AI & Data Analytics – 120 Days
Duration: 120 Days
Level: Beginner to Advanced
Mode: Theory + Practical + Projects
Tools: Excel, Python, SQL, Power BI, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook
Level: Beginner to Advanced
Mode: Theory + Practical + Projects
Tools: Excel, Python, SQL, Power BI, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Jupyter Notebook
Module 1 – Data Analytics Fundamentals
- Introduction to Data Analytics
- Types of Data
- Data Analytics lifecycle
- Descriptive, Diagnostic, Predictive and Prescriptive Analytics
- Structured and unstructured data
- Data collection methods
- Data cleaning concepts
- Introduction to AI, ML and Data Science
- Real-world applications
- Basic analytics exercises
Practical: Basic data analysis exercises using real-world datasets.
Module 2 – Advanced Excel for Analytics
- Excel fundamentals
- Data formatting and validation
- Sorting and filtering
- IF, SUMIF, COUNTIF, AVERAGEIF
- XLOOKUP and VLOOKUP
- INDEX and MATCH
- Text and date functions
- Conditional formatting
- Data cleaning
- Pivot Tables
- Pivot Charts
- Data visualization
- KPI preparation
- Interactive Excel dashboard
- Excel mini project
Mini-project: Interactive Excel business analytics dashboard.
Module 3 – SQL for Data Analytics
- Database fundamentals
- Tables, rows and columns
- Primary and foreign keys
- SELECT queries
- WHERE and ORDER BY
- GROUP BY and HAVING
- Aggregate functions
- JOINs
- Subqueries
- CASE expressions
- Date functions
- String functions
- Views
- Analytical queries
- SQL reporting project
Project: SQL-based business reporting and analytics system.
Module 4 – Python Programming
- Python installation and environment
- Variables and data types
- Operators
- Conditional statements
- Loops
- Strings
- Lists, tuples and dictionaries
- Sets
- Functions
- Modules
- File handling
- Exception handling
- OOP basics
- Working with CSV and Excel
- Python mini project
Mini-project: Python-based data processing application.
Module 5 – Python Data Analytics
- NumPy
- Pandas
- Series and DataFrame
- Importing datasets
- Missing value handling
- Duplicate removal
- Data transformation
- Filtering and grouping
- Merge and join
- Exploratory Data Analysis (EDA)
- Matplotlib
- Seaborn
- Statistical summaries
- Correlation analysis
- EDA project
Project: Complete Exploratory Data Analysis using Python, Pandas and visualization libraries.
Module 6 – Statistics for Data Analytics
- Mean, median and mode
- Variance and standard deviation
- Percentiles and quartiles
- Probability fundamentals
- Normal distribution
- Sampling
- Covariance
- Correlation
- Hypothesis testing basics
- Statistical case study
Case Study: Statistical analysis of a real-world business dataset.
Module 7 – Power BI & Business Intelligence
- Power BI introduction
- Importing Excel, CSV and SQL data
- Power Query
- Data cleaning
- Data modelling
- Relationships
- DAX fundamentals
- Measures
- Calculated columns
- KPI cards
- Charts and tables
- Slicers and filters
- Drill-down reports
- Interactive dashboards
- Power BI project
Project: Interactive Power BI business intelligence dashboard.
Module 8 – Machine Learning & AI
- Machine Learning fundamentals
- Supervised vs Unsupervised Learning
- Train/Test splitting
- Data preprocessing
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors
- Naive Bayes
- K-Means Clustering
- Model evaluation
- Accuracy
- Precision
- Recall
- F1-score
Project: Build and evaluate machine learning models using Scikit-learn.
Module 9 – Generative AI for Data Analysts
- Generative AI fundamentals
- Using AI for data analysis
- AI-assisted Excel and SQL
- AI-assisted Python coding
- Generating insights and report summaries
Practical: Use Generative AI tools to support data analysis, coding and business reporting.
Module 10 – Final Industry Project
Students will complete an end-to-end industry-oriented data analytics and AI project:
- Select business problem
- Collect dataset
- Clean and preprocess data
- Perform SQL/Python analysis
- Conduct EDA
- Build Power BI dashboard
- Apply a suitable ML model
- Present findings and business recommendations
Final Project: Complete portfolio-ready AI & Data Analytics solution.
Recommended Final Projects
- Retail Sales Analytics & Sales Prediction
- Hospital Patient Data Analytics
- Customer Churn Prediction
- Loan Default Prediction
- Employee Attrition Analytics
- E-commerce Customer Analytics
- Inventory & Stock Analytics
- Financial Performance Dashboard
- AI-Based Sales Forecasting
Course Outcomes
After completing this 120-day AI & Data Analytics program, students will be able to:
- Understand the complete data analytics lifecycle.
- Analyze and clean business data using Advanced Excel.
- Write SQL queries for data analysis and reporting.
- Develop Python programs for data processing.
- Perform data analysis using NumPy and Pandas.
- Perform Exploratory Data Analysis using Python.
- Create professional visualizations using Matplotlib and Seaborn.
- Apply statistical concepts to real-world datasets.
- Build interactive Power BI dashboards.
- Understand machine learning concepts and algorithms.
- Build and evaluate basic machine learning models.
- Use Generative AI tools for data analysis and reporting.
- Complete an end-to-end industry-oriented analytics project.
- Create a portfolio project suitable for job applications.
Tools & Technologies
- Microsoft Excel
- SQL
- Python
- Power BI
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Jupyter Notebook
- Generative AI Tools
Program Highlights
- 120 Days Structured Training
- Beginner to Advanced Learning Path
- Theory + Practical Training
- Real-world Datasets
- Hands-on Assignments
- Mini Projects
- Power BI Dashboard Projects
- Machine Learning Projects
- Generative AI for Data Analysis
- Industry-Oriented Final Project
- Portfolio Development
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