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M-SC in Information Technology Data Science at Shri Alpesh N. Patel Post Graduate Institute of Science & Research
Anand, Gujarat
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About the Specialization
What is Information Technology (Data Science) at Shri Alpesh N. Patel Post Graduate Institute of Science & Research Anand?
This M.Sc. Information Technology program at Shri Alpesh N. Patel Post Graduate Institute focuses on equipping students with advanced IT skills, with a specialized track in Data Science. It addresses the growing demand for skilled data professionals in India''''s digital transformation journey. The curriculum is designed to provide a robust foundation in core IT while diving deep into data analytics, machine learning, and artificial intelligence, crucial for the evolving Indian industry landscape.
Who Should Apply?
This program is ideal for BCA, B.Sc. IT, B.Sc. CS, or B.Sc. graduates aspiring to build a career in the data-driven world. It caters to fresh graduates seeking entry into analytics, data engineering, or machine learning roles. Working professionals looking to upskill in cutting-edge data technologies or transition into data science will also find this program beneficial, leveraging their foundational IT knowledge.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding careers as Data Scientists, Data Analysts, Machine Learning Engineers, or AI specialists in India. Entry-level salaries typically range from INR 4-7 lakhs per annum, with experienced professionals earning significantly more (INR 10-25 lakhs+). The program aligns with industry needs, fostering skills for high-growth trajectories in Indian tech companies, startups, and MNCs operating within the country.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus on building a strong base in Java and Python (from Semester 3 preparation). Regularly practice coding problems on platforms to solidify logic and syntax, which are essential for data manipulation and algorithm implementation.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Sololearn for Java basics
Career Connection
Strong programming skills are non-negotiable for any IT role, especially in data science for data manipulation, model implementation, and custom tool development.
Excel in Database Management- (Semester 1-2)
Gain hands-on expertise in Advanced DBMS (SQL, PL/SQL) and learn to optimize database queries. Understand data modeling principles thoroughly to efficiently store and retrieve information.
Tools & Resources
MySQL Workbench, PostgreSQL, W3Schools SQL tutorials, Official documentation for database systems
Career Connection
Data is stored in databases; proficiency is critical for data extraction, storage, and management, forming the backbone for analytical and data science roles.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and prepare for internal and external exams. Teaching concepts to others reinforces your own understanding and builds teamwork.
Tools & Resources
College library, Online collaborative platforms like Discord or Slack, Whiteboards for brainstorming
Career Connection
Enhances problem-solving, communication, and teamwork skills, which are vital in professional environments and collaborative data science projects.
Intermediate Stage
Deep Dive into Data Science Electives- (Semester 3-4)
Actively choose and thoroughly study Data Science Fundamentals, Machine Learning, and Deep Learning electives. Go beyond the syllabus to explore advanced concepts and real-world applications using varied datasets.
Tools & Resources
Coursera, edX courses (e.g., Andrew Ng''''s ML), Kaggle for datasets and competitions, Towards Data Science blog
Career Connection
Directly builds the specialized knowledge required for roles such as data scientist, ML engineer, and AI specialist, making you a competitive candidate.
Undertake Mini-Projects & Case Studies- (Semester 3-4)
Apply theoretical knowledge from Python and data science electives to solve small, real-world problems. Use datasets available on public platforms like Kaggle to build a comprehensive project portfolio.
Tools & Resources
Jupyter Notebook, Google Colab, scikit-learn, Pandas, NumPy libraries, Kaggle datasets and tutorials
Career Connection
Demonstrates practical problem-solving abilities to potential employers and provides tangible evidence of your skills, crucial for interviews and job applications.
Develop Communication & Presentation Skills- (Semester 3-4)
Participate actively in seminars, group discussions, and present project findings effectively. Learn to articulate complex technical concepts clearly to both technical and non-technical audiences, a key data scientist skill.
Tools & Resources
Departmental seminars, Mock presentations with peers/faculty, Public speaking clubs like Toastmasters if available
Career Connection
Essential for presenting data insights, project proposals, and contributing effectively in team meetings, enhancing your visibility and impact.
Advanced Stage
Execute a Comprehensive Capstone Project- (Semester 4)
Work on a substantial project that integrates data collection, analysis, model building (ML/DL), and deployment. Aim for an innovative solution to an industry-relevant problem, simulating a real-world scenario.
Tools & Resources
TensorFlow, PyTorch, Cloud platforms (AWS, Azure, GCP), Git/GitHub for version control
Career Connection
The capstone project is a critical showpiece for placements, demonstrating end-to-end data science capabilities and acting as a strong portfolio item.
Prepare for Placements & Industry Interviews- (Semester 4)
Sharpen technical interview skills, practice aptitude tests, and prepare for HR rounds. Tailor your resume and LinkedIn profile to highlight data science skills, projects, and accomplishments for specific roles.
Tools & Resources
College placement cell workshops, Mock interviews, Glassdoor for company insights and interview questions, LinkedIn Job Search
Career Connection
Directly prepares students for securing jobs in top companies as data scientists or ML engineers, ensuring readiness for competitive Indian job market.
Network and Engage with Industry Professionals- (Semester 4)
Attend webinars, industry conferences (even virtual ones), and connect with professionals on LinkedIn. Seek mentorship and stay updated with the latest trends and technologies in data science.
Tools & Resources
LinkedIn, Industry event platforms (e.g., Data Science Summit, AI Conclave), Professional organizations like ACM or IEEE
Career Connection
Opens doors to internship opportunities, job referrals, and provides invaluable insights into career growth paths and emerging roles in the Indian data science landscape.
