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B-TECH in Data Science at CHRIST (Deemed to be University)
Bengaluru, Karnataka
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About the Specialization
What is Data Science at CHRIST (Deemed to be University) Bengaluru?
This Data Science program at CHRIST, Bengaluru focuses on equipping students with deep knowledge in statistics, machine learning, and big data technologies. It is meticulously designed to meet the burgeoning demand for data professionals across various Indian industries, emphasizing practical application and cutting-edge methodologies. The curriculum offers a unique blend of theoretical foundations and hands-on experience, preparing graduates for complex data challenges.
Who Should Apply?
This program is ideal for aspiring engineers with a strong aptitude for mathematics and programming, seeking entry into the high-growth field of data science. It also caters to individuals aiming to pivot their careers into data analytics or machine learning, and fresh graduates eager to leverage their analytical skills for impactful business solutions in the Indian market.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Machine Learning Engineer, Data Analyst, or Business Intelligence Developer within India''''s thriving tech sector. Entry-level salaries typically range from INR 6-10 lakhs per annum, with experienced professionals earning significantly more. The curriculum aligns with certifications from platforms like Coursera and industry-recognized professional bodies, fostering rapid career growth within leading Indian and multinational companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python/C++)- (Semester 1-2)
Dedicate consistent effort to solidify core programming concepts in Python and C++, essential for data science. Regularly practice coding problems on platforms like HackerRank and LeetCode to build problem-solving abilities and algorithmic thinking. Focus on object-oriented programming principles and data structures.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL lectures
Career Connection
Strong programming skills are foundational for all data science roles. Proficiency here directly impacts performance in technical interviews and project development, paving the way for entry-level data analyst or junior developer positions.
Build a Strong Mathematical & Statistical Base- (Semester 1-3)
Actively engage with courses in Calculus, Linear Algebra, Probability, and Statistics. Supplement classroom learning with online tutorials and practice problems from textbooks. Understanding these concepts is crucial for comprehending advanced machine learning algorithms.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Mathematics), NPTEL
Career Connection
A solid quantitative background is indispensable for understanding the underlying mechanics of data science models, critical for roles requiring model development, validation, or research in AI/ML.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve assignments collaboratively, and clarify doubts. Explaining concepts to others reinforces your understanding. Participate in department-level coding clubs or hackathons for early exposure.
Tools & Resources
WhatsApp groups, Discord servers, University library resources, Coding clubs
Career Connection
Develops teamwork and communication skills, vital for corporate environments. Collaborative problem-solving prepares you for team-based projects in industry, enhancing your ability to contribute effectively from day one.
Intermediate Stage
Undertake Practical Data Science Projects- (Semester 3-5)
Beyond coursework, initiate personal projects or participate in hackathons focusing on real-world datasets. Apply concepts from Machine Learning, Databases, and Data Visualization. Document your projects thoroughly on platforms like GitHub to showcase your practical skills.
Tools & Resources
Kaggle, GitHub, Google Colab, Tableau Public
Career Connection
A strong project portfolio is key for demonstrating applied skills to recruiters. It showcases initiative, problem-solving, and the ability to translate theoretical knowledge into practical solutions, essential for internships and job applications.
Seek Early Industry Exposure through Internships- (Semester 4-6)
Actively look for short-term internships, even unpaid ones, during summer breaks or semester holidays in startups or smaller firms. This provides invaluable exposure to industry workflows, tools, and expectations in India''''s tech ecosystem.
Tools & Resources
Internshala, LinkedIn Jobs, AngelList India, Company career pages
Career Connection
Internships bridge the gap between academic learning and industry demands, enhancing your resume and building professional networks. Often, pre-placement offers (PPOs) are extended to successful interns, securing a job before graduation.
Specialize and Certify in Niche Areas- (Semester 4-6)
Identify specific areas within Data Science (e.g., Deep Learning, NLP, Big Data) that align with your interests. Pursue online courses or certifications from reputable platforms to deepen your expertise and gain a competitive edge.
Tools & Resources
Coursera (Andrew Ng''''s Deep Learning Specialization), edX, Udemy
Career Connection
Specialized certifications validate your skills to potential employers, particularly for roles requiring expertise in specific domains. This helps in targeting niche, high-demand data science positions in India and globally.
Advanced Stage
Prepare for Placements with Focused Practice- (Semester 6-8)
Intensively practice coding interview questions, brush up on data structures and algorithms, and prepare for case studies relevant to data science roles. Work on communication skills for HR rounds and behavioral interviews. Attend campus placement drives diligently.
