting up interactive elements.
- 4.26KPIs and Cards – Highlighting key performance metrics.
- 4.27Bookmarks and Buttons – Enhancing navigation and user experience.
- 4.28Module 6: Advanced Analytics and Insights
- 4.29DAX Time Intelligence – Year-to-date (YTD) and month-to-date (MTD) calculations.
- 4.30Conditional Formatting – Highlighting trends and top-performing values.
- 4.31Smart Narratives and Key Influencers – Deriving insights and explaining data trends.
- 4.32Manual Data Refresh – Refreshing and updating data in Power BI Desktop.
- 4.33Module 7: Performance Optimization and Troubleshooting
- 4.34Optimizing Data Models – Reducing model size and improving performance.
- 4.35Data Type Management – Choosing the correct data types for efficiency.
- 4.36Identifying and Fixing Issues – Troubleshooting errors in Power BI.
- 4.37Report Performance Tips – Best practices for creating efficient reports.
- 4.38Module 8: Dashboards and Report Publishing
- 4.39Creating Dashboards – Pinning visuals for quick insights.
- 4.40Exporting Reports – Exporting reports to PDF and PowerPoint formats.
- 4.41Sharing Options – Collaborating by sharing PBIX files or publishing to Power BI Service.
- Introduction to Machine Learning(OPTIONAL)14
- 5.1Machine Learning Fundamentals
- 5.2What is Machine Learning?
- 5.3Relationship between Machine Learning and Data Analytics
- 5.4Types of Machine Learning
- 5.5Working with Data and ML Workflow
- 5.6Scikit learn and Built-in datasets
- 5.7Features and target variables
- 5.8Data understanding and exploration
- 5.9Train-test split
- 5.10Model Building and Evaluation
- 5.11Model training and prediction
- 5.12Hands on
- 5.13Model evaluation using accuracy
- 5.14Comparing model performance
- Review and Project Work0
- Internship - 2 Live projects0
Courses you might be interested in
Our AI, Machine Learning & Generative AI with Python training course is designed to equip learners with the advanced skills required to build, deploy, and scale intelligent systems using Python....
-
70 Lessons
Free
Our AI and Applied Data Science with Python training course is designed to provide students with the skills and knowledge necessary to build and implement data-driven solutions using Python. This...
-
51 Lessons
₹60,000.00₹49,500.00
Our Advanced AI and Applied Data Science with Python training course builds upon fundamental concepts, taking your skills to the next level. This course is ideal for those with prior...
-
97 Lessons
₹125,000.00₹98,500.00
Introduction to the Course Our AI and Applied Data Science with Python – Level 4 training course is designed to provide learners with the skills and knowledge necessary to build,...
-
109 Lessons
Free