Data Analytics Training Course
Master your skills and Become a Job-Ready Data Analyst
Take your first step toward a successful analytics career with Brassy Academy’s Data Analytics Training Course. Learn through hands-on projects, real-world case studies, and expert mentorship. Gain in-demand skills in Python, Excel, Power BI, and SQL, and harness the power of AI-driven analytics tools to stand out in 2025’s competitive data landscape.
🌐 தமிழ் & English
Join Data Analyst Program
Professional Data Analyst 2025 Course - Brassy Academy
Become a job-ready Data Analyst with Brassy Academy. Master Python, Excel, Power BI, and AI tools through real-world projects and expert-led training.
500+
Students Trained
25+
Industry Experts
10+
Live Projects
10+
Hiring Partners
What will you learn ?
You’ll gain a perfect balance of knowledge and practice. Each module is designed to help you understand real-world applications, strengthen your fundamentals, and build a career-oriented mindset.
Why Choose Our Course?
100% Practical Learning
Master full stack skills through real-world projects and hands-on coding sessions that enhance job readiness and boost practical expertise.
AI-Integrated Training
Learn to integrate AI tools into development workflows and stay ahead with future-ready, in-demand tech skills.
Live Projects
Gain real industry experience by working on business-grade projects and building a strong, recruiter-attractive portfolio
One-to-One Mentorship
Get personalized expert guidance to clear doubts quickly and accelerate your full stack learning journey.
Job Placement Support
Receive end-to-end career support including resume building, mock interviews, and placement assistance.
Industry-Curated Modules
Learn from modules designed by top tech professionals to match current industry trends and job requirements.
Reasons to do Course Certification Training
₹ 6 LPA
25%
10,000+
Top global companies for employee training
This course is trusted by top global companies for employee training and is featured in our collection of industry-leading programs.
Who Can Join Our AI Full Stack Developer Course?
Fresh Graduates
Kickstart your IT career with a strong foundation in real-world technologies. Our hands-on training and project-based learning approach prepare you to meet industry expectations from day one and stand out in the job market.
Working Professionals
Stay ahead in your career by upgrading your technical expertise. Learn the latest tools, frameworks, and industry practices to boost your productivity, expand your opportunities, and stay competitive in the evolving tech landscape.
Career Switchers
Transform your career path with a guided transition into software development. Gain structured learning, mentorship, and project experience that help you confidently move from your current field into the tech industry.
Entrepreneurs
Empower your ideas with technical know-how. Learn how to build, launch, and scale digital products independently—whether it’s a web app, mobile platform, or SaaS solution—without relying solely on external developers.
Students in Any Discipline
No matter your academic background, you can learn to code and master full-stack development. Our beginner-friendly curriculum and expert guidance help you understand complex tech concepts with ease and confidence.
Self-Taught Programmers
Take your coding journey to the next level with structured learning, personalized mentorship, and career support. Validate your skills, fill knowledge gaps, and gain access to professional opportunities that recognize your true potential.
Data Analyst Tools Covered
Python
Pandas
NumPy
Matplotlib
SQL
PowerBI
Excel
Course Syllabus
Excel Fundamentals
- Excel Interface Overview
- Workbook & Worksheet Structure
- Basic Formatting
- Basic Formulas: SUM, AVERAGE, MIN, MAX, COUNT, COUNTA
- Conditional Functions: IF, AND, OR, Nested IF
- Text Functions:LEFT, RIGHT, MID, LEN, CONCAT, UPPER, LOWER, PROPER, TRIM
Excel Advanced
- Lookup Functions: VLOOKUP, HLOOKUP
- XLOOKUP, INDEX + MATCH Data Cleaning
- Techniques: Remove Duplicates, Find & Replace
- Flash Fill, Text to Columns Sorting
- Filtering & Advanced Filters Conditional