Tools & Resources
Interviews Q&A books, Mock interviews, LinkedIn networking, University Career Services
Career Connection
Directly impacts success in campus placements. Thorough preparation increases the likelihood of securing desirable job offers from top companies, initiating a strong career trajectory.
Develop a Professional Network- (Semester 6-8)
Attend industry workshops, seminars, and conferences (virtual or in-person) within India. Connect with professionals, alumni, and potential mentors on platforms like LinkedIn. Networking opens doors to opportunities and provides industry insights.
Tools & Resources
LinkedIn, Professional meetups (e.g., Data Science meetups in Bengaluru), Industry conferences
Career Connection
A strong professional network is crucial for job referrals, mentorship, and staying updated with industry trends, significantly aiding long-term career growth and opportunities in the competitive Indian job market.
Contribute to Open Source or Research- (Semester 7-8)
If possible, contribute to open-source data science projects or assist faculty in research papers. This demonstrates advanced technical skills, collaboration abilities, and a deeper understanding of theoretical concepts, which are highly valued.
Tools & Resources
GitHub, arXiv, University research labs
Career Connection
Showcases advanced capabilities and a passion for the field, making you stand out for roles in R&D, advanced analytics, or even pursuing higher education (MS/PhD) in leading Indian and international universities.
Program Structure and Curriculum
Eligibility:
- A pass in 10+2 with a minimum of 55% aggregate marks in Physics, Chemistry, and Mathematics (PCM) from any recognized Board in India. Candidates pursuing the International Baccalaureate (IB) diploma or A-levels must have Physics, Chemistry, and Mathematics at the required level. Selection is based on CUET/JEE Main/CHRIST (Deemed to be University) Entrance Test scores.
Duration: 8 semesters / 4 years
Credits: 170 Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22CPN110 | Programming for Problem Solving | Core Theory | 3 | Introduction to Programming, Conditional Statements and Loops, Functions and Arrays, Pointers and Structures, File Handling |
| U22CPL110 | Programming for Problem Solving Lab | Core Lab | 1 | C Programming Basics, Control Flow Implementation, Function and Array Exercises, Pointer and Structure Problems, File I/O Operations |
| U22MTN111 | Calculus and Linear Algebra | Common Core | 4 | Differential Calculus, Integral Calculus, Sequences and Series, Matrices and Determinants, Vector Spaces |
| U22PHN110 | Applied Physics | Common Core | 3 | Quantum Mechanics, Lasers and Fiber Optics, Electromagnetism, Semiconductor Physics, Dielectric and Magnetic Materials |
| U22PHL110 | Applied Physics Lab | Common Core Lab | 1 | Optics Experiments, Electronic Circuits, Semiconductor Device Characteristics, Magnetic Hysteresis, Ultrasonic Interferometer |
| U22ECN110 | Applied Chemistry | Common Core | 3 | Water Technology, Electrochemistry, Corrosion and its Control, Engineering Materials, Fuels and Combustion |
| U22ECL110 | Applied Chemistry Lab | Common Core Lab | 1 | Water Quality Analysis, Potentiometric Titration, Viscosity Measurements, Corrosion Rate Determination, pH Metry |
| U22ENH111 | English | Common Core | 2 | Grammar and Vocabulary, Reading Comprehension, Writing Skills, Listening and Speaking, Presentation Skills |
| U22MDN110 | Engineering Graphics | Common Core | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Development of Surfaces, AutoCAD Basics |
| U22MDN111 | Workshop Practice | Common Core | 2 | Carpentry, Fitting, Welding, Sheet Metal Operations, Foundry |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22CSN210 | Data Structures and Algorithms | Core Theory | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| U22CSL210 | Data Structures and Algorithms Lab | Core Lab | 1 | Array and List Implementations, Stack and Queue Operations, Tree and Graph Traversals, Sorting and Searching Practice, Algorithm Efficiency Analysis |
| U22CPN211 | Object Oriented Programming with C++ | Core Theory | 3 | Classes and Objects, Inheritance and Polymorphism, Encapsulation and Abstraction, Constructors and Destructors, Templates and Exception Handling |
| U22CPL211 | Object Oriented Programming with C++ Lab | Core Lab | 1 | Object-Oriented Design Principles, Inheritance Implementation, Polymorphism Concepts, Operator Overloading, File I/O with Objects |
