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BCA in Data Science at Seth S.S. Jain Subodh P.G. Autonomous College
Jaipur, Rajasthan
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About the Specialization
What is Data Science at Seth S.S. Jain Subodh P.G. Autonomous College Jaipur?
This BCA Data Science program at S.S. Jain Subodh Post Graduate Autonomous College, Jaipur, focuses on equipping students with essential skills for the rapidly evolving data-driven Indian industry. It integrates core computing with specialized data science techniques, preparing graduates for roles in analytics, machine learning, and artificial intelligence, addressing the high demand for skilled professionals in this sector.
Who Should Apply?
This program is ideal for 10+2 graduates, particularly those with a mathematics background or a computer diploma, seeking entry into the booming data science field. It also suits individuals passionate about problem-solving, analytical thinking, and leveraging data for business insights, providing a strong foundation for a career in data analysis and machine learning.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths as Data Analysts, Junior Data Scientists, Business Intelligence Developers, or Machine Learning Engineers. Entry-level salaries typically range from INR 3-6 lakhs per annum, with significant growth potential. The program aligns with industry demands, opening doors to roles in IT, finance, healthcare, and e-commerce sectors.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consolidate strong programming basics in C and C++ by regularly solving problems. Focus on data structures implementation and object-oriented principles, as these are critical building blocks for advanced data science. Regularly review concepts and participate in coding challenges.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Local programming clubs
Career Connection
Strong coding skills are foundational for any tech role, crucial for technical interviews and developing complex data science solutions.
Develop Mathematical and Statistical Acumen- (Semester 1-2)
Pay close attention to Applied Mathematics and Statistics courses. Practice regularly to build a solid understanding of linear algebra, calculus, probability, and inferential statistics, which are vital for comprehending data science algorithms.
Tools & Resources
Khan Academy, NPTEL courses on probability and statistics, Open-source statistical software like R
Career Connection
A robust mathematical and statistical base is indispensable for understanding, implementing, and interpreting data science models and their underlying principles.
Engage in Peer Learning and Communication- (Semester 1-2)
Form study groups, discuss challenging concepts, and practice explaining technical topics clearly. Participate actively in communication skills sessions to enhance presentation abilities, crucial for collaborating and presenting data insights.
Tools & Resources
College library discussion rooms, Online collaborative tools, Campus workshops on public speaking
Career Connection
Effective communication and teamwork are highly valued in industry, essential for collaborating on projects and presenting data insights to diverse stakeholders.
Intermediate Stage
Build a Strong Data Science Portfolio- (Semester 3-5)
Actively work on small data science projects using Python, focusing on data cleaning, exploratory analysis, and basic machine learning model implementation. Showcase these projects on platforms like GitHub to demonstrate practical skills.
Tools & Resources
Kaggle datasets, GitHub for project hosting, Jupyter notebooks, Python libraries Pandas, NumPy, Scikit-learn
Career Connection
A strong project portfolio showcases practical skills to recruiters and is critical for securing internships and entry-level job opportunities in data science.
Seek Industry Internships and Workshops- (Semester 4-5)
Look for internships during summer breaks in local tech companies, startups, or college research projects to gain real-world experience. Attend workshops on emerging data science tools and techniques to stay updated.
Tools & Resources
College placement cell, LinkedIn, Internshala, Industry events and seminars in Jaipur
Career Connection
Internships provide invaluable real-world experience, networking opportunities, and often lead to pre-placement offers, accelerating career entry into the data science field.
Participate in Coding and Data Challenges- (Semester 3-5)
Regularly participate in online coding competitions and data hackathons to sharpen problem-solving skills, learn from peers, and gain recognition. These platforms offer practical challenges aligned with industry scenarios.
Tools & Resources
CodeChef, HackerEarth, Kaggle competitions, College technical festivals
Career Connection
Such participation enhances your resume, demonstrates competitive spirit, and hones skills under pressure, making candidates more attractive to potential employers.
Advanced Stage
Specialize and Deepen Expertise- (Semester 6)
Focus on chosen electives like R Programming, Deep Learning, or NLP, and undertake a significant Major Project that applies advanced data science concepts to a real-world problem. This specialization will define your unique skill set.
Tools & Resources
Advanced online courses Coursera, edX, Specialized documentation for frameworks like TensorFlow/Keras, Academic journals and research papers
Career Connection
Deep specialization differentiates candidates, making them suitable for niche roles and demonstrating commitment to specific, high-demand data science sub-fields.
Prepare Rigorously for Placements- (Semester 6)
Dedicate time to mock interviews, aptitude tests, and resume building workshops. Practice explaining your project work and theoretical concepts clearly and concisely, aligning them with company requirements.
Tools & Resources
College placement cell, Career counseling services, Interview preparation platforms like InterviewBit, Glassdoor
Career Connection
Thorough preparation significantly increases your chances of securing desired job roles in top companies, ensuring a smooth and successful transition from academics to a professional career.
