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BACHELOR-OF-COMPUTER-APPLICATION in Data Science at Canara College
Dakshina Kannada, Karnataka
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About the Specialization
What is Data Science at Canara College Dakshina Kannada?
This Data Science program at Canara College, affiliated with Mangalore University, focuses on equipping students with the skills to analyze, interpret, and leverage large datasets. The curriculum, embedded within the BCA NEP framework, provides a strong foundation in computer applications while allowing students to specialize through crucial electives in data mining, big data analytics, machine learning, and deep learning. This program is highly relevant in India''''s rapidly growing digital economy, where data-driven decision-making is paramount across sectors.
Who Should Apply?
This program is ideal for fresh graduates with a 10+2 background, eager to enter the dynamic field of data science. It also caters to aspiring data analysts, data engineers, and machine learning enthusiasts looking for a structured academic path. Individuals with strong logical reasoning and a keen interest in statistical analysis and programming will find this specialization particularly rewarding, offering a gateway into India''''s tech ecosystem.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Junior Data Analyst, Business Intelligence Developer, Machine Learning Engineer (entry-level), or Big Data Engineer in Indian companies and MNCs. Entry-level salaries typically range from INR 3-6 LPA, with significant growth potential up to INR 10-15 LPA or more with experience. The program prepares students for professional certifications in areas like Python for Data Science, AWS Machine Learning, and Google Cloud Data Engineering.
Student Success Practices
Foundation Stage
Master Programming Fundamentals (Python & Java)- (Semester 1-2)
Dedicate consistent time to practice core programming concepts in Python and Java. Utilize online platforms like HackerRank, LeetCode, and GeeksforGeeks to solve daily coding challenges. Understand data structures and algorithms thoroughly, as they form the backbone of advanced computing and data science.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, NPTEL courses for Data Structures
Career Connection
Strong programming skills are non-negotiable for placements in any tech role, including data science. Proficiency ensures you can implement algorithms, clean data, and build prototypes efficiently.
Build a Solid Mathematical & Statistical Base- (Semester 1-3)
Pay close attention to Discrete Mathematics, Logic, and foundational statistics covered in core courses. Supplement classroom learning with online resources like Khan Academy for linear algebra, calculus, and probability, which are critical for understanding advanced data science concepts.
Tools & Resources
Khan Academy, MIT OpenCourseWare (Mathematics), NPTEL (Probability and Statistics)
Career Connection
A robust mathematical understanding helps in comprehending machine learning algorithms, statistical modeling, and interpreting data insights, crucial for analytical roles.
Develop Strong Problem-Solving Acumen- (Semester 1-3)
Engage actively in labs and small projects. Learn to break down complex problems into smaller, manageable parts. Participate in campus hackathons or mini-project competitions to apply theoretical knowledge and develop critical thinking skills.
Tools & Resources
Competitive programming platforms, Local hackathons, Github for project collaboration
Career Connection
Employers highly value problem-solving skills. Demonstrating this through practical projects enhances your resume and interview performance for analytical and developer roles.
Intermediate Stage
Dive Deep into Data Handling & SQL- (Semester 3-4)
Excel in Database Management Systems (DBMS) and practice advanced SQL queries extensively. Work on real-world datasets to perform data extraction, transformation, and loading (ETL). Explore tools like MySQL/PostgreSQL and familiarize yourself with data warehousing concepts.
Tools & Resources
SQLZoo, LeetCode SQL problems, Kaggle datasets (for ETL practice)
Career Connection
Proficiency in SQL and database management is fundamental for any data role, as data professionals spend a significant amount of time cleaning and preparing data for analysis.
Explore Data Science Tools & Libraries (R/Python)- (Semester 4-5)
Beyond basic Python/R, focus on data science-specific libraries like Pandas, NumPy, Matplotlib, Seaborn in Python, or dplyr, ggplot2 in R. Work on exploratory data analysis (EDA) projects, visualizing data to uncover insights. Share your work on platforms like GitHub.
Tools & Resources
DataCamp, Coursera (Python for Data Science), Anaconda Distribution, GitHub
Career Connection
Hands-on experience with these tools is expected in data science roles. Practical projects showcasing your EDA skills are excellent portfolio builders for internships and jobs.
