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B-SC-HONS in Data Science at GITAM (Gandhi Institute of Technology and Management)
Sangareddy, Telangana
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About the Specialization
What is Data Science at GITAM (Gandhi Institute of Technology and Management) Sangareddy?
This B.Sc (Hons) Data Science program at GITAM Hyderabad focuses on equipping students with a robust foundation in statistics, computer science, and machine learning principles essential for data-driven decision making. The curriculum is designed to meet the growing demand for skilled data professionals in the Indian industry, emphasizing practical application and theoretical understanding. It aims to develop versatile professionals capable of extracting insights from complex datasets.
Who Should Apply?
This program is ideal for fresh 10+2 graduates with a strong aptitude for mathematics and analytical thinking who are seeking entry into the rapidly expanding field of data science. It also caters to students who aspire to pursue higher education in AI, ML, or Data Analytics. A keen interest in programming, statistics, and problem-solving is a prerequisite for thriving in this challenging yet rewarding domain.
Why Choose This Course?
Graduates of this program can expect to pursue diverse career paths in India such as Data Analyst, Business Intelligence Developer, Machine Learning Engineer, or Data Scientist with starting salaries often ranging from INR 4-8 lakhs per annum, growing significantly with experience. The program aligns with industry needs, preparing students for roles in IT, finance, healthcare, and e-commerce sectors, contributing to India''''s digital transformation.
Student Success Practices
Foundation Stage
Master Python and Statistical Fundamentals- (Semester 1-2)
Dedicate early semesters to building a solid grasp of Python programming, including data structures and algorithms, alongside core statistical concepts. Utilize online platforms for coding practice and problem-solving, ensuring a strong base for advanced topics.
Tools & Resources
HackerRank, LeetCode, Kaggle, Coursera courses on Python/Statistics
Career Connection
Proficiency in Python and statistics forms the bedrock for almost all data science roles, enabling efficient data manipulation, analysis, and algorithm implementation.
Excel in Database Management- (Semester 1-2)
Develop strong skills in Database Management Systems (DBMS) and SQL. Practice designing, querying, and managing databases extensively. Participate in database-focused mini-projects to apply theoretical knowledge.
Tools & Resources
MySQL Workbench, PostgreSQL, SQLZoo, W3Schools SQL Tutorial
Career Connection
Robust database skills are crucial for data extraction and management, a daily task for any data professional, directly impacting readiness for data engineering and analysis roles.
Cultivate Logical and Analytical Thinking- (Semester 1-2)
Engage in competitive programming and mathematical puzzles to enhance logical reasoning and problem-solving abilities. Participate in university or inter-college hackathons focused on algorithmic challenges.
Tools & Resources
CodeChef, GeeksforGeeks, Project Euler
Career Connection
Strong analytical and problem-solving skills are paramount for dissecting complex data problems and designing effective data science solutions, making candidates highly desirable for technical roles.
Intermediate Stage
Build a Machine Learning Portfolio- (Semester 3-5)
Actively work on practical Machine Learning projects, starting with supervised and unsupervised learning, and build a portfolio on GitHub. Focus on understanding model evaluation metrics and hyperparameter tuning.
Tools & Resources
Scikit-learn, Keras/TensorFlow tutorials, GitHub, Kaggle competitions
Career Connection
A strong ML project portfolio demonstrates practical skills, which is a key differentiator in Indian tech recruitment, leading to roles as ML Engineer or Data Scientist.
Gain Exposure to Big Data Technologies- (Semester 4-5)
Explore and gain hands-on experience with Big Data tools like Hadoop and Spark. Understand distributed computing concepts and try implementing basic data processing pipelines.
Tools & Resources
Cloudera/Hortonworks sandboxes, Apache Spark documentation, Google Cloud Dataproc tutorials
Career Connection
Proficiency in Big Data technologies opens doors to Data Engineer and Big Data Analyst roles, highly sought after in enterprises dealing with large datasets.
Enhance Communication and Soft Skills- (Semester 3-5)
Actively participate in workshops on professional communication, presentation skills, and resume building. Seek opportunities for public speaking and group discussions to articulate technical concepts effectively.
Tools & Resources
Toastmasters International (local chapters), LinkedIn Learning courses, Campus career services
Career Connection
Effective communication is critical for presenting data insights to non-technical stakeholders and collaborating within teams, making you a well-rounded and impactful data professional.
Advanced Stage
Specialized Skill Development in Deep Learning/NLP/Cloud- (Semester 6-7)
Delve deeper into specialized areas like Deep Learning, Natural Language Processing, or Cloud Computing. Complete advanced projects, consider certifications (e.g., AWS Certified Cloud Practitioner), and contribute to open-source projects.
