Data Science

Non IT Course

This comprehensive 120-hour curriculum provides a complete journey from data science fundamentals to advanced AI-powered applications. The…

AdminElevate
23
120h
0
(0)

This comprehensive 120-hour curriculum provides a complete journey from data science fundamentals to advanced AI-powered applications. The project-based approach ensures practical skills with industry-standard tools, while the extensive AI integration modules—including machine learning, deep learning, NLP, and generative AI—prepare students for the future of data science where AI assistants and autonomous systems are transforming the field . Students will graduate with a robust portfolio of 7+ projects demonstrating their expertise across the entire data science spectrum and will be prepared for roles such as Data Scientist, Machine Learning Engineer, and AI Specialist

What Will You Learn?

Master the complete data science pipeline from data acquisition and cleaning to model deployment and monitoring
Perform exploratory data analysis (EDA) using Python libraries to discover patterns, anomalies, and relationships in complex datasets
Build and evaluate machine learning models including regression, classification, and clustering algorithms to solve practical business problems
Implement deep learning architectures including neural networks, CNNs, and RNNs for advanced analytics applications
Process and analyze text data using Natural Language Processing (NLP) techniques for sentiment analysis and text classification
Leverage generative AI and large language models including prompt engineering, RAG, and agentic AI concepts
Deploy models to production using MLOps practices, Docker, and cloud platforms
Apply responsible AI principles including ethics, bias mitigation, and interpretability in data science workflows

Course Curriculum

Python Basics

Python setup: Anaconda, Jupyter Notebook, Google Colab
Variables, data types, and operators
Control flow: conditional statements (if-else) and loops (for, while)
Functions: definition, parameters, return values, lambda functions
Data structures: lists, tuples, dictionaries, sets
List and dictionary comprehensions
Error handling with try-except blocks

NumPy Essentials

NumPy arrays: creation and manipulation
Vectorization and broadcasting
Array indexing, slicing, and filtering
Mathematical operations and universal functions
Matrix calculations and linear algebra basics
Stacking arrays and array functions across axes

Pandas Introduction

Pandas Series and DataFrames
Importing data from CSV, Excel, and APIs
Basic DataFrame operations: selection, filtering, sorting
Handling missing data and data cleaning
Working with categorical data
Grouping and aggregation operations
Merging DataFrames (concat, merge)

Project 1: Python Data Manipulation Lab

Load multiple datasets using Pandas
Clean data by handling missing values and duplicates
Perform basic statistical analysis
Transform and reshape data for analysis
Document findings in Jupyter notebook

Advanced Pandas for EDA

Advanced data cleaning techniques
Handling outliers and anomalies
Feature engineering basics
Pivot tables and cross-tabulations
Time series data handling
Window functions and rolling statistics

Data Visualization with Matplotlib

Matplotlib fundamentals: line plots, bar charts, histograms
Figure customization: labels, titles, legends, colors
Subplots and complex layouts
Creating publication-quality figures

Statistical Visualization with Seaborn

Seaborn for statistical visualizations
Distribution plots (histograms, KDE, box plots)
Categorical plots (bar plots, boxen plots, violin plots)
Relationship plots (scatter plots, pair plots, heatmaps)
Regression plots and faceting

Interactive Visualization with Plotly

Plotly express for quick interactive charts
Dashboards with interactive elements
3D visualizations for complex data

Project 2: Comprehensive EDA on Real-World Dataset

Select a real-world dataset (e.g., housing, customer churn, retail sales)
Load and clean data using Pandas
Perform statistical analysis and summary
Create visualizations to uncover patterns
Document insights in Jupyter notebook with markdown
Present findings with actionable recommendations

Machine Learning Fundamentals

What is Machine Learning? Types: supervised, unsupervised, reinforcement learning
Machine learning workflow and project implementation
scikit-learn library introduction
Train-test split and cross-validation
Feature engineering techniques
Model evaluation metrics overview

Regression Models

Simple linear regression
Multiple linear regression
Polynomial regression
Regularization: Ridge, Lasso, ElasticNet
Evaluation metrics: MSE, RMSE, MAE, R-squared