| U22MTN211 | Differential Equations and Transform Techniques | Common Core | 4 | First Order Differential Equations, Higher Order Differential Equations, Laplace Transforms, Fourier Series, Partial Differential Equations |
| U22EEN210 | Basic Electrical and Electronics Engineering | Common Core | 3 | DC and AC Circuits, Semiconductor Devices, Digital Electronics, Transducers, Electrical Safety |
| U22EEL210 | Basic Electrical and Electronics Engineering Lab | Common Core Lab | 1 | Circuit Laws Verification, PN Junction Diode Characteristics, Transistor Amplifier, Logic Gates, Measurement of Electrical Quantities |
| U22CVN210 | Engineering Mechanics | Common Core | 3 | Statics of Particles, Rigid Bodies, Friction, Dynamics of Particles, Work and Energy |
| U22CVL210 | Engineering Mechanics Lab | Common Core Lab | 1 | Forces and Moments, Simple Lifting Machines, Trusses and Frames, Moments of Inertia, Friction Experiments |
| U22MDN210 | Constitution of India and Professional Ethics | Common Core | 2 | Indian Constitution Features, Fundamental Rights and Duties, Union and State Government, Engineering Ethics, Professional Responsibility |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22MTN311 | Probability and Statistics for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Hypothesis Testing, Regression Analysis, ANOVA |
| U22CSN311 | Database Management Systems | Core Theory | 3 | Relational Model, SQL Queries, Database Design (ER Model), Normalization, Transaction Management |
| U22CSL311 | Database Management Systems Lab | Core Lab | 1 | SQL DDL and DML, Joins and Subqueries, Stored Procedures, Database Connectivity, ER Diagram Tools |
| U22CEN311 | Data Communication and Networking | Core Theory | 3 | Network Topologies, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer Protocols, Transport Layer Services |
| U22CEL311 | Data Communication and Networking Lab | Core Lab | 1 | Network Device Configuration, Socket Programming, Packet Analysis, Routing Protocols, Network Simulation |
| U22DSN310 | Introduction to Data Science | Core Theory | 3 | Data Science Lifecycle, Data Collection and Preprocessing, Exploratory Data Analysis, Introduction to Machine Learning, Data Science Tools |
| U22DSL310 | Introduction to Data Science Lab | Core Lab | 1 | Python for Data Science, Numpy and Pandas, Data Cleaning Techniques, Matplotlib and Seaborn, Basic Model Building |
| U22CPN310 | Discrete Mathematics | Core | 3 | Set Theory, Logic and Proofs, Relations and Functions, Graph Theory, Combinatorics |
| U22MDN311 | Environmental Studies | Common Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources, Environmental Management, Sustainable Development |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22MTN411 | Applied Linear Algebra | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Matrix Decompositions, Applications in Data Science |
| U22CSN411 | Operating Systems | Core Theory | 3 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks and Synchronization |
| U22CSL411 | Operating Systems Lab | Core Lab | 1 | Shell Programming, Process Creation, CPU Scheduling Algorithms, Memory Allocation Algorithms, Synchronization Problems |
| U22DSN410 | Machine Learning | Core Theory | 3 | Supervised Learning, Unsupervised Learning, Model Evaluation Metrics, Feature Engineering, Ensemble Methods |
| U22DSL410 | Machine Learning Lab | Core Lab | 1 | Scikit-learn for Classification, Clustering Algorithms, Dimensionality Reduction, Hyperparameter Tuning, Model Deployment Basics |
| U22DSN411 | Big Data Technologies | Core Theory | 3 | Hadoop Ecosystem, MapReduce Framework, HDFS Architecture, Spark Basics, NoSQL Databases |
| U22DSL411 | Big Data Technologies Lab | Core Lab | 1 | Hadoop Command Line, MapReduce Programming, Hive and Pig, Spark DataFrames, MongoDB Operations |
| U22CSN412 | Design and Analysis of Algorithms | Core | 4 | Algorithm Complexity, Divide and Conquer, Dynamic Programming, Greedy Algorithms, Graph Algorithms |
| U22HSE410 | Professional Communication | Common Core | 2 | Business Communication, Technical Report Writing, Presentation Skills, Interview Skills, Group Discussions |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22DSN510 | Deep Learning | Core Theory | 3 | Neural Network Architectures, Convolutional Neural Networks, Recurrent Neural Networks, Generative Models, Deep Learning Frameworks |
| U22DSL510 | Deep Learning Lab | Core Lab | 1 | TensorFlow/Keras Basics, Image Classification with CNNs, Sequence Modeling with LSTMs, GAN Implementations, Model Optimization Techniques |