Tools & Resources
TensorFlow/PyTorch documentation, Hugging Face Transformers, AWS/Azure/GCP certifications
Career Connection
Specialization makes you a valuable asset for advanced roles in AI/ML research, cloud-based data solutions, or niche NLP applications within Indian R&D hubs and startups.
Undertake Impactful Internships and Capstone Projects- (Semester 7-8)
Secure a substantive internship in a relevant industry, focusing on a real-world problem. Leverage the final year capstone project to address a complex data science challenge, aiming for publishable results or a deployable solution.
Tools & Resources
LinkedIn Jobs, Internshala, Company career pages, Faculty advisors
Career Connection
Internships provide crucial industry experience and networking opportunities, often leading to pre-placement offers. A strong capstone project showcases your ability to lead and execute, highly valued by recruiters.
Prioritize Placement Preparation and Networking- (Semester 7-8)
Engage in mock interviews, aptitude tests, and resume reviews organized by the career services department. Actively network with alumni and industry professionals through conferences, webinars, and platforms like LinkedIn.
Tools & Resources
Campus Placement Cell, Glassdoor for interview experiences, LinkedIn for professional networking
Career Connection
Proactive placement preparation ensures you are interview-ready, while networking can open doors to opportunities not advertised publicly, accelerating your career launch in India''''s competitive job market.
Program Structure and Curriculum
Eligibility:
- Minimum 60% aggregate marks in 10+2 or Intermediate Examination from a recognized central / state board or its equivalent and Qualified in GITAM Admission Test (GAT) (UG Science) 2024. OR Students with a valid JEE (Main) score are exempted from GAT (UG Science) 2024.
Duration: 4 years (8 semesters)
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| GSO101 | Environmental Studies | Ability Enhancement Course (AEC) | 2 | Ecosystems and Biodiversity, Environmental Pollution and Control, Natural Resources and Conservation, Climate Change and Sustainable Development, Environmental Policies and Ethics |
| CSH101 | Introduction to Programming using Python | Program Core Course (PCC) | 3 | Python Fundamentals, Data Types and Operators, Control Flow Statements, Functions and Modules, Object-Oriented Programming Basics |
| CSH102 | Introduction to Programming using Python Lab | Program Core Course (PCC) | 1.5 | Python Programming Exercises, Debugging and Error Handling, File I/O Operations, Practical Problem Solving, Basic Data Structures Implementation |
| LAH101 | Technical English Communication | Language & Communication Skills Course (LCC) | 2 | Grammar and Vocabulary, Reading Comprehension, Professional Writing Skills, Presentation Techniques, Interpersonal Communication |
| MAH101 | Discrete Mathematics | Program Core Course (PCC) | 3 | Set Theory and Logic, Relations and Functions, Combinatorics and Probability, Graph Theory, Boolean Algebra |
| MAH102 | Statistics for Data Science | Program Core Course (PCC) | 3 | Descriptive Statistics, Probability Theory, Random Variables and Distributions, Inferential Statistics and Hypothesis Testing, Correlation and Regression |
| CSH103 | Data Analytics with Spreadsheets | Skill Enhancement Course (SEC) | 2 | Spreadsheet Functions and Formulas, Data Import and Cleaning, Data Visualization in Excel, Pivot Tables and Charts, Basic Data Analysis Techniques |
| CSH104 | Data Analytics with Spreadsheets Lab | Skill Enhancement Course (SEC) | 1 | Practical Spreadsheet Exercises, Advanced Formula Application, Dashboard Creation, Scenario and Goal Seek Analysis, Data Reporting and Presentation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH105 | Data Structures and Algorithms | Program Core Course (PCC) | 3 | Arrays, Linked Lists, Stacks, Queues, Trees and Graphs, Searching and Sorting Algorithms, Time and Space Complexity Analysis, Hashing Techniques |
| CSH106 | Data Structures and Algorithms Lab | Program Core Course (PCC) | 1.5 | Implementation of Data Structures, Algorithm Design and Analysis, Problem-Solving with DS&A, Recursion and Dynamic Programming, Performance Evaluation |
| CSH107 | Database Management Systems | Program Core Course (PCC) | 3 | Relational Model and SQL, Database Design and Normalization, Transactions and Concurrency Control, Storage and Indexing, Database Security |
