B.Tech. in
Computer Science Engineering Specialization in Data Science (Major) & Cyber Security (Minor)
In Collaboration with
Course Highlights
Introduction
Bachelor of Technology in Computer Science Engineering with a specialization in Data Science is one of the most preferred fields in the domain of computer science.
The program aims to make the students master the intricacies of Data Science, which deals with analytics, statistics, computational physics, algebra, CS, and other relevant subjects.
This emerging field of data science stands at one of the highest-paid
fields in the market, with large corporate global firms like Google,
Microsoft, Facebook, Twitter, Instagram, Cisco, Amazon, Oracle hiring people with a similar background.
In this data-driven world, the program strives to build students with key skills in machine learning, statistics, visualization, etc. which is
ultimately beneficial to identify meaning from huge volumes of data to make informed decisions in technology, science, business, etc.
Eligibility
Passed 10+2 examination with Physics, Mathematics, and one of the following subjects:
Chemistry / Computer Science / Electronics / Information
Technology / Biology / Informatics Practices / Biotechnology /
Technical Vocational Subject / Agriculture / Engineering
Graphics / Business Studies / Entrepreneurship.
Obtained at least 45% marks (40% for candidates belonging to
reserved categories) in the above subjects taken together.
OR
Passed D.Voc. stream in the same or allied sector.
The university may offer suitable bridge courses such as
Mathematics, Physics, Engineering Drawing, etc., for students
coming from diverse backgrounds to ensure a level playing
field and desired learning outcomes of the program.
Note: Physics and Mathematics are mandatory subjects at the
10+2 level.
Duration
4 Years Full-Time
Tuition Fees
Rs. 98,000 per Semester
*Fees such as Admission, Caution Money, Examination, Hostel, and Transport fees are extra.
Program Details
Statistical Analysis & Inference
Learning statistical methods to collect, analyze, interpret, and draw conclusions from data, enabling evidence-based decision-making.
Machine Learning & Predictive Modeling
Building models that learn patterns from historical data to make accurate predictions and classifications.
Data Wrangling & Preprocessing
Techniques to clean, transform, and prepare raw data from various sources for analysis and modeling.
Big Data Technologies
Working with large-scale data using tools like Hadoop, Spark, and distributed computing systems for efficient storage and processing.
Data Visualization & Communication
Conveying insights through charts, dashboards, and reports using tools like Tableau, Power BI, or Python libraries (Matplotlib, Seaborn).
Database Systems & Data Engineering
Understanding relational and non-relational databases, SQL, and data pipeline design to manage structured and unstructured data.
Domain-Specific Analytics
Applying data science in fields such as finance, healthcare, marketing, and social sciences to solve real-world problems.
Program Outcome
Data Literacy and Analytical Thinking
Graduates will be able to collect,
explore, clean, and interpret data
from diverse sources to uncover
meaningful insights and solve
real-world problems.
Proficiency in Statistical and Machine Learning Methods
Apply core statistical techniques
and machine learning models to
build predictive and classification
systems for decision-making.
Programming and Tool Mastery
Demonstrate practical skills in
programming (Python, R, SQL)
and proficiency in tools and
platforms such as Jupyter,
Hadoop, Spark, and Power BI.
Big Data Handling and Cloud Integration
Design scalable solutions using
big data technologies and cloud
platforms to manage and process
large datasets efficiently.
Data Engineering and Pipeline Development
Build and manage robust data
pipelines using database systems,
ETL processes, and modern data
architectures.
Effective Communication of Data Insights
Visualize data using dashboards
and storytelling techniques to
communicate findings clearly to
both technical and non-technical
audiences.
Ethical Data Use and Privacy Awareness
Understand the ethical
implications, data governance
standards, and privacy concerns
associated with data science
applications
Research and Innovation Orientation
Encourage innovation through
data-driven research and hands-
on projects in domains like
healthcare, finance, e-commerce,
and social sciences
Team Collaboration and Leadership
Work effectively in
interdisciplinary teams and
demonstrate leadership qualities
in collaborative data science
projects
Lifelong Learning and Adaptability
Stay updated with evolving
technologies and methodologies
in the data science ecosystem
through continuous learning.
Placement Opportunities
Data Scientist
Leverages statistical analysis and
machine learning to extract
actionable insights from large
datasets and build predictive
models.
Data Analyst
Interprets structured data to help
businesses make informed decisions
through dashboards, reports, and
trend analysis.
Machine Learning Engineer
Designs and deploys machine
learning models into production
environments to automate complex
tasks and processes
Business Intelligence (BI) Analyst
Uses data visualization tools and
reporting techniques to track
business performance and provide
strategic recommendations.
Data Engineer
Builds and manages the
infrastructure and data pipelines
required for collecting, storing, and
processing large-scale data.
Quantitative Analyst (Quant)
Applies mathematical models and
algorithms in finance or trading to
assess risk and identify investment
opportunities.
AI/ML Research Associate
Roles: Quant Analyst, Fraud Detection Analyst, Risk Modeling Engineer
Companies: JPMorgan Chase, Goldman Sachs, Paytm, Razorpay
Skills Needed: Python, Financial Modelling, ML algorithms, Risk Assessment tools
Data Consultant
Advises organizations on data
strategy, implementation of data-
driven solutions, and analytics
transformations.
Product Analyst
Analyzes user behavior, market
trends, and product performance to
support data-driven product
development and improvements.
Risk Analyst
Identifies and evaluates risks using
data modeling and simulations,
particularly in sectors like finance,
insurance, and cybersecurity.
Lab List for DS Specialization
1.Programming for Data Science Lab
Python and R basics
Data structures, functions, and scripting
Jupyter Notebooks and data manipulation
2.Statistics and Probability Lab
Descriptive and inferential statistics
Probability distributions
Hypothesis testing using tools like R/Python
3.Data Wrangling & Preprocessing Lab
Handling missing data, outliers
Feature scaling, encoding techniques
Pandas, NumPy, and data pipelines
4.Database Management Systems Lab
SQL queries, joins, indexing
Relational schema design
NoSQL (MongoDB basics)
5.Machine Learning Lab
Supervised and unsupervised algorithms
Scikit-learn, model evaluation, cross-validation
Mini projects using real-world datasets
6.Big Data & Hadoop Lab
HDFS commands and MapReduce
Introduction to Apache Hive and Pig
Spark for distributed processing
7.Data Visualization Lab
Plotting with Matplotlib, Seaborn, Plotly
Creating dashboards in Tableau/Power BI
Storytelling with data
8.Cloud Computing for Data Science Lab
Working with AWS, GCP , or Azure
Cloud storage, compute instances
Deploying ML models on the cloud
9.Data Engineering Lab
10.Building ETL pipelines
11.Stream processing with Kafka
12.Airflow and data workflow orchestration
13.Capstone Project Lab
14.Real-world datasets
15.End-to-end data science lifecycle
·Report writing and model deployment
Where Talent Meets Opportunity:
Our Graduates Are Building Futures with Leading Companies