| U22DSN511 | Data Visualization | Core Theory | 3 | Principles of Data Visualization, Static and Interactive Visualizations, Storytelling with Data, Dashboard Design, Visualization Tools |
| U22DSL511 | Data Visualization Lab | Core Lab | 1 | Matplotlib and Seaborn for Python, Plotly and Dash, Tableau/Power BI Basics, Geospatial Data Visualization, Creating Interactive Dashboards |
| U22DSN512 | Natural Language Processing | Core Theory | 3 | Text Preprocessing, Tokenization and Stemming, Word Embeddings, Sequence Models for NLP, Applications: Sentiment Analysis, Chatbots |
| U22DSL512 | Natural Language Processing Lab | Core Lab | 1 | NLTK and SpaCy, Building Text Classifiers, Named Entity Recognition, Machine Translation Concepts, Text Summarization |
| U22MTN511 | Optimization Techniques | Core | 3 | Linear Programming, Non-Linear Programming, Dynamic Programming, Heuristic Algorithms, Gradient Descent Methods |
| U22DSE | Department Elective - I (e.g., Exploratory Data Analysis) | Elective | 3 | Data Cleaning, Missing Value Imputation, Outlier Detection, Feature Scaling, Data Transformation |
| U22CPN511 | Universal Human Values | Common Core | 2 | Self-Exploration, Harmony in the Family, Harmony in Society, Harmony in Nature, Professional Ethics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22DSN610 | Cloud Computing for Data Science | Core Theory | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Big Data on Cloud (AWS, Azure, GCP), Serverless Computing, Cloud Security |
| U22DSL610 | Cloud Computing for Data Science Lab | Core Lab | 1 | AWS S3, EC2, Lambda, Azure Blob Storage, VMs, GCP Storage, Compute Engine, Containerization with Docker, Orchestration with Kubernetes |
| U22DSN611 | Data Ethics and Privacy | Core | 3 | Ethical Principles in AI/ML, Data Privacy Regulations (GDPR, India''''s DPDP Bill), Fairness and Bias in Algorithms, Transparency and Accountability, Responsible AI Development |
| U22DSN612 | Reinforcement Learning | Core Theory | 3 | Markov Decision Processes, Q-Learning, Policy Gradient Methods, Deep Reinforcement Learning, Applications in Robotics and Games |
| U22DSL612 | Reinforcement Learning Lab | Core Lab | 1 | OpenAI Gym Environments, Implementing Q-Learning, Policy Gradients with TensorFlow, Exploration-Exploitation Strategies, Multi-Agent Reinforcement Learning |
| U22DSE | Department Elective - II (e.g., Computer Vision for Data Science) | Elective | 3 | Image Processing Basics, Feature Extraction, Object Detection, Image Segmentation, Facial Recognition |
| U22DS | Open Elective - I (e.g., Internet of Things) | Elective | 3 | IoT Architecture, Sensors and Actuators, IoT Communication Protocols, Cloud Platforms for IoT, IoT Security |
| U22INT610 | Internship | Core | 3 | Industry Problem Solving, Team Collaboration, Practical Skill Application, Professional Communication, Project Documentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22DSN710 | Data Stream Processing | Core Theory | 3 | Stream Processing Concepts, Apache Kafka, Apache Flink/Spark Streaming, Real-time Analytics, Complex Event Processing |
| U22DSL710 | Data Stream Processing Lab | Core Lab | 1 | Kafka Producer/Consumer, Flink/Spark Streaming Jobs, Real-time Dashboarding, Stream Data Transformation, Fault Tolerance in Streams |
| U22DSN711 | Time Series Analysis and Forecasting | Core | 4 | Time Series Components, ARIMA Models, Exponential Smoothing, Prophet Model, Forecasting Applications |
| U22DSE | Department Elective - III (e.g., MLOps) | Elective | 3 | ML Model Lifecycle, Experiment Tracking, Model Versioning, Deployment Strategies, Monitoring and Maintenance |
| U22DS | Open Elective - II (e.g., Entrepreneurship for Engineers) | Elective | 3 | Startup Ecosystem, Business Model Canvas, Market Analysis, Funding Strategies, Legal Aspects of Startups |
| U22PRJ710 | Major Project - I | Project | 6 | Problem Identification, Literature Survey, System Design, Methodology Development, Initial Implementation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| U22DSE | Department Elective - IV (e.g., Financial Data Analytics) | Elective | 3 | Financial Market Data, Algorithmic Trading, Risk Modeling, Fraud Detection, Portfolio Optimization |
| U22DSE | Department Elective - V (e.g., Data Warehousing and Mining) | Elective | 3 | Data Warehouse Architecture, ETL Processes, OLAP Cubes, Association Rule Mining, Clustering for Business Intelligence |
| U22PRJ810 | Major Project - II | Project | 7 | Advanced Implementation, Testing and Validation, Performance Evaluation, Result Analysis, Technical Report and Presentation |