Classification Models

Logistic regression
K-Nearest Neighbors (KNN)
Decision trees and its types
Naïve Bayes classification
Evaluation metrics: accuracy, precision, recall, F1-score, confusion matrix
Handling imbalanced datasets
ROC-AUC curves

Clustering & Unsupervised Learning

Clustering concepts and types
K-Means clustering and Elbow method
Hierarchical clustering
DBSCAN for density-based clustering
Cluster evaluation metrics

Model Evaluation & Selection

Cross-validation techniques (KFold, Stratified KFold)
Grid search for hyperparameter tuning
Model selection strategies
Overfitting and underfitting detection

Project 3: Predictive Modeling Project

Select a business problem (e.g., house price prediction, fraud detection, customer churn)
Perform EDA and feature engineering
Build multiple regression or classification models
Compare model performance using appropriate metrics
Select best model with hyperparameter tuning
Document methodology and results
Present findings with business recommendations

Ensemble Methods

Random Forest algorithm
Gradient Boosting (XGBoost, LightGBM, AdaBoost)
Bagging vs Boosting concepts
Voting and stacking classifiers
Feature importance analysis

Feature Engineering & Selection

Feature creation from existing data
Handling categorical variables (encoding)
Feature scaling and normalization
Feature selection techniques

Dimensionality Reduction

Curse of dimensionality
Principal Component Analysis (PCA)
t-SNE for visualization
Autoencoders for nonlinear reduction

Model Optimization & Regularization

Regularization techniques (L1, L2)
Hyperparameter optimization strategies
Bayesian optimization concepts
Cross-validation best practices

Project 4: Ensemble Learning & Feature Engineering

Build advanced models using ensemble methods
Implement comprehensive feature engineering
Apply dimensionality reduction techniques
Optimize models with grid search
Compare performance against baseline models
Document feature importance and model interpretability

Deep Learning Fundamentals

What is Deep Learning? Neural networks basics
Activation functions: sigmoid, tanh, ReLU
Forward and backward propagation
Deep Learning libraries: TensorFlow, Keras, PyTorch
Building Artificial Neural Networks (ANN)
Batch normalization and dropout
Performance metrics for ANN

Convolutional Neural Networks

CNN architecture and components
Convolutional layers, pooling, flattening
CNN for computer vision
Image classification with CNNs
Transfer learning with pre-trained models
OpenCV basics for image processing
Object detection concepts

Advanced Deep Learning Topics

Recurrent Neural Networks (RNN) for sequence data
Long Short-Term Memory (LSTM) networks
Autoencoders for anomaly detection
Generative Adversarial Networks (GANs) concepts
Transformer architecture introduction

Project 5: Image Classification System

Load image dataset (e.g., CIFAR-10, custom dataset)
Preprocess images using OpenCV
Build CNN from scratch for classification
Implement transfer learning with pre-trained model
Compare performance of different architectures
Deploy model with Streamlit for inference
Document results with confusion matrix and sample predictions

NLP Fundamentals

What is NLP? Applications and use cases
Text preprocessing pipeline
Tokenization, Stopwords removal
Stemming and lemmatization, Regular expressions for text cleaning
NLTK and spaCy libraries

Text Representation & Features

Bag of Words (BoW) model
TF-IDF (Term Frequency-Inverse Document Frequency)
N-grams (unigrams, bigrams)
Word embeddings: Word2Vec, GloVe
Sentence embeddings and FastText

Deep Learning for NLP

RNN architecture for sequence data
Bidirectional LSTMs
Encoder-decoder architecture
Attention mechanism fundamentals
Transformer models and BERT introduction
Text classification using ML and deep learning

Project 6: Sentiment Analysis Application

Load sentiment analysis dataset (movie reviews, tweets, product reviews)
Perform comprehensive text preprocessing
Build multiple models
Compare model performance using accuracy, precision, recall
Create Streamlit web interface for real-time sentiment prediction
Deploy to Hugging Face Spaces or Streamlit Cloud

Generative AI Fundamentals

Introduction to Generative AI: Rule-based vs Neural Generation
Large Language Models (LLMs): GPT, Claude, Llama, Gemini
Evolution from traditional ML to foundation models
Generative AI applications in business
GANs and VAEs for generative tasks
Ethics in Generative AI: bias, hallucinations, copyright