| CSH108 | Database Management Systems Lab | Program Core Course (PCC) | 1.5 | SQL Querying and Manipulation, Database Creation and Management, Stored Procedures and Triggers, Query Optimization, ER Modeling and Schema Implementation |
| MAH103 | Linear Algebra for Data Science | Program Core Course (PCC) | 3 | Vectors and Matrices, Linear Transformations, Eigenvalues and Eigenvectors, Vector Spaces and Subspaces, Matrix Decompositions (SVD, PCA) |
| CSH109 | Web Design | Skill Enhancement Course (SEC) | 2 | HTML Structure and Elements, CSS Styling and Layout, JavaScript Fundamentals, Responsive Web Design, Web Development Tools |
| CSH110 | Web Design Lab | Skill Enhancement Course (SEC) | 1 | Building Static Web Pages, Implementing CSS Frameworks, Adding JavaScript Interactivity, Frontend Development Best Practices, Web Project Development |
| VDC101 | Value Added Course | Value Added Course (VAC) | 2 | |
| CSH111 | Computer Organization & Architecture | Program Core Course (PCC) | 3 | Digital Logic Circuits, CPU Design and Function, Memory Hierarchy, Input/Output Organization, Instruction Set Architecture |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH201 | Object Oriented Programming with Java | Program Core Course (PCC) | 3 | Java Basics and OOP Concepts, Classes, Objects, Inheritance, Polymorphism and Abstraction, Exception Handling, Collections Framework |
| CSH202 | Object Oriented Programming with Java Lab | Program Core Course (PCC) | 1.5 | Java Programming Exercises, Implementing OOP Principles, GUI Development with Java (basics), File I/O in Java, Debugging Java Applications |
| CSH203 | Operating Systems | Program Core Course (PCC) | 3 | Process Management and Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency, Operating System Structures |
| CSH204 | Introduction to R Programming | Skill Enhancement Course (SEC) | 2 | R Syntax and Data Types, Vectors, Matrices, Data Frames, Data Input/Output, Control Structures, Basic Statistical Functions in R |
| CSH205 | Introduction to R Programming Lab | Skill Enhancement Course (SEC) | 1 | R Scripting for Data Analysis, Data Manipulation with dplyr, Data Visualization with ggplot2, Statistical Modeling in R, Generating Reports with RMarkdown |
| MAH201 | Optimization Techniques for Data Science | Program Core Course (PCC) | 3 | Linear Programming, Non-linear Programming, Gradient Descent Algorithms, Convex Optimization, Optimization in Machine Learning |
| OE | Open Elective | Open Elective (OE) | 3 | |
| CSH206 | Computer Networks | Program Core Course (PCC) | 3 | OSI and TCP/IP Models, Network Topologies and Devices, Data Link Layer Protocols, Network Layer Protocols (IP, Routing), Transport Layer (TCP, UDP) |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH207 | Machine Learning | Program Core Course (PCC) | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Bias-Variance Tradeoff, Ensemble Methods |
| CSH208 | Machine Learning Lab | Program Core Course (PCC) | 1.5 | Implementing ML Algorithms with Scikit-learn, Data Preprocessing and Feature Engineering, Model Training and Hyperparameter Tuning, Practical ML Project Development, Visualization of Model Performance |
| CSH209 | Data Visualization | Skill Enhancement Course (SEC) | 2 | Principles of Data Visualization, Exploratory Data Analysis, Static and Interactive Visualizations, Tools: Matplotlib, Seaborn, Dashboard Design Basics |
| CSH210 | Data Visualization Lab | Skill Enhancement Course (SEC) | 1 | Creating Various Chart Types, Customizing Visualizations, Using Advanced Visualization Libraries, Storytelling with Data, Building Interactive Dashboards |
| CSH211 | Data Warehousing and Data Mining | Program Core Course (PCC) | 3 | Data Warehousing Concepts, ETL Processes, Data Mining Techniques, Association Rule Mining, Clustering and Classification in Data Mining |
| LAH201 | Professional Communication & Soft Skills | Language & Communication Skills Course (LCC) | 2 | Public Speaking and Presentation, Interview Skills and Group Discussions, Resume and Cover Letter Writing, Workplace Etiquette, Negotiation and Conflict Resolution |
| CSH212 | Research Methodology for Data Science | Skill Enhancement Course (SEC) | 2 | Research Design and Ethics, Data Collection Methods, Hypothesis Formulation and Testing, Statistical Analysis for Research, Technical Report Writing |
| PE | Program Elective | Program Elective Course (PEC) | 3 | |
