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M-TECH 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 Data Science program at Gandhi Institute of Technology and Management, Hyderabad, focuses on equipping students with advanced analytical skills and knowledge to extract insights from complex data. It covers foundational statistics, machine learning, deep learning, big data technologies, and visualization. The curriculum is designed to meet the growing demand for data scientists in the rapidly expanding Indian technology landscape, emphasizing both theoretical understanding and practical application.
Who Should Apply?
This program is ideal for engineering graduates (CSE, IT, ECE, EEE, ME, CE, AE) or postgraduates (M.Sc., MCA) with a strong mathematical and programming aptitude who aspire to build a career in data-driven roles. It caters to fresh graduates seeking entry into the data science field, as well as working professionals looking to upskill and transition into advanced analytics, machine learning engineering, or AI specialist roles within the Indian industry.
Why Choose This Course?
Graduates of this program can expect to pursue high-demand career paths such as Data Scientist, Machine Learning Engineer, Big Data Analyst, AI Specialist, or Business Intelligence Developer in India. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals commanding INR 15-30+ LPA in leading Indian tech companies and startups. The program fosters skills aligned with professional certifications in cloud platforms and specialized machine learning frameworks, enhancing growth trajectories.
Student Success Practices
Foundation Stage
Master Core Mathematical & Programming Fundamentals- (Semester 1-2)
Dedicate significant time to reinforce concepts in Linear Algebra, Probability, Statistics, and Python programming. Utilize online platforms like NPTEL, Coursera, and HackerRank to practice problems related to data structures and algorithms, which are crucial for efficient data manipulation and model building.
Tools & Resources
NPTEL courses, Coursera, HackerRank, LeetCode, Khan Academy
Career Connection
A strong foundation ensures a deep understanding of machine learning algorithms and efficient coding for data projects, critical for cracking technical interviews at top analytics firms and startups.
Build a Portfolio of Mini-Projects- (Semester 1-2)
Start working on small data analysis projects using publicly available datasets (e.g., Kaggle). Implement basic machine learning models, visualize data, and document your approach. Share these projects on GitHub to showcase practical skills and learning progress.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, Google Colab
Career Connection
A tangible project portfolio demonstrates practical application of learned concepts, making resumes stand out to recruiters seeking hands-on experience in the Indian job market.
Engage in Peer Learning & Discussion Groups- (Semester 1-2)
Form study groups with classmates to discuss complex topics, solve problems collaboratively, and prepare for lab sessions. Actively participate in departmental seminars and workshops to broaden your understanding and learn from peers and faculty.
Tools & Resources
Study groups, Online forums (Stack Overflow), Departmental workshops
Career Connection
Collaboration and communication skills are highly valued in industry roles. Peer learning enhances problem-solving abilities and exposes students to diverse perspectives, crucial for team-based data science projects.
Intermediate Stage
Gain Industry Exposure through Internships & Certifications- (Semester 2-3)
Actively seek internships during summer breaks with Indian tech companies, startups, or research labs to apply theoretical knowledge to real-world problems. Consider pursuing industry-recognized certifications in cloud platforms (AWS, Azure, GCP) or specialized ML frameworks (TensorFlow, PyTorch).
Tools & Resources
LinkedIn, Internshala, Naukri.com, AWS/Azure/GCP Certifications, DeepLearning.AI
Career Connection
Internships provide invaluable practical experience and networking opportunities, often leading to pre-placement offers. Certifications validate specialized skills, giving a competitive edge in the Indian data science job market.
Specialize in a Niche Area & Develop Advanced Skills- (Semester 2-3)
Based on your interest and career goals, delve deeper into an elective area like NLP, Computer Vision, or Big Data. Participate in advanced hackathons, develop end-to-end data science projects, and contribute to open-source projects related to your chosen specialization.
Tools & Resources
Kaggle Competitions, GitHub, Open-source communities, Specialized MOOCs
Career Connection
Specialized skills make you a valuable asset for niche roles in companies focusing on specific AI applications, increasing your chances of securing higher-paying and more challenging positions in India.
Network Actively with Professionals & Alumni- (Semester 2-3)
Attend industry conferences, webinars, and alumni meet-ups organized by the institution or external bodies. Connect with data science professionals on LinkedIn, participate in industry discussions, and seek mentorship to understand career trajectories and market demands.
Tools & Resources
LinkedIn, Industry conferences (Data Science Summit India), Alumni network events
Career Connection
Networking opens doors to hidden job opportunities, provides insights into industry trends, and helps build a professional support system, which is crucial for career progression in India''''s competitive tech sector.
Advanced Stage
Focus on Capstone Project & Thesis Development- (Semester 3-4)
Dedicate thorough effort to your Project Work I and II. Choose a challenging, industry-relevant problem, apply advanced data science techniques, and produce high-quality research/solution. Document your work meticulously in a thesis-style report.
Tools & Resources
Research papers, Academic journals, Industry problem statements, Advanced ML/DL libraries
Career Connection
A strong capstone project demonstrates your ability to independently tackle complex problems, a key requirement for R&D roles, product development, or even pursuing further academic research.
Intensive Placement Preparation & Mock Interviews- (Semester 4)
Engage in rigorous placement preparation, focusing on data structures, algorithms, machine learning concepts, and SQL. Participate in mock interviews (technical and HR) conducted by the placement cell or external platforms. Refine your resume and cover letter with specific project achievements.
Tools & Resources
Placement cell resources, GeeksforGeeks, LeetCode, InterviewBit, Mock interview platforms
Career Connection
Thorough preparation is essential for securing placements in top-tier companies. Mock interviews help in identifying weaknesses and improving performance under pressure, leading to successful job offers.