- Formatting Pivot Tables – Grouping
- Summaries, Calculated Fields Charts
- Visualization: Line, Bar, Pie, Combo Charts
Introduction & Setup
- Introduction to Power BI & Its
- Components Power Query, Data Model,
- DAX, Reports Installing Power BI
- Desktop Getting Familiar with the Interface
- Importing Data from Excel / CSV / SQL
Data Cleaning & Transformation (Power Query)
- Power Query – Data Cleaning Remove
- Rows, Fill, Replace, Split Columns
- Column Transformations Merge
- Append, Unpivot Basic Data Profiling (Errors & Nulls Handling)
Functions
- Function declaration & expression
- Parameters & return values
- Arrow functions
Data Modeling & Relationships
- Understanding Relationships
- One-to-Many, Many-to-Many
- Star Schema vs Snowflake Schema
- Creating Optimized Data Models
DAX Calculations & Logic
- DAX Basics – Calculated Columns vs Measures
- Aggregation Functions – SUM, COUNT, AVERAGE, DISTINCTCOUNT
- Conditional Logic – IF, SWITCH, CALCULATE, FILTER
- Advanced DAX Functions (Time Intelligence, Ranking, Cumulative Analysis)
Data Visualization & Reporting
- Power BI Visualization Basics
- Bar, Column, Line Charts
- Pie, Donut, Waterfall, KPI Cards
- Maps, Matrix, Slicers, Drill-Down Reports
- Designing Interactive Dashboards
- Layout, Theme, Bookmarks
Advanced Concepts
- How to Build & Publish Dashboards
- Power BI Service (My Workspace)
- Subscriptions & Data Refresh
- Alerts, Gateway Setup
SQL Basics & Data Retrieval
- Introduction to Databases & SQL
- RDBMS Concepts, Tables, Primary & Foreign Keys
- Basic Queries: SELECT, FROM, WHERE – Filtering Records
- Sorting & Limiting Data: ORDER BY, LIMIT
- Removing Duplicates: DISTINCT
- Practice Queries / Assignments
Operators & Conditional Filtering
- Basic Operators: BETWEEN, IN, LIKE, IS NULL
- Combining Conditions: AND, OR, NOT
- Practice Queries / Mini Assignments
Aggregation & Grouping
- Aggregate Functions: SUM, COUNT, AVG, MIN, MAX
- Grouping Data: GROUP BY, HAVING (Advanced Filtering)
- Practice Queries / Case Study
Joins & Relationships
- Understanding Joins: INNER JOIN
- LEFT JOIN, RIGHT JOIN
- FULL OUTER JOIN
- Practice Queries / Multiple Table Scenarios
Advanced SQL Queries
- Set Operations: UNION vs UNION ALL
- Conditional Logic with CASE WHEN
- Subqueries: IN, EXISTS, SELECT within SELECT
- Window Functions: ROW_NUMBER, RANK, DENSE_RANK
- Practice Queries / Problem Solving
Optimization & Views
- Creating and Using Views
- Indexing Basics for Performance
- Best Practices for Query Optimization
- Final Practice Queries / Mini Project
Python Fundamentals
- Data Types – Numbers, Strings, Lists
- Tuples, Dictionaries, Sets
- Conditional Statements (if-else) & Loops (for, while)
- Functions, Lambda Functions, Modules
Numpy
NumPy – Numerical Computing
- Introduction to NumPy Arrays, Array Creation
- Array Indexing & Slicing
- Array Operations: Math, Aggregations, Broadcasting
- Reshaping, Stacking, Splitting Arrays
Pandas
Pandas – Data Manipulation (Basics)
- Series & DataFrame Basics
- Importing Data (CSV, Excel), Viewing & Describing Data
- Filtering, Sorting, Conditional Selection
Pandas – Data Manipulation (Advanced)
- GroupBy, Aggregations
- Merge & Join DataFrames
- Handling Missing Values & Data Cleaning
Data Visualization
- Introduction to Matplotlib: Line, Bar, Scatter
- Histogram, Pie, Subplots
- Seaborn Basics: Heatmap, Boxplot
Capstone Projects to Work on in Our
Data Analyst Course
Design and build an interactive sales dashboard that tracks key performance indicators (KPIs) such as revenue, profit margins, regional performance, top-selling products, and customer segments. Use real or simulated sales data across multiple years.
Scrape or collect Twitter or Reddit data related to a recent product launch (e.g., a smartphone, video game, or tech gadget). Perform sentiment analysis to assess public perception and track how it changes over time.
Analyze customer behavior to identify patterns and predictors of churn in a subscription-based company (e.g., telecom, SaaS, or streaming service). Use historical customer data to build a churn prediction model and uncover actionable insights to reduce churn.
FAQs
Student Voices
Hear from Our Alumni
