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M-TECH in Computer Engineering Data Sciences at School of Technology
Gandhinagar, Gujarat
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About the Specialization
What is Computer Engineering (Data Sciences) at School of Technology Gandhinagar?
This M.Tech Computer Engineering (Data Sciences) program at Gujarat University - School of Technology focuses on equipping students with advanced skills in data analysis, machine learning, and big data technologies. It aligns with the growing demand for skilled data professionals in India''''s booming digital economy, distinguishing itself through a blend of theoretical foundations and practical applications crucial for real-world problem-solving.
Who Should Apply?
This program is ideal for engineering graduates (B.E./B.Tech. in CE/IT) or MCA holders passionate about leveraging data for insights. It caters to fresh graduates seeking entry into the data science field and working professionals aiming to upskill or transition into data-centric roles in various Indian sectors. Strong analytical and programming aptitude are key prerequisites.
Why Choose This Course?
Graduates of this program can expect to pursue lucrative career paths as Data Scientists, Machine Learning Engineers, Data Analysts, or Big Data Architects in India. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The program prepares students for roles in IT services, e-commerce, finance, and healthcare, fostering skills aligned with industry certifications.
Student Success Practices
Foundation Stage
Master Core Concepts with Practical Application- (Semester 1-2)
Focus on deeply understanding advanced data structures, algorithms, mathematical foundations, and big data concepts by implementing them. Actively code and debug to solidify theoretical knowledge.
Tools & Resources
LeetCode, HackerRank, Kaggle, Python/R
Career Connection
Strong foundational coding and problem-solving skills are essential for clearing technical interviews and excelling in initial project assignments.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups to discuss complex topics like Advanced Algorithms and Machine Learning. Teaching concepts to peers strengthens your own understanding and hones collaborative problem-solving.
Tools & Resources
Collaborative whiteboards (e.g., Miro), Discord channels, University library study rooms
Career Connection
Enhances communication, teamwork, and critical thinking – vital skills for industry roles and group projects.
Build a Strong Portfolio of Mini-Projects- (Semester 1-2)
Beyond lab assignments, take initiative to build small data-related projects. For example, analyze a public dataset using Hadoop or Spark, or implement a machine learning algorithm from scratch. Showcase on GitHub.
Tools & Resources
GitHub, Kaggle, UCI Machine Learning Repository, Google Colab
Career Connection
Demonstrates practical skills and passion to recruiters, making your resume stand out for internships and entry-level positions.
Intermediate Stage
Deep Dive into Specialization and Research- (Semester 3)
During Dissertation Part I, thoroughly explore a research problem in Data Science. Attend workshops, read research papers, and consult faculty mentors to refine your problem statement and methodology.
Tools & Resources
IEEE Xplore, ACM Digital Library, Google Scholar, Mendeley/Zotero, LaTeX
Career Connection
Develops critical research, analytical, and technical writing skills, valuable for R&D roles or further academic pursuits.
Pursue Relevant Internships- (Semester 3)
Actively seek internships at Indian tech companies, startups, or research labs that align with Data Science. Apply the knowledge gained in Machine Learning and Deep Learning to real-world challenges.
Tools & Resources
LinkedIn, Internshala, Company career pages, University placement cell
Career Connection
Provides invaluable industry exposure, networking opportunities, and often leads to pre-placement offers.
Participate in Data Science Competitions- (Semester 3)
Engage in online data science competitions (e.g., Kaggle, Analytics Vidhya) to test your skills against real-world problems and learn from diverse approaches.
Tools & Resources
Kaggle, Analytics Vidhya, DataHack platforms, scikit-learn, TensorFlow, PyTorch
Career Connection
Boosts problem-solving abilities, exposes you to complex datasets, and provides a tangible achievement for your resume.
Advanced Stage
Focus on Dissertation and Publication- (Semester 4)
Dedicate significant effort to Dissertation Part II, aiming for high-quality results and a potential research publication. Collaborate with faculty and peers, and prepare a strong thesis defense.
Tools & Resources
Relevant data science tools and libraries for your project, Academic writing software, Presentation tools
Career Connection
A strong dissertation can lead to research positions, PhD opportunities, and showcases advanced problem-solving capabilities to employers.