| Project-I | Project-I | Project | 1 | Problem Identification and Scoping, Literature Review, Methodology Design, Initial Implementation, Project Documentation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH301 | Deep Learning | Program Core Course (PCC) | 3 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning Frameworks (TensorFlow/PyTorch), Transfer Learning |
| CSH302 | Deep Learning Lab | Program Core Course (PCC) | 1.5 | Building and Training Neural Networks, Image Classification with CNNs, Sequence Modeling with RNNs, Hyperparameter Tuning in DL, Utilizing GPUs for Deep Learning |
| CSH303 | Big Data Analytics | Program Core Course (PCC) | 3 | Big Data Concepts and Challenges, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for Big Data, NoSQL Databases, Distributed Data Processing |
| CSH304 | Big Data Analytics Lab | Program Core Course (PCC) | 1.5 | Hadoop and Spark Cluster Setup, Implementing MapReduce Jobs, Spark Data Processing, Working with NoSQL Databases, Big Data Querying Tools |
| CSH305 | Business Analytics | Program Core Course (PCC) | 3 | Descriptive Analytics, Predictive Analytics, Prescriptive Analytics, Business Intelligence Tools, Decision Support Systems |
| PE | Program Elective | Program Elective Course (PEC) | 3 | |
| OE | Open Elective | Open Elective (OE) | 3 | |
| INT | Summer Internship | Internship (INT) | 3 | Industry Exposure and Practical Application, Problem Solving in Real-world Scenarios, Professional Skill Development, Team Collaboration, Project Report and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH306 | Natural Language Processing | Program Core Course (PCC) | 3 | Text Preprocessing and Tokenization, Word Embeddings (Word2Vec, GloVe), Sentiment Analysis, Text Classification, Sequence-to-Sequence Models |
| CSH307 | Natural Language Processing Lab | Program Core Course (PCC) | 1.5 | Implementing NLP tasks with NLTK/spaCy, Building Chatbots, Text Summarization, Named Entity Recognition, Language Model Fine-tuning |
| CSH308 | Cloud Computing | Program Core Course (PCC) | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Virtualization Technologies, Cloud Security, Introduction to AWS/Azure/GCP |
| CSH309 | Cloud Computing Lab | Program Core Course (PCC) | 1.5 | Deploying Virtual Machines in Cloud, Managing Cloud Storage, Implementing Serverless Functions, Setting Up Cloud Networks, Using Cloud APIs |
| PE | Program Elective | Program Elective Course (PEC) | 3 | |
| OE | Open Elective | Open Elective (OE) | 3 | |
| Project II | Project II | Project | 1 | Advanced Project Design, Implementation with Modern Tools, Testing and Debugging, Comprehensive Documentation, Interim Presentation |
| CSH310 | Industry-Ready Skill Course | Skill Enhancement Course (SEC) | 2 |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CSH401 | Reinforcement Learning | Program Core Course (PCC) | 3 | Markov Decision Processes, Q-Learning and SARSA, Deep Reinforcement Learning, Policy Gradient Methods, Exploration vs Exploitation |
| CSH402 | Reinforcement Learning Lab | Program Core Course (PCC) | 1.5 | Implementing RL Algorithms, Using OpenAI Gym, Agent Training and Evaluation, Deep Q-Networks (DQNs), Solving Classic Control Problems |
| CSH403 | Block Chain Technology | Skill Enhancement Course (SEC) | 2 | Cryptography and Hashing, Distributed Ledger Technology, Consensus Mechanisms, Smart Contracts, Blockchain Platforms (Ethereum, Hyperledger) |
| CSH404 | Block Chain Technology Lab | Skill Enhancement Course (SEC) | 1 | Setting up Blockchain Development Environment, Smart Contract Programming (Solidity), Developing Decentralized Applications (DApps), Interacting with Blockchain Networks, Blockchain Security Practices |
| PE | Program Elective | Program Elective Course (PEC) | 3 | |
| PE | Program Elective | Program Elective Course (PEC) | 3 | |
| Project III | Project III | Project | 2 | Advanced Research and Development, System Integration and Deployment, Performance Evaluation and Optimization, Technical Report Writing, Final Project Presentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| INT/Project | Internship / Project Work | Internship/Project | 14 | Full-time Industry Internship, Capstone Project Development, Independent Research and Publication, Portfolio Building, Comprehensive Project Report and Viva |
| AEC | Professional Ethics & Human Values | Ability Enhancement Course (AEC) | 2 | Ethical Theories and Principles, Professionalism and Code of Conduct, Human Values and Virtues, Social Responsibility and Corporate Governance, Cyber Ethics and Data Privacy |
