Career Options
- Data Analyst (Python-based)
- Business Data Analyst
- MIS / Reporting Analyst (Python Automation)
- Junior Data Scientist
- Operations / Process Analyst
Contact Us
Data Analysis Using Excel
Designed for students and professionals to turn Excel data into meaningful insights.
Duration: 1 Month
Mode: Online | Offline | Hybrid
Level: Beginner → Advanced
Projects: Excel analysis exercises, Business-data practice tasks, Practical reporting sheets.
Certificate: Available. ISO Certified.
Module 1 – Python Programming Core – 48 Hrs
- Introduction to Python: Python’s role in data analysis
- Data Types and Variables: Integers, floats, strings, and booleans
- Operators and Expressions: Arithmetic, logical, comparison, and assignment
- Control Flow: If-else statements, for and while loops
- Data Structures: Strings, tuples, dictionaries
- File Handling Basics: Reading and writing text files
- Functions and Modules: Writing reusable code and using built-in modules
- Error Handling: Managing errors with try-except blocks
Module 2 – Python Advanced: Key Libraries – 24 Hrs
- NumPy: Array creation, mathematical operations, indexing, reshaping
- Pandas: DataFrames, Series, importing/exporting, data cleaning
- Matplotlib: Bar charts, scatter plots, histograms, line graphs etc.
- Working with CSV and TXT Files: Reading, writing, and modifying structured data
Module 3 – SQL (Structured Query Language) – 24 Hrs
- Database Basics: Relational database concepts, RDBMS fundamentals
- SQL Queries: SELECT, WHERE, ORDER BY, GROUP BY, HAVING
- Joins and Relationships: INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
- Data Manipulation: INSERT, UPDATE, DELETE
- Constraints: Primary Key, Foreign Key, Unique, Check, Default, Not Null
- Subqueries and Set Operations: Subqueries, IN, EXISTS, UNION, INTERSECT
- Window (Analytic) Functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG
- Administrative Commands: CREATE USER, ALTER USER, DROP USER, GRANT, REVOKE, COMMIT, ROLLBACK
Module 4 – Basic & Advanced Excel – 18 Hrs
- Basic Excel
- Formulas and Functions: SUM, AVERAGE, IF, VLOOKUP, HLOOKUP
- Data Formatting: Conditional formatting, tables, sorting
- Basic Charts: Bar, line, and pie charts
- Advanced Excel
- Pivot Tables: Creating and customizing pivot tables
- Data Analysis ToolPak: Regression, correlation, statistical analysis
- Macros and VBA: Automating tasks with simple scripts
Module 5 – Power BI – 10 Hrs
- Introduction to Power BI: Dashboards and reports overview
- Connecting Data Sources: Excel, SQL, APIs
- Data Transformation: Using Power Query
- Building Visualizations: Bar charts, pie charts, maps
- DAX (Data Analysis Expressions): Calculated columns, measures
- Interactive Dashboards: Creating dynamic, user-driven reports
- Publishing and Sharing: Making your reports accessible
Module – Introduction to ML & Web Scraping (4 Hrs)
- Overview of Machine Learning & its applications (Supervised, Unsupervised, Reinforcement)
- Key Python ML libraries (scikit-learn, etc.)
- Basics of Web Scraping & Python tools (Requests, BeautifulSoup, Selenium)
- Data collection, cleaning, and exploration
- Types of Machine Learning Model
Capstone Project – 20 Hrs
Showcase Your Skills
- Database Connectivity: Build applications that connect to databases, execute queries, and retrieve data seamlessly.
- Data Analysis & Insights: Work with large datasets to uncover user behavior, sales trends, and product performance.
- Business Operations Management: Manage inventory, track sales, and forecast demand to support better decision-making.
- Institutional Management System: Design database schemas, implement queries, and generate reports for administrative tasks.
- Interactive Dashboards: Present analysis results through dynamic dashboards using Excel and Power BI.
- End-to-End Workflow: Demonstrate data collection, cleaning, analysis, visualization, and reporting within a single integrated project.