Program Structure and Curriculum
Eligibility:
- BCA / B.Sc. IT / B.Sc. CS / B.Sc. from recognized university with 50% marks
Duration: 2 years (4 semesters)
Credits: 108 Credits
Assessment: Internal: 30-50% (Varies by subject type), External: 50-70% (Varies by subject type)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| M.Sc. IT-101 | Advanced Java | Core | 4 | Java Evolution and Features, Object-Oriented Programming with Java, Exception Handling, Threads, Applets, AWT, Swings, JDBC and Networking, Servlets and JSP |
| M.Sc. IT-102 | Data Communication and Networking | Core | 4 | Introduction to Data Communication, Network Models (OSI, TCP/IP), Physical Layer and Media, Data Link Layer Protocols, Network Layer (IP, Routing), Transport Layer (TCP, UDP) |
| M.Sc. IT-103 | Advanced Database Management System | Core | 4 | DBMS Architecture and Models, Relational Algebra and Calculus, SQL and PL/SQL, Transaction Management and Concurrency Control, Database Recovery Techniques, Distributed and Object-Oriented Databases |
| M.Sc. IT-104 | Operating System Concepts | Core | 4 | Operating System Services and Structures, Process Management and CPU Scheduling, Deadlocks and Concurrency, Memory Management Techniques, Virtual Memory and Paging, File System Implementation |
| M.Sc. IT-105 | Practical based on IT-101 | Lab | 4 | Java Programming Exercises, Object-Oriented Implementation, Database Connectivity via JDBC, Networking Application Development, GUI Development using AWT/Swings |
| M.Sc. IT-106 | Practical based on IT-103 | Lab | 4 | SQL Querying and Data Definition, PL/SQL Programming, Database Administration Tasks, Transaction Management Scenarios, Database Backup and Recovery |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| M.Sc. IT-201 | .Net Technology | Core | 4 | .NET Framework and CLR, C# Language Fundamentals, Windows Forms Applications, ADO.NET and Data Access, ASP.NET Web Forms, Web Services and WCF |
| M.Sc. IT-202 | Advanced Web Technology | Core | 4 | Web Development Fundamentals, HTML5, CSS3, JavaScript, Client-side Scripting (DOM, AJAX), XML, JSON, Web Services, Server-Side Scripting Concepts, Web Security Principles |
| M.Sc. IT-203 | Linux Administration | Core | 4 | Linux Operating System Fundamentals, File System Hierarchy Standard, User and Group Management, Process Management, Networking Configuration, Shell Scripting and Automation |
| M.Sc. IT-204 | Object Oriented Analysis and Design using UML | Core | 4 | Object-Oriented Concepts, UML Fundamentals and Diagrams, Use Case Diagrams, Class and Object Diagrams, Interaction Diagrams, Design Patterns |
| M.Sc. IT-205 | Practical based on IT-201 | Lab | 4 | C# Programming Exercises, Windows Forms Application Development, ADO.NET Data Access, ASP.NET Web Development, Web Service Implementation |
| M.Sc. IT-206 | Practical based on IT-202 | Lab | 4 | HTML5 and CSS3 Layouts, JavaScript and DOM Manipulation, AJAX Implementation, XML and JSON Parsing, Responsive Web Design |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| M.Sc. IT-301 | Python Programming | Core | 4 | Python Basics and Data Types, Control Flow and Functions, Object-Oriented Programming in Python, File I/O and Exception Handling, Modules and Packages, Data Structures in Python |
| M.Sc. IT-302 | Compiler Design | Core | 4 | Introduction to Compilers, Lexical Analysis, Syntax Analysis (Parsing), Semantic Analysis, Intermediate Code Generation, Code Optimization and Generation |
| M.Sc. IT-303 | Advanced Artificial Intelligence | Elective-I (Data Science focus) | 4 | Introduction to AI, Knowledge Representation, Heuristic Search Algorithms, Expert Systems, Fuzzy Logic and Neural Networks, Natural Language Processing (NLP) Basics |
| M.Sc. IT-304 | Data Science Fundamentals | Elective-II (Data Science Specialization Core) | 4 | Introduction to Data Science, Data Collection and Preprocessing, Exploratory Data Analysis (EDA), Probability and Statistics for Data Science, Introduction to Machine Learning, Data Visualization Techniques |
| M.Sc. IT-305 | Practical based on IT-301 | Lab | 4 | Python Scripting for Automation, Data Manipulation with Pandas, Numerical Operations with NumPy, Object-Oriented Python Applications, File Handling and Exception Management |
| M.Sc. IT-306 | Practical based on Elective-I | Lab/Case Study | 4 | AI Problem Solving using Python, Search Algorithm Implementation, Knowledge-based System Development, Fuzzy Logic Applications, Basic NLP Tasks |
| M.Sc. IT-307 | Practical based on Elective-II | Lab (Data Science Fundamentals) | 4 | Data Cleaning and Preprocessing in Python, Statistical Analysis with Python Libraries, Basic Machine Learning Model Implementation, Data Visualization using Matplotlib/Seaborn, Hypothesis Testing and Regression Analysis |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| M.Sc. IT-401 | Project | Core (Major Project) | 20 | Problem Identification and Formulation, System Design and Architecture, Implementation and Coding, Testing and Debugging, Documentation and Reporting, Presentation and Demonstration |
| M.Sc. IT-402 | Seminar | Core | 4 | Research Topic Selection, Literature Review, Content Organization, Presentation Skills, Question and Answer Session, Technical Communication |
| M.Sc. IT-403 | Machine Learning | Elective-III (Data Science Specialization Core) | 4 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Model Evaluation and Selection, Ensemble Methods (Bagging, Boosting), Support Vector Machines and Decision Trees |
| M.Sc. IT-404 | Deep Learning | Elective-IV (Data Science Specialization Core) | 4 | Neural Network Basics, Feedforward Networks and Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Deep Learning Frameworks (TensorFlow, Keras) |