Network and Professional Branding- (Semester 6)
Attend industry meetups, connect with professionals on LinkedIn, and contribute to open-source data science projects. Develop a strong online professional presence showcasing your skills and passion for data science.
Tools & Resources
LinkedIn, GitHub, Local tech communities, Industry conferences and webinars
Career Connection
Networking opens doors to hidden opportunities, mentorship, and keeps you updated with industry trends, all crucial for long-term career growth and professional advancement.
Program Structure and Curriculum
Eligibility:
- Passed 10+2 examination with Mathematics as one of the subjects or a certificate/diploma in Computers awarded by any recognized Board/University.
Duration: 3 years (6 semesters)
Credits: 142 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS101 | Computer Fundamentals | Core Theory | 4 | Introduction to Computers, Input/Output Devices, Memory Organization, Software Concepts, Operating Systems, Number Systems |
| CS102 | Programming in C | Core Theory | 4 | Introduction to C Programming, Data Types and Operators, Control Structures, Functions, Arrays and Strings, Pointers, Structures, and Unions |
| CS103 | Applied Mathematics | Core Theory | 4 | Set Theory, Relations and Functions, Matrices, Determinants, Differentiation, Integration |
| CS104 | Communication Skills | Ability Enhancement Compulsory Course (AECC) Theory | 4 | English Grammar, Vocabulary Building, Comprehension, Letter Writing, Report Writing, Presentation Skills |
| CS105 | Environmental Studies | Ability Enhancement Compulsory Course (AECC) Theory | 4 | Ecosystems, Biodiversity Conservation, Pollution Types and Control, Renewable Energy Sources, Environmental Ethics, Sustainable Development |
| CS106 | Lab 1: Programming in C | Core Practical | 2 | C programs using control statements, Functions and recursion, Arrays and strings manipulation, Pointers and memory management, Structures and file handling |
| CS107 | Lab 2: Computer Fundamentals | Core Practical | 2 | MS Word document creation and formatting, MS Excel spreadsheet operations, MS PowerPoint presentation design, Internet browsing and email management, Basic hardware identification |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS201 | Data Structures | Core Theory | 4 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Binary Search Trees, Graphs and Traversals, Searching and Sorting Algorithms |
| CS202 | Object-Oriented Programming with C++ | Core Theory | 4 | OOP Concepts, Classes and Objects, Inheritance, Polymorphism, Abstraction and Encapsulation, Exception Handling and Templates |
| CS203 | Computer Organization and Architecture | Core Theory | 4 | Digital Logic Circuits, Combinational and Sequential Circuits, CPU Organization, Instruction Set Architecture, Memory Hierarchy, Input/Output Organization |
| CS204 | Discrete Mathematics | Core Theory | 4 | Logic and Proofs, Set Theory, Relations and Functions, Graph Theory, Recurrence Relations, Combinatorics |
| CS205 | Statistics | Core Theory | 4 | Data Collection and Representation, Measures of Central Tendency, Measures of Dispersion, Probability Theory, Correlation and Regression, Hypothesis Testing |
| CS206 | Lab 3: Data Structures | Core Practical | 2 | Implementation of arrays and linked lists, Stack and queue operations, Tree traversal algorithms, Graph representation and traversal, Sorting and searching techniques |
| CS207 | Lab 4: Object-Oriented Programming with C++ | Core Practical | 2 | C++ programs using classes and objects, Inheritance implementation, Polymorphism concepts, Function and operator overloading, File handling and exception handling |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS301 | Database Management Systems | Core Theory | 4 | Introduction to DBMS, Entity-Relationship Model, Relational Model and Algebra, Structured Query Language SQL, Normalization, Transaction Management |
| CS302 | Python Programming | Core Theory | 4 | Python Basics and Data Types, Control Flow Statements, Functions and Modules, File I/O, Object-Oriented Programming in Python, Exception Handling |
| CS303 | Operating Systems | Core Theory | 4 | Operating System Concepts, Process Management, CPU Scheduling, Deadlocks, Memory Management, File Systems |
| CS304 | Data Communication & Networking | Core Theory | 4 | Network Models OSI/TCP-IP, Network Topologies, Transmission Media, Switching Techniques, Network Devices, Internet Protocols |
| CS305 | Data Science Fundamentals | Skill Enhancement Course (SEC) Theory | 4 | Introduction to Data Science, Data Collection and Cleaning, Exploratory Data Analysis, Data Preprocessing, Feature Engineering, Basic Data Visualization |
| CS306 | Lab 5: Database Management Systems | Core Practical | 2 | DDL and DML commands in SQL, SQL queries with joins, Views and stored procedures, Database creation and manipulation, Transaction control language |
