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B-SC-DATA-SCIENCE in General at Kristu Jyoti College of Management and Technology
Kottayam, Kerala
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About the Specialization
What is General at Kristu Jyoti College of Management and Technology Kottayam?
This B.Sc. Data Science program at Kristu Jyoti College of Management and Technology, affiliated with Mahatma Gandhi University Kottayam, focuses on developing strong foundations in mathematics, statistics, computer science, and practical data analysis. The curriculum is meticulously designed to equip students with the skills required to navigate the rapidly evolving data landscape. It emphasizes both theoretical knowledge and hands-on application, crucial for India''''s accelerating digital transformation and burgeoning tech industry demand.
Who Should Apply?
This program is ideal for fresh graduates from science or commerce backgrounds, especially those with a strong aptitude for mathematics or statistics, who are seeking entry into data-driven roles. It also suits individuals with basic programming knowledge looking to specialize in areas like data analysis, machine learning, and artificial intelligence. Career changers eager to transition into the high-demand data science industry will find the comprehensive curriculum beneficial, provided they meet the foundational prerequisites.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles such as Data Analyst, Business Intelligence Developer, Machine Learning Engineer, and Data Scientist across IT, finance, healthcare, and e-commerce sectors. Entry-level salaries typically range from INR 3-6 LPA, with significant growth potential with experience. The program’s focus on industry-relevant skills and tools prepares students for various professional certifications, further enhancing their growth trajectories in Indian companies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Consolidate Python programming skills and understand fundamental data structures and algorithms, which are crucial for advanced data science courses. Actively participate in coding challenges and problem-solving exercises to build a strong programming base.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation
Career Connection
Strong coding skills are essential for technical interviews and efficient data manipulation, forming a core requirement for nearly all data science and analytics roles.
Build Strong Mathematical & Statistical Acumen- (Semester 1-2)
Thoroughly grasp concepts in linear algebra, calculus, probability theory, and inferential statistics. Utilize online resources and textbooks for practice problems to solidify this mathematical and statistical bedrock, crucial for understanding machine learning algorithms.
Tools & Resources
Khan Academy, NPTEL courses, Statistics LibreTexts, textbook problem sets
Career Connection
A deep understanding of these subjects is vital for comprehending, building, and interpreting complex machine learning models, which are critical for advanced Data Scientist positions.
Engage in Peer Learning & Study Groups- (Semester 1-2)
Form study groups to discuss complex academic topics, solve problems collaboratively, and prepare effectively for examinations. Peer teaching significantly reinforces understanding and develops essential communication skills for future professional interactions.
Tools & Resources
College library study rooms, online collaboration tools like Google Meet
Career Connection
Fosters teamwork and communication abilities, which are highly valued soft skills in collaborative, project-based work environments common within the data industry.
Intermediate Stage
Undertake Mini-Projects & Kaggle Competitions- (Semester 3-5)
Apply learned concepts from Database Management, Machine Learning, and Data Visualization to build small, impactful projects. Participate in beginner-friendly Kaggle competitions to gain practical experience and publicly showcase problem-solving skills.
Tools & Resources
GitHub for version control, Kaggle platform, Jupyter Notebook, scikit-learn, pandas libraries
Career Connection
Develops a robust project portfolio, a key asset for demonstrating practical skills and initiative to potential employers, crucial for roles like Machine Learning Engineer or Data Analyst.
Explore Industry-Relevant Tools & Technologies- (Semester 3-5)
Beyond the prescribed curriculum, proactively learn popular data science tools such as Tableau, PowerBI, advanced SQL, and basic cloud platforms (AWS, Azure, GCP). Consider pursuing relevant online certifications to validate these skills.
Tools & Resources
Coursera, Udemy, DataCamp for courses, official documentation for Tableau/PowerBI, free tiers of cloud platforms
Career Connection
Acquiring in-demand software skills significantly enhances employability, making graduates job-ready for roles requiring specific tool proficiencies like Business Intelligence Developer or Cloud Data Engineer.
