Next-Gen
Data Science with
Generative AI

Data visualisation using excel

Charts and visualization techniques

Data visualisation best practices

Pivot charts

Building interactive dashboard

Data analysis using SQL

Introduction to database

Selection, filtering & ordering

Data types, operators and functions

Aggregate and group By commands

Data manipulation with SOL

Conditional and case statements

Working with date/timestamps

Joins

Subqueries

Advanced data manipulation with SOL

Common table expression (CTE)

Window functions in SOL

SET operators

Data preparation & exploration with tableau

Loading data on tableau

Data cleaning & preparation

Techniques

Aggregation and logical functions

Filters & analytics in tableau

Data visualisation with tableau

Understanding chart types

Creating charts in tableau

LOD and calculated fields

Creating dashboards & stories

Introduction & data handling basics

What is power BI? overview & components (power BI desktop, service, mobile)

Installing power BI desktop

Understanding the interface & workflow

Connecting to data Sources (Excel, CSV, Web, SQL, etc.)

Data loading & transformation using power query editor

Data modelling & relationships

Data types and column vs measure

Building relationships between tables

Creating star & snowflake schema

Introduction to DAX (Data Analysis Expressions)

Common DAX (SUM, AVERAGE, COUNT, CALCULATE, etc.)

Data visualization basics

Creating Visuals: Tables, Cards, Column Charts, Bar Charts, Pie Charts

Using slicers, filters, and drill through

Sorting, formatting, and customizing visuals

Managing interactions between visuals

Bookmarking and button navigation

Advanced DAX & Custom Visualizations

Advanced DAX: Time Intelligence (YTD, MTD, QoQ, YoY)

IF, SWITCH, RANKX, FILTER functions

Creating calculated tables and columns

Tooltips and advanced Charts (Donut, Funnel, Gauge, KPI)

Conditional formatting and custom Tooltips

Power BI service & publishing

Publishing reports to power BI service

Creating and managing dashboards

Scheduling data refresh

Sharing and collaboration

Workspace and apps in power BI

Real-world projects & deployment

Hands-on capstone project (Sales Dashboard, HR Analytics, Finance Dashboard)

Best practices in dashboard design

Resume building & portfolio showcase

Interview prep: common power BI questions

Integration with excel and powerpoint for reporting

Programming in python

Dictionaries

Conditional statements

Loop and control flow

Functions

Data analysis using python

Mathematical computing using numPy

Data manipulation using pandas

Exploratory data analysis using pandas

Confirmatory data analysis in python

Data visualisation using python

Introduction to python libraries

Creating plots with matplotlib

Visualisation using seaborn

Interactive visualisation with plotly

Interview prep & portfolio building

Interview preparation

Resume building

Portfolio building

Graduation test & projects

Data Visualization

Matplotlib & Seaborn for visual analytics

Creating line, bar, scatter, box, and heatmap plots

Hands-on: Data storytelling with visualization

Python for Data Science

Python Basics for Data Science

Introduction to Python

Working with Jupyter Notebook and Google Colab

Python Libraries for Data Science

NumPy - Arrays, matrix operations, broadcasting

Pandas - Series, DataFrames, data cleaning, data wrangling

Exploratory Data Analysis (EDA)

Handling missing values and outliers

Feature transformation and correlation analysis

Mini Project: EDA on COVID-19 or Sales Dataset

Statistics for Data Science

Mean, Median, Mode, Variance, Standard Deviation

Probability Distributions (Normal, Binomial, Poisson)

Data Sampling and Visualization

Web Scraping & APIS

Hypothesis Testing, t-test, Chi-square, ANOVA

Confidence Intervals and P-Values

Correlation and Regression Concepts

Statistics in Python

Using Python (SciPy, Statsmodels) for statistical analysis

Project: A/B Testing for Marketing Campaigns

Machine Learning Foundations

What is ML? ML pipeline and workflow

Data Preprocessing, Feature Engineering, Encoding, and Scaling

Regression Models

Linear Regression, Polynomial Regression, Ridge/Lasso Regression

Evaluation Metrics (MSE, RMSE, R2)

Project: House Price Prediction

Classification Models

Logistic Regression, Decision Trees, Random Forest

Model Evaluation: Confusion Matrix, Precision, Recall, F1 Score

Advanced Algorithms

SVM (Support Vector Machine)

KNN (K-Nearest Neighbors)

Naive Bayes Classifier

Unsupervised Learning

Clustering (K-Means, Hierarchical)

Dimensionality Reduction (PCA, t-SNE)

Recommendation Systems

Collaborative Filtering & Content-Based Filtering

Project: Movie or Product Recommendation System

Reinforcement Learning (Intro)

Q-Learning Basics

Practical understanding with simple game environments

Machine Learning Project

Capstone Project 1: End-to-End ML Model Deployment

(e.g., Predictive Analytics Dashboard using Flask/Streamlit)

Natural Language Processing

Text Preprocessing: Tokenization, Stopwords, Lemmatization

Bag of Words, TF-IDF, Word2Vec

NLP Applications

Named Entity Recognition (NER)

Sentiment Analysis using ML

Project: Twitter Sentiment Analysis / Chatbot Basics

Neural Network Basics

Understanding Perceptron, Activation Functions, Backpropagation

Implementing ANN using Keras/TensorFlow

Convolutional Neural Networks (CNN)

CNN architecture and feature extraction

Image Classification using CNN

Transfer Learning

Using pre-trained models (VGG, ResNet, MobileNet)

Fine-tuning and feature reuse

Recurrent Neural Networks (RNN)

RNN architecture, LSTM, GRU models

Time Series Forecasting

Generative Models & GANs

Understanding Generative Adversarial Networks

Project: Generate Synthetic Images using GAN

Introduction to Computer Vision

Basics of OpenCV for image processing (filters, edges, contours)

Real-time object detection

Deep Learning for Vision

Building CNN models with Keras

Working with VGG16 /VGG19 architectures

Project: Face Mask or Object Detection System

Real-Time Project 1

Rain Prediction System using ML

Techniques: Regression, Feature Engineering, Model Optimization

Real-Time Project 2

Sentiment Analysis using Multiple ML Algorithms

Comparative Model Evaluation

Final Capstone Project

House Price Prediction using ML + LSTM Integration

Full Model Development - Deployment - Presentation