Targeted Skill Development for Placements- (Semester 4)
Identify specific skill gaps for your desired job roles (e.g., specific cloud platforms, advanced MLOps tools, domain-specific knowledge). Practice mock interviews and aptitude tests.
Tools & Resources
Online courses (Coursera, Udemy), Industry whitepapers, Company-specific interview prep materials, Campus placement cell workshops
Career Connection
Direct preparation for the job market, increasing employability and securing better placement offers.
Network and Build Professional Relationships- (Semester 4)
Attend industry meetups, webinars, and conferences. Connect with alumni and professionals on LinkedIn. Leverage these connections for career advice, mentorship, and job opportunities.
Tools & Resources
LinkedIn, Professional organizations (e.g., ACM India), Industry conferences (e.g., Data Science Congress India)
Career Connection
Opens doors to hidden job markets, provides insights into industry trends, and establishes a strong professional network for future growth.
Program Structure and Curriculum
Eligibility:
- B.E./B.Tech. in Computer Engineering/Information Technology or MCA with minimum 50% marks (45% for SEBC/SC/ST candidates) and valid GATE score or PGCET score.
Duration: 4 semesters / 2 years
Credits: 62 Credits
Assessment: Internal: 30% (for theory/practical subjects), 50% (for dissertation), External: 70% (for theory/practical subjects), 50% (for dissertation)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| RMC101 | Research Methodology & IPR | Core | 4 | Research Problem Formulation, Literature Review, Research Design, Data Collection Methods, Statistical Analysis, Intellectual Property Rights |
| CE113 | Big Data Analytics | Program Elective (Data Science Specific) | 4 | Introduction to Big Data, Hadoop Ecosystem, MapReduce Programming, HDFS, Spark Framework, NoSQL Databases |
| CE115 | Advanced Database Management Systems | Program Elective (Data Science Specific) | 4 | Distributed Databases, NoSQL Systems, Data Warehousing, OLAP, Query Optimization, Database Security |
| CE121 | Lab based on Program Elective-I (Big Data Analytics Lab) | Lab | 2 | Hadoop Setup and Commands, MapReduce Implementation, Spark Application Development, Pig and Hive Scripting, Data Ingestion Tools |
| CE122 | Lab based on Program Elective-II (Advanced DBMS Lab) | Lab | 2 | MongoDB Operations, Cassandra/HBase Usage, SQL Query Optimization, Data Warehousing ETL tools, Database Backup and Recovery |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CE201 | Advanced Algorithms | Core | 4 | Algorithmic Paradigms, Graph Algorithms, Network Flow, Linear Programming, NP-Completeness, Approximation Algorithms |
| CE213 | Machine Learning | Program Elective (Data Science Specific) | 4 | Supervised Learning, Unsupervised Learning, Regression Models, Classification Algorithms, Clustering Techniques, Model Evaluation |
| CE215 | Data Mining | Program Elective (Data Science Specific) | 4 | Data Preprocessing, Association Rule Mining, Classification Methods, Clustering Algorithms, Outlier Detection, Text and Web Mining |
| CE221 | Lab based on Program Elective-III (Machine Learning Lab) | Lab | 2 | Python for ML (Scikit-learn), Data Preprocessing Techniques, Implementing Regression Models, Implementing Classification Algorithms, Clustering Algorithms, Model Evaluation Metrics |
| CE222 | Lab based on Program Elective-IV (Data Mining Lab) | Lab | 2 | Weka Tool for Data Mining, R/Python for Data Mining, Association Rule Generation, Classification Model Building, Clustering Analysis, Visualization of Mining Results |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CE301 | Dissertation Part - I | Project | 8 | Problem Identification, Literature Survey, Research Design, Methodology Development, Preliminary Implementation, Report Writing |
| CE311 | Deep Learning | Program Elective (Data Science Specific) | 4 | Artificial Neural Networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Transfer Learning, Deep Learning Frameworks (TensorFlow/PyTorch) |
| OE301 | Business Intelligence | Open Elective (Recommended for Data Science) | 4 | BI Architecture, Data Warehousing Concepts, ETL Processes, Data Visualization, Reporting Tools, Decision Support Systems |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CE401 | Dissertation Part - II | Project | 14 | Advanced Research & Experimentation, Data Analysis and Interpretation, Thesis Writing, Results Presentation, Publication Planning, Defense Preparation |