Network with Professionals & Attend Workshops- (Semester 3-5)
Attend college-organized workshops, webinars, and local meetups focused on data science and emerging technologies. Connect with alumni and industry professionals on platforms like LinkedIn to gain valuable insights and seek mentorship opportunities.
Tools & Resources
LinkedIn, Eventbrite for local events, college alumni network platforms
Career Connection
Helps in understanding current industry trends, discovering potential internship opportunities, and building a professional network that is invaluable for long-term career progression and job searches.
Advanced Stage
Focus on Capstone Project & Specialization- (Semester 6)
Dedicate significant effort to the Major Project in Semester 6, choosing a topic that aligns with personal career interests and incorporates advanced concepts from Deep Learning, AI, or Big Data. Aim for a high-quality, impactful output that solves a real-world problem.
Tools & Resources
Advanced deep learning frameworks (TensorFlow, PyTorch), cloud GPUs, relevant public datasets, academic advisors
Career Connection
A strong capstone project serves as a compelling portfolio piece, directly demonstrating advanced problem-solving, research, and implementation skills to recruiters, especially for specialized roles.
Intensive Placement Preparation & Mock Interviews- (Semester 6)
Engage in rigorous preparation for interviews, encompassing technical rounds (coding, machine learning concepts) and HR interviews. Participate actively in mock interviews conducted by the college''''s career services or external mentors to refine communication and confidence.
Tools & Resources
InterviewBit, Glassdoor for company-specific questions, mock interview platforms, college career services
Career Connection
Maximizes the chances of securing desirable placements by honing interview techniques, strengthening conceptual understanding, and building confidence, crucial for success in competitive recruitment drives.
Explore Advanced Certifications & Ethics- (Semester 6)
Consider pursuing advanced certifications in specific areas like cloud data engineering (e.g., AWS Certified Data Analytics) or specialized AI/ML. Develop a strong understanding of data ethics, governance, and responsible AI principles for ethical professional practice.
Tools & Resources
Official certification guides, professional bodies (e.g., Data Science Council of America), ethical AI frameworks
Career Connection
Differentiates candidates in a competitive market, validates specialized skills, and prepares them for responsible data leadership roles, especially critical in highly regulated industries and for addressing societal impact.
Program Structure and Curriculum
Eligibility:
- Candidates who have passed the Plus Two / equivalent examination with Mathematics/Computer Science/Informatics Practices/Statistics/Physics/Chemistry/Electronics as one of the subjects are eligible for admission to the B.Sc. Data Science Programme.
Duration: 6 Semesters / 3 Years
Credits: Minimum 120 Credits
Assessment: Internal: 20%, External: 80%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADA01 | English I - The Four Skills for Communication | Common | 3 | Communication Skills, Reading Comprehension, Writing Skills, Grammar and Vocabulary, Public Speaking |
| DCADA02 | English II - Readings for Academic and Professional Enrichment | Common | 3 | Academic Writing, Critical Reading, Professional Communication, Report Writing, Presentation Skills |
| DCADA03 | Additional Language (Malayalam/Hindi/Sanskrit/Arabic) | Common | 4 | Grammar, Literature, Communication, Translation, Cultural Aspects |
| DCADA04 | Mathematical Foundations for Data Science | Core | 4 | Linear Algebra, Calculus, Matrices and Determinants, Eigenvalues and Eigenvectors, Optimization Techniques |