| CS307 | Lab 6: Python Programming | Core Practical | 2 | Python programs for data types and operators, Control flow statements implementation, Functions and module usage, File handling operations, Object-oriented programming concepts |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Web Technologies | Core Theory | 4 | HTML and CSS for Web Design, JavaScript and DOM, XML and AJAX, Web Servers Apache/IIS, PHP Basics, Database Connectivity MySQL |
| CS402 | Introduction to Machine Learning | Skill Enhancement Course (SEC) Theory | 4 | Supervised Learning, Unsupervised Learning, Regression Algorithms, Classification Algorithms, Clustering Techniques, Model Evaluation and Validation |
| CS403 | Data Warehousing & Data Mining | Discipline Specific Elective (DSE) Theory | 4 | Data Warehouse Architecture, OLAP Operations, Data Preprocessing for Mining, Association Rule Mining, Classification Algorithms, Clustering Algorithms |
| CS404 | Big Data Analytics | Discipline Specific Elective (DSE) Theory | 4 | Introduction to Big Data, Hadoop Ecosystem HDFS, MapReduce Framework, Spark RDDs, NoSQL Databases, Big Data Processing Tools |
| CS405 | Software Engineering | Core Theory | 4 | Software Development Life Cycle, Requirement Analysis, Software Design Principles, Software Testing Techniques, Software Maintenance, Project Management |
| CS406 | Lab 7: Web Technologies | Core Practical | 2 | Designing web pages with HTML and CSS, Client-side scripting with JavaScript, Dynamic web content using PHP, Database integration with MySQL, Developing interactive web forms |
| CS407 | Lab 8: Machine Learning | Core Practical | 2 | Implementing regression models, Applying classification algorithms, Performing clustering analysis, Using Python libraries like Scikit-learn, Evaluating model performance metrics |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Artificial Intelligence | Core Theory | 4 | AI Concepts and History, Problem Solving Search Algorithms, Knowledge Representation, Expert Systems, Fuzzy Logic, Machine Learning Basics |
| CS502 | Cloud Computing | Skill Enhancement Course (SEC) Theory | 4 | Cloud Computing Architecture, Service Models IaaS, PaaS, SaaS, Deployment Models, Virtualization Technology, Cloud Security, Cloud Platforms AWS/Azure Basics |
| CS503 | Natural Language Processing | Discipline Specific Elective (DSE) Theory | 4 | NLP Introduction, Text Preprocessing Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Sentiment Analysis, Text Classification |
| CS504 | Deep Learning | Discipline Specific Elective (DSE) Theory | 4 | Neural Networks Basics, Perceptrons and Backpropagation, Convolutional Neural Networks CNNs, Recurrent Neural Networks RNNs, Deep Learning Frameworks TensorFlow/Keras, Generative Adversarial Networks GANs |
| CS505 | Data Visualization | Discipline Specific Elective (DSE) Theory | 4 | Principles of Data Visualization, Data Storytelling, Visualizing Data with Matplotlib/Seaborn, Interactive Dashboards Tableau/Power BI, Geospatial Data Visualization, Ethics in Data Visualization |
| CS506 | Lab 9: Artificial Intelligence & NLP | Core Practical | 2 | Implementing AI search algorithms, Knowledge representation in AI, Text preprocessing with NLTK/SpaCy, Sentiment analysis tasks, Named entity recognition implementation |
| CS507 | Lab 10: Deep Learning & Data Visualization | Core Practical | 2 | Implementing basic neural networks, Building CNNs for image classification, Developing RNNs for sequence data, Creating various plots with Matplotlib/Seaborn, Designing interactive dashboards |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Elective - I (R Programming) | Discipline Specific Elective (DSE) Theory | 4 | Introduction to R Language, Data Types and Control Structures, Functions and Packages, Data Frames and Manipulation, Statistical Graphics with R, Basic Statistical Analysis |
| CS601 | Elective - I (Block Chain Technology) | Discipline Specific Elective (DSE) Theory | 4 | Blockchain Fundamentals, Cryptography in Blockchain, Consensus Mechanisms, Smart Contracts, Cryptocurrency Basics, Decentralized Applications DApps |
| CS602 | Elective - II (Mobile Application Development) | Discipline Specific Elective (DSE) Theory | 4 | Android Architecture Components, UI Design with Activities and Layouts, Intents and Broadcast Receivers, Data Storage SQLite/Shared Preferences, Networking and Web Services, Location-Based Services |
| CS602 | Elective - II (Cyber Security) | Discipline Specific Elective (DSE) Theory | 4 | Network Security Concepts, Cryptography Principles, Web Security Vulnerabilities, Malware and Viruses, Cyber Forensics Basics, Ethical Hacking Methodologies |
| CS603 | Major Project (Dissertation & Viva) | Core Project | 6 | Project Definition and Scope, Literature Review, System Design and Architecture, Implementation and Coding, Testing and Debugging, Report Writing and Viva-Voce |
| CS604 | Minor Project | Core Project | 4 | Problem Identification, Requirement Gathering, Design and Development, Testing and Evaluation, Project Documentation, Presentation of Work |
| CS605 | Internship / Industrial Training | Core Internship | 4 | Industry Exposure and Practices, Application of Academic Knowledge, Professional Skill Development, Teamwork and Communication, Project Implementation in Industry, Internship Report Submission |
