Advanced Diploma in Data Science with AIML
Durations – 300 Hrs. -Offline
Modules
Foundational Modules
Database:
- RDBMS using MySQL
- Python Programming & Advanced Python
- Problem Solving and Data Structures using Python
Data Analytics
- Exploratory Data Analysis with Pandas
AI SPECIALIZATIONS
Machine Learning 120 hrs
- Machine Learning Fundamentals & Advanced ML
- System Design & Deployment (MLOps)
Advance AI 120 hrs.
- Deep Learning using TensorFlow
- Natural Language Processing
- Generative AI & Agentic AI
Duration Break up:
- Training – 50 days
- Project & Assessment & Technical Mock – 10 Days
Project
- Apply Statistical Methods for Business Decision Making (Banking, Stock Market, Business Problems)
- Apply Regression to Predict Future Flight Prices
- Apply Classification to Classify Customers
- Use Clustering Techniques to Segment Banking Customers
- Computer Vision Projects (Face Recognition, Image Quality Improvement)
- Gesture Recognition Using Image or Video Data
- Gender Identification Application using Streamlit
Software & Framework required
- Anaconda Distribution: Jupyter, Spyder, Google Colab, PyCharm
- MySQL
- Libraries: Pandas, Matplotlib, Seaborn
- ML Libraries: scikit-learn, TensorFlow
Experiential Project Based Learning
An end-to-end machine learning model
development using scikit-learn and real-world
datasets.
Core Programming
RDBMS using MySQL – 40 hrs. – 7 days – 1 week ( Online )
| Introduction to databases and RDBMS, | Database creation, concept of relation and working examples | Creating tables. Design view of the table, Alter table operations & Key Constraints |
| Read, update and delete operations on tables. Working with nulls | Querying tables: Select statement, examples and its variations | Filtering, Sorting, Predicates and working examples |
| Joins in SQL and working examples | Insert, Update, delete operations and working examples | Scalar functions in SQL and working examples |
| SQL set based operations and data aggregation Sub-queries in SQL |
Normalization and de-normalization: Views and Temporary tables Transactions in SQL |
SQL programming Creating stored procedures, Cursors in SQL |
| Assessment – Module Test – MCQ, Theory | Technical Mock |
Python Programming & Advanced Python – 72 hrs. – 12 days – 2 weeks
| Introduction to Python | Python Data types and Conditions | Control Statements |
| Python Functions | Default arguments | Functions with variable number of args |
| Scope of Variables | Global specifier | Working with multiple files |
| List and Tuple | List Methods | List Comprehension |
| Map and filter functions | String | List comprehension with conditionals |
| Set and dictionary | Exception Handling | File Handling |
| Object Oriented Programming | Overloading Operator | Inheritance |
| Regular Expression | Finding Patterns of Text | Meta characters |
| Testing Fundamentals | Unit Testing | Working with JSON |
| Decorators | Iterators | Generators |
Problem Solving and Data Structures using Python – 18 hrs. – 3 Days – 0.5 weeks
| Time and Space Complexity | Utopian Tre | Viral Advertising |
| Birthday Cake Candles | Migratory Birds | Kaprekar Number |
| Pangram String and Anagram String | Palindrome Index | Array Rotation |
| Project- Intermediate project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
| Data Analytics | ||
| Exploratory Data Analysis with Pandas – 36 hrs. – 6 Days – 1 weeks | ||
| NumPy | Vectorization | Broadcasting |
| Slicing of Matrices | Filtering | Array Creation Functions |
| NumPy Functions across axis | Stacking of arrays | Matrix Calculation |
| Pandas Series | Data Cleaning | Handling Missing Data |
| Pandas Data frame | Selection Data (loc, iloc) | Filtering Data Frames |
| Working with Categorical Data | Grouping & Aggregation | Merging Data Frame (concat, merge) |
| Sorting Data Frames | Importing csv files | Importing Excel Files |
| Creating graphs using Matplotlib | Customizing Plots | Seaborn, PlotLy |
| Project- Advanced project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
AI Specialization
Machine Learning Fundamentals & Advanced ML – 60 hrs. – 10 days – 1.5 weeks
Introduction to Machine Learning
Regression
Logistic regression
Supervised machine learning
Simple linear regression
Naïve Bayes Classification
Unsupervised machine learning
Multiple linear regression
Decision trees and its types
Train test split the data
Performance measure for regression
K Nearest Neighbor Classification
ML Workflow for project implementation
Classification and types
Performance Measure for Classification
Random Forest
Clustering and types
Evaluate clustering results, Elbow Plot
Optimizing regression models with forward elimination, grid search cv
Improving classification models with Ensemble modeling
Model evaluation strategies (KFold, Stratified KFold)
Regularization L1 and L2 regularization
Bagging
Boosting techniques: ADA boost
Hyperparameter Tuning, SVM
Stacking and Voting
Dimensionality Reduction with PCA
Project - Advanced project & Demo
Assessment – Module Test – MCQ, Theory
Technical Mock
ML System Design & Deployment (MLOps) – 30 hrs - 5 Days - 1 week (online)
MLOps Fundamentals
Reproducible Project Setup
Data Versioning & Validation
Experiment Tracking & Model Registries
Feature Store Fundamentals
Automating with CI/CD Pipelines
Model Deployment Patterns
Advanced Deployment Strategies
Production Model Monitoring
ML Infrastructure & Scaling
ML Pipeline Orchestration
End-to-End MLOps Project
Assessment – Module Test – MCQ, Theory
Technical Mock
Advance AI
Deep Learning using TensorFlow - 36 hrs. - 6 Days - 1 week
What is Deep Learning
Performance measure for ANN
Building project based on CNN
Deep Learning Methods
Need for Hardware's in Deep Learning
Need for Data augmentation
Deep Learning Application
Basics of image processing
Batch Normalization, dropout
Artificial Neural Network
Opencv library
Object detection with CNN
hidden Layers
Image reading, writing, enhancement
Object recognition with CNN
Activation Function
Edge detection, filtering, morphology
Forward and Backward propagation
| CNN for computer vision | CNN architecture and its types | Tensorflow, PyTorch, Keras |
| Project - Advanced project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
| Recurrent Neural Network (RNN) | Long-Short term Memory (LSTM) | Basic Open CV Functions |
Natural Language Processing – 36 hrs. 6 Days - 1 weeks
| Introduction to NLP | NLP: Areas of Application | Understanding the Text |
| Text Encoding | Word frequencies and stop words | Bag of words representation |
| Stemming and Lemmatization | TF-IDF representation | Canonicalisation |
| Phonetic Hashing | Spell Corrector | Point wise mutual Information |
| Gensim, Word2Vec | Word Embeddings | Named Entity Recognition (NER) and Parts of Speech Tagging |
| Dependency Parsing and Syntactic Analysis | Semantic Similarity and Sentence Embeddings | Bidirectional LSTM |
| Project - Advanced project & Demo | Assessment – Module Test – MCQ, Theory | Technical Mock |
Generative AI & Agentic AI - 36 hrs - 6 Days - 1 weeks
| Introduction to Gen AI | Rule-based vs neural generation | Generative Adversarial Network |
| Variable Auto Encoder | Transformers | Application of Generative AI, Ethics |
| FastText and subword models | Sentence embeddings and similarity | Encoding long text documents |
| Visualizing embeddings with tools | Prompt Engineering | Zero_shot and few_shot prompts |
| Chain-of-thought prompting style | System and user prompts | Common prompt engineering mistakes |
| Agentic AI | Application of Agentic AI | Developing an AI Agent |
| Project - Application Development | Assessment – Module Test – MCQ, Theory | Technical Mock |