| DCADA05 | Mathematical Foundations for Data Science Lab | Lab | 2 | Matrix Operations, Solving Linear Equations, Calculus Problems, Optimization Implementation, Data Manipulation |
| DCADA06 | Introduction to Programming | Core | 4 | Python Programming, Data Types and Variables, Control Structures, Functions and Modules, Object-Oriented Concepts |
| DCADA07 | Introduction to Programming Lab | Lab | 2 | Python Scripting, Debugging Techniques, Basic Algorithms, Data Structures in Python, Problem Solving Exercises |
| DCADA08 | Complementary Course I (Physics/Statistics) | Complementary | 4 | Fundamental Concepts, Measurement and Analysis, Theory and Applications, Experimental Techniques, Problem Solving |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADB01 | English III - Literature and Contemporary Issues | Common | 3 | Literary Analysis, Social Issues, Critical Thinking, Argumentative Writing, Cultural Studies |
| DCADB02 | English IV - Language and Linguistics | Common | 3 | Phonetics and Phonology, Morphology and Syntax, Semantics and Pragmatics, Sociolinguistics, Applied Linguistics |
| DCADB03 | Additional Language (Malayalam/Hindi/Sanskrit/Arabic) - II | Common | 4 | Advanced Grammar, Literary Criticism, Creative Writing, Discourse Analysis, Regional Language Studies |
| DCADB04 | Statistical Methods for Data Science | Core | 4 | Probability Theory, Random Variables and Distributions, Sampling Distributions, Hypothesis Testing, Regression and Correlation |
| DCADB05 | Statistical Methods for Data Science Lab | Lab | 2 | Statistical Software (R/Python), Data Visualization for Statistics, Hypothesis Testing Implementation, Regression Modeling, Statistical Inference Techniques |
| DCADB06 | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms, Algorithm Analysis |
| DCADB07 | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Data Structures, Algorithm Design, Complexity Analysis, Problem Solving with DS, Recursion Practice |
| DCADB08 | Complementary Course II (Physics/Statistics) | Complementary | 4 | Advanced Concepts, Applications in Science, Quantitative Analysis, Research Methods, Statistical Modeling |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADC01 | Database Management Systems | Core | 4 | Relational Model, SQL Querying, Database Design, Normalization, Transaction Management, Database Security |
| DCADC02 | Database Management Systems Lab | Lab | 2 | SQL Commands, Database Creation, Data Manipulation Language, Stored Procedures, Triggers and Views, Database Connectivity |
| DCADC03 | Computer Networks | Core | 4 | Network Topologies, OSI and TCP/IP Models, Network Protocols, Routing and Switching, Network Security, Wireless Networks |
| DCADC04 | Computer Networks Lab | Lab | 2 | Network Configuration, Packet Sniffing Tools, Socket Programming, Network Troubleshooting, Client-Server Application |
| DCADC05 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks, Linux Commands |
| DCADC06 | Operating Systems Lab | Lab | 2 | Shell Scripting, Process Management Commands, System Calls Implementation, File Permissions, Memory Allocation Simulation |
| DCADC07 | Generic Elective I | Generic Elective | 4 | Interdisciplinary Topics, Skill Development, Application of Concepts, Problem Solving, Emerging Technologies |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADD01 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression and Classification, Clustering Algorithms, Model Evaluation Metrics, Ensemble Methods |
| DCADD02 | Machine Learning Lab | Lab | 2 | Python Libraries (Scikit-learn), Data Preprocessing, Model Training and Testing, Hyperparameter Tuning, Algorithm Implementation |
| DCADD03 | Data Visualization | Core | 4 | Principles of Visualization, Data Storytelling, Chart Types and Design, Interactive Visualizations, Tools like Matplotlib/Seaborn/Plotly |
| DCADD04 | Data Visualization Lab | Lab | 2 | Creating Dashboards, Using Visualization Libraries, Geospatial Visualization, Infographics Design, Interactive Report Generation |
