Data Analytics & AI Professional Program Training In Kochi

Module 1 – Excel for Data Analytics

1. Introduction to Data Analytics
   - Data Analytics lifecycle & process flow
   - Types of Data (Structured, Semi-structured, Unstructured)
   - Basic Statistics: Mean, Median, Mode, Variance, Standard Deviation
   
   2. Excel Basics
   - Interface, spreadsheet navigation, data entry, insertion, deletion
   - Importing data, table creation
   - Core formulas: SUM, MIN, MAX, COUNT, AVERAGE, IF, SUMIF, COUNTIF, AVERAGEIF, RIGHT, LEFT, MID, UPPER, LOWER, PROPER
   
   3. Power Query Introduction
   
   4. Data Cleaning & Preparation
   - Remove duplicates, handle missing data, fix inconsistencies
   - Text-to-columns, Flash Fill
   - Splitting & merging data, Data Validation
   - What-if analysis, Scenario Manager
   - Removing unnecessary characters/spaces, Conditional Formatting
   
   5. Data Exploration & Analysis
   - Descriptive statistics with Analysis ToolPak
   - Date & Time functions: TODAY, NOW, YEAR, MONTH, WEEKDAY, DATEDIF
   - Lookup & Reference: VLOOKUP, HLOOKUP, XLOOKUP, INDEX, MATCH
   
   6. Data Summarization & Visualization
   - PivotTables & PivotCharts
   - Charts: Bar, Column, Pie, Line, Scatter, Combo, Sparklines
   - Dynamic Charts & Dashboards

Module 2 – Power BI

1. Introduction
   - Power BI ecosystem, installation, interface
   - Data sources & connections
   
   2. Power Query for Data Preparation
   - Import, cleaning, transformation, sorting, filtering, aggregation
   
   3. Data Modeling
   - Normalization vs Denormalization
   - Relationships, Cardinality, Star Schema
   
   4. DAX (Data Analysis Expressions)
   - Calculated columns/tables, measures
   - Mathematical, Logical, Statistical, Text functions
   - Advanced DAX: Aggregate, Filter, Datetime, Time Intelligence, Table functions
   
   5. Visualization
   - Charts: Bar, Column, Pie, Donut, Line, Area, Scatter, KPI, Gauge, Table, Matrix, Map, Tree Map, Combo
   - Conditional Formatting, Hierarchies, Filters, Slicers, Drill-Downs
   
   6. Report Creation & Publishing
   - Comprehensive Power BI reports
   - Bookmarks, buttons, dashboard design
   - Publishing to Power BI Service
   
   Mini Project 1 – Create an interactive business dashboard integrating Excel & Power BI

Module 3 – MySQL for Data Analytics

1. Introduction
   - Databases & RDBMS concepts, Normalization
   - MySQL overview & Workbench setup
   
   2. SQL Fundamentals
   - DDL: CREATE, ALTER, RENAME, TRUNCATE, DROP
   - Keys: PK, FK, SK, CK, Composite Keys
   - Constraints: UNIQUE, NOT NULL, CHECK, DEFAULT, AUTO_INCREMENT
   
   3. DML & Clauses
   - INSERT, SELECT, UPDATE, DELETE
   - DROP vs TRUNCATE vs DELETE
   - DISTINCT, AS, WHERE, Operators, IS NULL, IN, BETWEEN, LIKE, ORDER BY, LIMIT
   
   4. Aggregation & Joins
   - Aggregates: COUNT, SUM, AVG, MIN, MAX
   - GROUP BY, HAVING
   - Joins: INNER, LEFT, RIGHT; UNION, UNION ALL
   
   5. Functions & Advanced SQL
   - Mathematical, String, Date & Time functions
   - Stored Procedures, Subqueries, Views, Triggers
   - DCL: GRANT, REVOKE
   - TCL: COMMIT, SAVEPOINT, ROLLBACK, ACID properties
   
   6. Integration
   - MySQL-Power BI connection for reporting
   
   Mini Project 2 – Design a relational database, insert data, run analytics queries

Module 4 – Python for Data Analytics

1. Introduction to Python
   - IDEs
   - Syntax, data types
   
   2. Data Structures & Control Flow
   - String, Tuple, Dictionary, Set, Frozenset, List
   - Operators, Conditionals, Loops, List Comprehension
   
