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 Applied Data Science with Deep Learning

100% JOB Assured with Globally Accepted Certificate

Duration:460+60 Hrs

Eligibility: BE,CS, BCA

Course level: Intermediate

Modules

Foundational Modules
Database:

RDBMS using MySQL

Core Programming

Python Programming & Advanced Python
Problem Solving and Data Structures using Python

Certification – Python Programming

Data Analytics

Advanced Excel
Data Analysis & Reporting using Power BI
Exploratory Data Analysis with Pandas
Capstone project – Data Analysis

Certification – Diploma in Data Science with AIML

SPECIALIZATIONS

Machine Learning Database:

Machine Learning Fundamentals & Advanced ML

Advance AI

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

Certification – PG Diploma in Applied Data Science with Deep Learning

Experiential Project-Based Learning

An end-to-end machine learning model development using scikit-learn and real-world datasets

Projects

Apply statistical methods to solve various business problems, including banking and stock market analysis
Apply regression techniques to predict future flight prices
Apply classification techniques to categorize customers
Use clustering methods to segment banking customers
Computer vision projects such as face recognition and image quality improvement
Gesture recognition using image or video data
Gender identification using Streamlit

Platforms

Anaconda Distribution (Jupyter, Spyder, Google Colab, PyCharm)
MySQL
Microsoft Excel
Power BI
Libraries: Pandas, Matplotlib, Seaborn
ML Libraries: scikit-learn (sklearn), TensorFlow, PyTorch, NLTK

Core Programming
RDBMS using MySQL – 40 hrs. – 10 Days – 2 weeks
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
Learners Outcome
Design, query, and manage relational databases using SQL with transactions, procedures, and data integrity constraints.
Python Programming & Advanced Python – 80 hrs. – 20 Days – 4 weeks
Introduction to Python	Python Data Types and Conditions	Control Statements
Python Functions	Default arguments	Functions with a 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
Learners Outcome
Develop robust Python applications using core, advanced, and object-oriented concepts with real-world data handling.
Problem Solving and Data Structures using Python – 40 hrs. – 10 Days – 2 weeks
Time and Space Complexity	Utopian Tree	Viral Advertising
Birthday Cake Candles	Migratory Birds	Kaprekar Number
Pangram String and Anagram String	Palindrome Index	Array Rotation
Learners Outcome
Apply algorithmic thinking and complexity analysis to solve logical and competitive programming problems using Python.
Certification - Certification in Python Programming

Analytics Specialization

Advanced Excel – 40 hrs. – 10 Days – 2 weeks

Introduction to MS-Excel	Fill Series, Flash Fill	Logical Functions – IF, AND, OR, NOT, IF Error
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

Learners Outcome

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

Data Analysis & Reporting using Power BI – 40 hrs. – 10 Days – 2 weeks

Introduction to Power BI	Getting started with Power BI Desktop	Data modelling in Power BI
Creating visualization	Advanced data transformation	Power BI Dashboards
Data Visualization Best practices	Table and Conditional Formatting	Data Cleaning and Transformation

Learners Outcome

Build interactive dashboards and business reports using data modeling, visualization, and transformation techniques

Exploratory Data Analysis with Pandas – 40 hrs. – 10 Days – 2 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

Learners Outcome

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

Capstone project - Data Analysis – 20 hrs. – 5 Days – 1 week

Certification - Diploma in Data Science with AIML

AI Specialization

Machine Learning Fundamentals & Advanced ML – 80 hrs. – 20 Days – 4 weeks

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, 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

Deep Learning using TensorFlow – 40 hrs. – 10 Days – 2 weeks

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

Recurrent Neural Network (RNN)

Long- Short term Memory (LSTM)

Basic Open CV Functions

Learners Outcome

Design and implement deep learning models for computer vision and sequence-based applications.

Natural Language Processing – 20 hrs. – 5 Days – 1 week

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

Learners Outcome

Develop NLP solutions for text processing, representation, and semantic understanding.

Generative AI & Agentic AI – 40 hrs. – 10 Days – 2 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

Learners Outcome

Create and apply generative AI models, embeddings, and prompt-engineering techniques responsibly.

Capstone project – AI – 20 hrs. – 5 Days – 1 week

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FAQs

What is the duration of the PG Diploma in Data Science course?

The course duration is 6 months, covering both foundational and advanced data science modules.

Who is eligible to apply for the course?

The program is open to graduates in BE,CS,BCA from any engineering stream.

Is prior programming experience required?

Not mandatory, but basic knowledge of programming or mathematics/statistics is recommended for better understanding.

What topics are covered in the curriculum?

The course includes:

Python Programming & Data Structures
Data Analytics (Excel, Power BI, Pandas)
SQL & RDBMS
Machine Learning
Deep Learning & NLP
Generative AI & Prompt Engineering
Hands-on Project Work

What tools and platforms will I learn?

You will work with tools like:

Jupyter, Google Colab, PyCharm
MySQL, Excel, Power BI
Python Libraries: Pandas, NumPy, Matplotlib, Scikit-learn, TensorFlow, PyTorch, NLTK

Are the classes conducted online or offline?

The format is not explicitly stated. It’s best to contact Cranes Varsity directly to confirm if the mode is online, offline, or hybrid.

Is this a government-recognized or university-affiliated diploma?

While the program offers a "globally accepted certificate", specific government or university affiliations are not mentioned. You should inquire directly for accreditation details.