| DCADD05 | Introduction to Big Data | Core | 4 | Big Data Concepts, Hadoop Ecosystem, HDFS and MapReduce, Apache Spark, NoSQL Databases, Data Warehousing |
| DCADD06 | Introduction to Big Data Lab | Lab | 2 | Hadoop Commands, MapReduce Programming, Spark RDDs and DataFrames, HDFS Operations, Querying NoSQL Databases |
| DCADD07 | Generic Elective II | Generic Elective | 4 | Advanced Topics, Critical Thinking, Research Skills, Domain-Specific Applications, Societal Impact of Technology |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADE01 | Artificial Intelligence | Core | 4 | AI Fundamentals, Search Algorithms, Knowledge Representation, Expert Systems, Machine Learning Integration, Ethical AI Principles |
| DCADE02 | Artificial Intelligence Lab | Lab | 2 | AI Problem Solving, Prolog Programming, Python AI Libraries, Game Playing Algorithms, Knowledge-based Systems Development |
| DCADE03 | Deep Learning | Core | 4 | Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, Backpropagation, Deep Learning Frameworks (TensorFlow/PyTorch) |
| DCADE04 | Deep Learning Lab | Lab | 2 | Building Neural Networks, Image Classification, Natural Language Processing, Model Optimization, GPU Acceleration Techniques |
| DCADE05 | Cloud Computing | Core | 4 | Cloud Service Models (IaaS, PaaS, SaaS), Virtualization Technology, Cloud Security, AWS/Azure/GCP Services, Cloud Deployment Strategies |
| DCADE06 | Cloud Computing Lab | Lab | 2 | Setting up Virtual Machines, Deploying Cloud Applications, Using Cloud Storage, Serverless Functions, Managing Cloud Resources |
| DCADE07 | Data Security and Privacy | Core | 4 | Cryptography Fundamentals, Access Control Mechanisms, Data Encryption, Privacy-Preserving Techniques, Cybersecurity Laws and Regulations, Ethical Hacking Principles |
| DCADE09 | Natural Language Processing (Dept Elective Option 1) | Department Elective | 4 | Text Preprocessing, Word Embeddings, Part-of-Speech Tagging, Sentiment Analysis, Machine Translation, Chatbot Development |
| DCADE09-PR | Natural Language Processing Lab (Dept Elective Option 1 Practical) | Lab | 2 | Text Normalization Techniques, Implementing Word Embeddings, Building Sentiment Classifiers, Seq2Seq Models, Chatbot Frameworks, NLP Toolkits (NLTK, spaCy) |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DCADF01 | Business Intelligence | Core | 4 | BI Architecture, Data Warehousing, OLAP Concepts, ETL Processes, Dashboards and Reporting, Decision Support Systems |
| DCADF02 | Business Intelligence Lab | Lab | 2 | BI Tools (PowerBI, Tableau), Data Modeling Techniques, Dashboard Creation, Report Generation, Data Cube Operations |
| DCADF03 | Research Methodology and Project | Core | 4 | Research Design, Data Collection Methods, Statistical Analysis, Thesis Writing, Research Ethics, Project Management |
| DCADF04 | Research Methodology and Project Lab | Lab | 2 | Data Analysis Software, Survey Design, Research Paper Writing, Presentation Skills, Project Development Planning |
| DCADF05 | Major Project | Project | 6 | Problem Identification, Literature Survey, System Design and Architecture, Implementation and Testing, Project Documentation, Final Presentation and Viva |
| DCADF07 | Data Ethics and Governance (Dept Elective Option 1) | Department Elective | 4 | Ethical AI Principles, Data Privacy Regulations (DPDPA), Data Bias and Fairness, Responsible Data Use, Data Governance Frameworks, Data Security Policies |
| DCADF07-PR | Data Ethics and Governance Lab (Dept Elective Option 1 Practical) | Lab | 2 | Case Studies in Data Ethics, Privacy Enhancing Technologies, Implementing Data Anonymization, Bias Detection in AI Models, Compliance with Data Regulations, Developing Governance Policies |
| DCADF10 | Open Course I | Open Elective | 3 | Introduction to Emerging Fields, Practical Skills Development, Societal Relevance, Interdisciplinary Knowledge, Basic Concepts |