Develop Leadership & Communication Skills- (Semester 3-4)
Take initiative in leading team projects, organize technical events, or mentor junior students. Practice presenting complex technical concepts clearly and concisely. Effective communication, both written and verbal, is crucial for leadership roles in data science teams.
Tools & Resources
Toastmasters clubs, Public speaking workshops, Leadership training programs
Career Connection
Beyond technical skills, leadership and communication are vital for career advancement into senior data scientist, team lead, or managerial positions, enabling you to influence decisions and drive projects effectively within organizations.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in CSE/IT/ECE/EEE/AE/CE/ME or equivalent, M.Sc./MCA or equivalent with at least 50% marks in the qualifying examination from a recognized University.
Duration: 2 years (4 semesters)
Credits: 58.5 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RMDS701 | Research Methodology and IPR | Core | 3 | Research problem formulation, Data collection and analysis, Report writing and presentation, Intellectual Property Rights basics, Patents, Copyrights, Trademarks |
| MACS701 | Mathematical Foundations for Data Science | Core | 3 | Linear Algebra, Probability and Statistics, Calculus and Optimization, Vector spaces and matrices, Statistical inference and hypothesis testing |
| DS701 | Data Structures and Algorithms for Data Science | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees and Graphs, Sorting and Searching Algorithms, Algorithm analysis and complexity, Hashing and priority queues |
| DS703 | Machine Learning | Core | 3 | Supervised and Unsupervised Learning, Regression and Classification algorithms, Model evaluation and selection, Ensemble methods and boosting, Feature engineering and dimensionality reduction |
| DS705 | Advanced Database Management Systems | Core | 3 | Relational Database concepts, SQL and PL/SQL, NoSQL databases (MongoDB, Cassandra), Query processing and optimization, Transaction management and concurrency control |
| DS7L1 | Machine Learning Lab | Lab | 1.5 | Python programming for ML, Data preprocessing and exploration, Implementing regression and classification models, Model training and hyperparameter tuning, Performance evaluation metrics |
| DS7L3 | Advanced Database Management Systems Lab | Lab | 1.5 | SQL query writing and optimization, Database design and normalization, Working with NoSQL databases, Stored procedures and triggers, Data manipulation and security |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS702 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark for data processing, Data warehousing and ETL, Stream processing with Kafka/Spark Streaming |
| DS704 | Deep Learning | Core | 3 | Neural Network architectures, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Backpropagation and optimization, Transfer learning and fine-tuning |
| DS706 | Data Visualization | Core | 3 | Principles of effective visualization, Exploratory Data Analysis (EDA), Visualization tools (Tableau, PowerBI), Python libraries (Matplotlib, Seaborn, Plotly), Dashboard design and storytelling |
| DS7E1 to DS7E6 | Professional Elective - I (Choose one from Natural Language Processing, Computer Vision, Cloud Computing for Data Science, Reinforcement Learning, Time Series Analysis, Optimization Techniques for Data Science) | Elective | 3 | Natural Language Processing (Text analysis, Transformers), Computer Vision (Image processing, Object detection), Cloud Computing for Data Science (AWS/Azure/GCP), Reinforcement Learning (Q-learning, MDPs), Time Series Analysis (ARIMA, Forecasting), Optimization Techniques for Data Science (Gradient Descent) |
| DS7L2 | Big Data Analytics Lab | Lab | 1.5 | Hadoop installation and configuration, MapReduce programming, Spark RDD and DataFrame operations, Data ingestion with Sqoop/Flume, Building big data pipelines |
| DS7L4 | Deep Learning Lab | Lab | 1.5 | TensorFlow/Keras/PyTorch implementations, Image classification with CNNs, Text generation with RNNs/LSTMs, Model deployment fundamentals, Hyperparameter tuning for deep networks |
| DS7L6 | Data Visualization Lab | Lab | 1.5 | Creating interactive dashboards with Tableau/PowerBI, Advanced visualizations with Python, Storytelling with data, Customizing charts and graphs, Connecting to various data sources |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS8E1 to DS8E6 | Professional Elective - II (Choose one from Advanced Machine Learning, Blockchain Technology, AI in Healthcare, Industrial IoT, Explainable AI (XAI), Edge AI) | Elective | 3 | Advanced Machine Learning (Kernel Methods, Bayesian Learning), Blockchain Technology (Distributed Ledgers, Cryptography), AI in Healthcare (Medical imaging, Diagnostics), Industrial IoT (Sensors, Edge computing), Explainable AI (XAI) (Interpretability methods), Edge AI (On-device AI, TinyML) |
| DS8E7 to DS8E12 | Professional Elective - III (Choose one from Data Ethics and Privacy, Quantum Machine Learning, Data Storytelling for Business, Financial Analytics, Geospatial Data Science, Conversational AI) | Elective | 3 | Data Ethics and Privacy (GDPR, Data governance), Quantum Machine Learning (Quantum algorithms, Qiskit), Data Storytelling for Business (Narrative structures, Impact), Financial Analytics (Algorithmic trading, Risk modeling), Geospatial Data Science (GIS, Satellite imagery), Conversational AI (Chatbots, Voice assistants) |
| DS8PE1 | Project Work – I | Project | 6 | Problem identification and scope definition, Literature review and gap analysis, Methodology design and planning, Initial implementation and prototyping, Technical documentation and interim report |
| DS8SW1 | Seminar | Seminar | 1.5 | Technical presentation skills, Research paper analysis, Public speaking and confidence building, Selection of advanced research topics, Effective communication of technical ideas |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS8PE2 | Project Work – II | Project | 10.5 | Advanced system implementation and integration, Thorough testing and validation, Detailed results analysis and interpretation, Thesis writing and documentation, Project defense and viva-voce |