   3. Functions & OOP
   - Built-in & user-defined functions, arguments, namespaces
   - Object-Oriented Programming basics
   
   4. Libraries for Data Analysis
   - NumPy arrays & operations
   - Pandas: Series, DataFrame, ingestion, slicing, indexing, manipulation, exploration, wrangling
   
   5. Data Visualization
   - Matplotlib: Scatter, Line, Bar, Pie, Histogram, Box
   - Seaborn: Count, Bar, Scatter, Line, Box, Histogram, Density, Violin, Swarm, Heatmap, Pair Plot
   
   6. Advanced Topics
   - Web Scraping: BeautifulSoup, Selenium, HTTP Requests
   - PySpark: Pandas vs PySpark, MySQL integration
   - Python-Power BI integration

Module 5 – AI & Gen AI

1. Foundations of AI in Data Analytics

- AI vs ML vs Data Analytics
- How AI fits into the analytics workflow
- Use cases: forecasting, pattern recognition, automation

- Key Python libraries (pandas, numpy, matplotlib, scikit-learn)

2. Predictive Analytics with Machine Learning

- Supervised learning
- Regression overview
- Model workflow: split → train → predict → evaluate

3. Customer Segmentation using Clustering

- Difference between supervised and unsupervised learning
- K-Means clustering algorithm

4. Generative AI for Smarter Insights

- What is Generative AI & how it differs from ML
- Large Language Models (LLMs): ChatGPT, Gemini, Copilot
- GenAI use-cases in analytics:

- Auto-summaries
- Prompt-to-SQL / Prompt-to-Chart
- Insight generation

5. AI-Powered Dashboards & Reporting

- Power BI AI visuals: Key Influencers, Decomposition Tree, Smart Narrative
- Python–Power BI integration overview
- Excel/Power BI Copilot demonstration

Capstone Project

End-to-end data analytics project integrating Excel, Power BI, MySQL, and Python.

Example: E-commerce Sales Analytics

Data cleaning in Excel & Power Query

Storing and querying in MySQL

Advanced analysis & visualization in Python

Final dashboard creation in Power BI

Module 6 – Applied Statistics

• Descriptive vs Inferential Statistics
• Data Distributions (Normal, Skewed)
• Correlation vs Causation with examples
• Regression (basic understanding)
• Hypothesis Testing (p-value, A/B testing)
• Practical implementation using Excel & Python

Module 7 – Exploratory Data Analysis (EDA)

• EDA workflow and importance
• Handling missing values and outliers
• Feature relationships and data patterns
• Visualization techniques (histogram, boxplot, heatmap)
• Insight generation and storytelling
• Case studies using real datasets

Module 8 – Advanced Python

• File handling (CSV, Excel, JSON)
• Exception handling
• Lambda functions, map(), filter()
• Writing reusable scripts
• Basic automation for data processing

Module 9 – Tableau

• Tableau interface and data connection
• Charts and visualizations
• Filters, parameters, groups, sets
• Calculated fields
• Dashboard design and storytelling
• KPI dashboards
• Real-world projects (Sales, Business dashboards)

Module 10 – Advanced Generative AI & LLM Applications 

• GenAI Applications - Analytics use cases
• LLMs - Overview, transformer (basic)
• Prompt Engineering  - Zero/few-shot
• LLM Ecosystem - Gemini, Hugging Face, Ollama
• Concepts - Embeddings, RAG, Vector DB
• Frameworks - LangChain intro
• Trends - Agentic AI, image generation

Capstone Project

• End-to-end Data Analytics Project
• SQL + Python + Tableau Integration
• Business Insights & Presentation

Data Analytics is central to the success of any modern organization. From retail trends to financial forecasting, the ability to interpret data defines whether a business succeeds or fails. A strong foundation in Data Analytics allows you to create data-driven, strategic, and visually compelling business solutions.

This Data Analytics course covers a comprehensive curriculum including:

• Advanced Data Manipulation – Master Excel and SQL for cleaning and extracting information.
• Statistical Thinking – Understand trends, correlations, and business metrics.
• Data Visualization – Build interactive dashboards to communicate insights effectively.
• Python for Automation – Learn to handle large datasets and automate repetitive tasks.
• Business Intelligence (BI) Tools – Hands-on training with Power BI and Tableau.

Portfolio Building & Case Studies – Work on real projects to showcase your analytical skills to employers.