Prompt Engineering

Prompt engineering fundamentals
Zero-shot, few-shot, and chain-of-thought prompting
System prompts vs user prompts
Structured prompting for consistent outputs
Common prompting mistakes and how to avoid them

RAG & LangChain

Retrieval-Augmented Generation (RAG) architecture
Vector databases and embeddings
LangChain for LLM orchestration
Building chatbots with memory
Tool calling and function execution

Agentic AI & MLOps for Generative AI

Introduction to Agentic AI concepts
Multi-agent systems and orchestration
AI developer tools: GitHub Copilot, Cursor.ai
Model deployment with Docker and cloud platforms
Monitoring and maintaining LLM applications

Project 7: GenAI-Powered Application

Build an application using generative AI
Document architecture, prompt strategies, and limitations
Include ethical considerations and bias mitigation

Final Project: Complete Data Science Solution

Phase 1: Problem Definition & Data Acquisition
Phase 2: Exploratory Data Analysis
Phase 3: Feature Engineering & Modeling
Phase 4: Model Evaluation & Interpretation
Phase 5: Deployment & Communication

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Data Science

What Will You Learn?

Course Curriculum

Python Basics

Python setup: Anaconda, Jupyter Notebook, Google Colab

Variables, data types, and operators

Control flow: conditional statements (if-else) and loops (for, while)

Functions: definition, parameters, return values, lambda functions

Data structures: lists, tuples, dictionaries, sets

List and dictionary comprehensions

Error handling with try-except blocks

NumPy Essentials

NumPy arrays: creation and manipulation

Vectorization and broadcasting

Array indexing, slicing, and filtering

Mathematical operations and universal functions

Matrix calculations and linear algebra basics

Stacking arrays and array functions across axes

Pandas Introduction

Pandas Series and DataFrames

Importing data from CSV, Excel, and APIs

Basic DataFrame operations: selection, filtering, sorting

Handling missing data and data cleaning

Working with categorical data

Grouping and aggregation operations

Merging DataFrames (concat, merge)

Project 1: Python Data Manipulation Lab

Load multiple datasets using Pandas

Clean data by handling missing values and duplicates

Perform basic statistical analysis

Transform and reshape data for analysis

Document findings in Jupyter notebook

Advanced Pandas for EDA

Advanced data cleaning techniques

Handling outliers and anomalies

Feature engineering basics

Pivot tables and cross-tabulations

Time series data handling

Window functions and rolling statistics

Data Visualization with Matplotlib

Matplotlib fundamentals: line plots, bar charts, histograms

Figure customization: labels, titles, legends, colors

Subplots and complex layouts

Creating publication-quality figures

Statistical Visualization with Seaborn

Seaborn for statistical visualizations

Distribution plots (histograms, KDE, box plots)

Categorical plots (bar plots, boxen plots, violin plots)

Relationship plots (scatter plots, pair plots, heatmaps)

Regression plots and faceting

Interactive Visualization with Plotly

Plotly express for quick interactive charts

Dashboards with interactive elements

3D visualizations for complex data

Project 2: Comprehensive EDA on Real-World Dataset

Select a real-world dataset (e.g., housing, customer churn, retail sales)

Load and clean data using Pandas

Perform statistical analysis and summary

Create visualizations to uncover patterns

Document insights in Jupyter notebook with markdown

Present findings with actionable recommendations

Machine Learning Fundamentals

What is Machine Learning? Types: supervised, unsupervised, reinforcement learning

Machine learning workflow and project implementation

scikit-learn library introduction

Train-test split and cross-validation

Feature engineering techniques

Model evaluation metrics overview

Regression Models

Simple linear regression

Multiple linear regression

Polynomial regression

Regularization: Ridge, Lasso, ElasticNet

Evaluation metrics: MSE, RMSE, MAE, R-squared

Classification Models

Logistic regression

K-Nearest Neighbors (KNN)

Decision trees and its types

Naïve Bayes classification

Evaluation metrics: accuracy, precision, recall, F1-score, confusion matrix