How is Cranes Varsity PG diploma in Data Science and Machine Learning program?

The PG diploma in data science by Cranes Varsity is a 5-month program designed specifically for working professionals to develop practical knowledge and skills, and accelerate their data science careers.

What are the career opportunities in Data Science?

Career opportunities are very high with high pay packages compared to other domains, Roles like Data scientist, Data analyst, business analyst, Data analytics manager, Business intelligence manager and many other roles can be expected.

What are the prerequisites to learn Data Science?

Having prior knowledge of any programming language is an added advantage but not mandatory, and the basics of linear algebra are a must.

What are the prerequisites for Machine Learning Course enrollment?

Having a good knowledge of Python Programming concepts, and a good hold on linear algebra.

Can I learn Machine Learning Online?

Yes, you can learn Machine Learning Course completely online.

PG Diploma in Data Science | Data Science Courses In Bangalore

PG Diploma in
Artificial Intelligence & Generative AI

100% JOB Assured with Globally Accepted Certificate

Duration:460+60 Hrs

Eligibility: BE,CS, BCA

Course level: Intermediate

Program Outline

Core Programming

RDBMS using MySQL
Python Programming & Advanced Python
Capstone Project – Python

Analytics Specialization

Advanced Excel
Data Analysis & Reporting using Power BI
Exploratory Data Analysis with Pandas
Machine Learning Fundamentals & Advanced ML
Capstone Project – Data Analysis

Advanced AI Specialization

Deep Learning using TensorFlow
Natural Language Processing
Generative AI & Agentic AI
Capstone Project – AI

Tools / Software / Frameworks

Anaconda, Jupyter, Colab
MySQL Workbench
Excel, Power BI
Pandas, NumPy, Matplotlib
Scikit-learn
TensorFlow, PyTorch, Keras
NLTK, Gensim, Word2Vec

Capstone Projects

Business Data Analysis
Flight Price Prediction
Customer Classification
Customer Segmentation
Face Recognition & Image Processing
Gesture Recognition
Gender Detection System

Industry Job Roles

Data Analyst
BI Analyst
Python Developer
ML Engineer
Data Scientist
AI Engineer
Deep Learning Engineer
NLP Engineer
MLOps Engineer
Generative AI Engineer

Module 1 • RDBMS using MySQL 40 hrs

Key Skills: Databases · SQL queries · Joins · Stored procedures · Normalization · Transactions

Introduction to databases and RDBMS	Database creation, concept of relation and working examples	Creating tables, design view, 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	Normalization and de-normalization: Views and Temporary tables Transactions in SQL	SQL programming Creating stored procedures, Cursors in SQL

Learner Outcome: Design, query, and manage relational databases using SQL with transactions, procedures, and data integrity constraints.

Module 2 • Python Programming & Advanced Python 80 hrs

Key Skills: Data types · OOP · Functions · STL · File handling · Decorators · Generators

Introduction to Python	Python data types and conditions	Control statements
Python functions	Default arguments	Functions with a variable number of arguments
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	Operator overloading	Inheritance
Regular expressions	Finding patterns of text	Meta characters
Testing fundamentals	Unit testing	Working with JSON
Decorators	Iterators	Generators

Learner Outcome: Develop robust Python applications using core, advanced, and object-oriented concepts with real-world data handling.

Analytics Specialization & Machine Learning

Module 3 • Advanced Excel 40 hrs

Key Skills: Logical functions · VLOOKUP · Pivot tables · Dashboards · What-if analysis

Introduction to MS-Excel	Fill series and flash fill	Logical functions: IF, AND, OR, NOT, IFERROR
Text functions	Date functions	Statistical functions
VLOOKUP and H-Lookup	Index and match functions	Sorting and filtering data
Pivot table	Data validation	What-if analysis
Charting techniques in Excel	Interactive dashboard creation	Data analytics project using Excel

Learner Outcome: Perform data analysis, reporting, and dashboard creation using advanced Excel functions and analytical tools.

Module 4 • Data Analysis & Reporting using Power BI 40 hrs

Key Skills: Data modeling · DAX · Visualization · Dashboards · Data transformation

Introduction to Power BI	Getting started with Power BI Desktop	Data modelling in Power BI
Creating visualizations	Advanced data transformation	Power BI dashboards
Data visualization best practices	Table and conditional formatting	Data cleaning and transformation

Learner Outcome: Build interactive dashboards and business reports using data modeling, visualization, and transformation techniques.

Module 5 • Exploratory Data Analysis with Pandas 40 hrs

Key Skills: NumPy · Pandas · Matplotlib · Seaborn · Plotly · Data cleaning & aggregation

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 DataFrame	Selection data (loc, iloc)	Filtering data frames
Working with categorical data	Grouping & aggregation	Merging data frames (concat, merge)
Sorting data frames	Importing CSV files	Importing Excel files
Creating graphs using Matplotlib	Customizing plots	Seaborn, Plotly

Learner Outcome: Analyze, clean, and visualize large datasets using NumPy, Pandas, and Python visualization libraries.

Module 6 • Machine Learning Fundamentals & Advanced ML 80 hrs -20 Days -4 Weeks

Key Skills: Regression · Classification · Clustering · Ensemble methods · SVM · PCA · Hyperparameter tuning

Introduction to machine learning	Regression	Logistic regression
Supervised machine learning	Simple linear regression	Naïve Bayes Classification
Unsupervised machine learning	Multiple linear regression	Decision tress and its types
Train test split the data	Performance measure for regression	K Nearest Neighbour 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 and Grid Search CV	Improving classification models with ensemble modeling	Model evaluation strategies (KFold, Stratified KFold)
Regularization: L1 and L2	Bagging	Boosting techniques: ADA boost
Hyperparameter Tuning, SVM	Stacking and Voting	Dimensionality Reduction with PCA

Learner Outcome: Build optimize, and evaluate machine learning models using supervised, unsupervised, and ensemble techniques.

▶ Capstone Project • Capstone Project – Data Analysis · 20 hrs · End-to-end ML model development using Scikit-learn and real-world datasets

★ CERTIFICATION MILESTONE · Diploma in Data Science with AIML

Advanced AI Specialization

Module 7 • Deep Learning using TensorFlow 40 hrs

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
idden Layers	Image reading, writing, enhancement	Building projects based on CNN
Activation Function	Edge detection, filtering, morphology	Forward and Backward propagation
CNN for computer vision	CNN architecture and its types	TensorFlow, PyTorch, Keras
Recurrent Neural Network (RNN)	Long Short-Term Memory (LSTM)	Basic OpenCV functions

Learner Outcome: Design and implement deep learning models for computer vision and sequence-based applications using TensorFlow and PyTorch.

Module 8 • Natural Language Processing 20 hrs

Key Skills: Text encoding · TF-IDF · Word2Vec · NER · Dependency parsing · Sentence embeddings

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	Canonicalization
Phonetic hashing	Spell corrector	Pointwise 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

Learner Outcome: Develop NLP solutions for text processing, representation, and semantic understanding using classical and deep learning methods.

Module 9 • Generative AI & Agentic AI 40 hrs

Key Skills: VAE · GAN · Transformers · Prompt engineering · Zero/few-shot · Embeddings

Introduction to Generative AI	Rule-based vs neural generation	Generative Adversarial Networks (GAN)
Variational AutoEncoder (VAE)	Transformers	Applications of Generative AI and 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.