Seek Internships and Industry Exposure- (Semester 4-5)
Actively apply for internships in data analytics, business intelligence, or software development during summer breaks. Even short-term internships provide invaluable practical experience, industry networking, and a clearer understanding of career paths within data science in India.
Tools & Resources
LinkedIn Jobs, Internshala, College placement cell
Career Connection
Internships convert into full-time roles or provide strong references. They bridge the gap between academic knowledge and industry expectations, making you job-ready.
Advanced Stage
Master Machine Learning and Deep Learning- (Semester 5-6)
Deeply understand the theoretical foundations and practical applications of machine learning and deep learning algorithms. Implement models from scratch and use frameworks like Scikit-learn, TensorFlow, and Keras. Participate in Kaggle competitions to apply skills and learn from diverse datasets.
Tools & Resources
Kaggle, TensorFlow/Keras documentation, Scikit-learn documentation, NPTEL (ML/DL)
Career Connection
Expertise in ML/DL is crucial for roles like Machine Learning Engineer, Data Scientist. Kaggle successes demonstrate real-world problem-solving abilities and competitive edge.
Build a Strong Project Portfolio & Network- (Semester 5-6)
Work on at least 2-3 significant end-to-end data science projects, including data collection, cleaning, modeling, and deployment (even a basic one). Document these thoroughly on GitHub with clear explanations. Attend industry webinars, workshops, and connect with professionals on LinkedIn.
Tools & Resources
GitHub, LinkedIn, Industry conferences (virtual/local)
Career Connection
A robust project portfolio is essential for showcasing your capabilities to recruiters. Networking can lead to mentorship, job opportunities, and staying updated with industry trends.
Prepare for Placements and Mock Interviews- (Semester 6)
Begin placement preparation early. Practice aptitude tests, technical rounds covering data structures, algorithms, SQL, and ML concepts. Participate in mock interviews with peers and faculty to refine communication skills and build confidence for campus placements.
Tools & Resources
Placement preparation guides, Mock interview sessions, Online aptitude tests
Career Connection
Systematic preparation significantly increases your chances of securing placements in top companies. Effective communication and problem-solving during interviews are key to success.
Program Structure and Curriculum
Eligibility:
- Passed PUC/10+2 or equivalent examination with English as one of the languages and obtained a minimum of 35% of marks in aggregate of all subjects (as per Canara College BCA eligibility)
Duration: 6 semesters (3 years) detailed, with provision for 8 semesters (4 years)
Credits: 132 (for 6 semesters) Credits
Assessment: Internal: 40% (for Theory subjects), 50% (for Practical subjects), External: 60% (for Theory subjects), 50% (for Practical subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 1 | Problem Solving Techniques | Core | 4 | Algorithms and Flowcharts, Python Programming Fundamentals, Data Types and Control Structures, Functions and Modules, File Handling and Exception Handling |
| DSC 2 | Digital Fluency | Core | 4 | Number Systems and Binary Codes, Logic Gates and Boolean Algebra, Combinational and Sequential Logic, Computer Organization Basics, Memory and I/O Devices |
| DSC 3 | Computer Fundamentals and Office Automation | Core | 4 | Introduction to Computers, Hardware and Software Concepts, Operating Systems, Microsoft Word and Excel, Microsoft PowerPoint and Access |
| AECC 1 | English | AECC | 2 | Language Skills, Communication Strategies, Grammar and Vocabulary, Reading Comprehension, Report Writing |
| AECC 2 | Indian Constitution | AECC | 2 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Government, Judiciary and Elections, Constitutional Amendments |
| DSC 1P | Problem Solving Techniques Lab | Lab | 2 | Python Programming Exercises, Conditional Statements, Looping Constructs, Functions Implementation, File Operations |
| DSC 2P | Digital Fluency Lab | Lab | 2 | Logic Gate Simulations, Boolean Algebra Simplification, Combinational Circuit Design, Memory Unit Concepts, Basic Computer Organization Experiments |
| DSC 3P | Office Automation Lab | Lab | 2 | Word Processing using MS Word, Spreadsheet Management with MS Excel, Presentation Graphics using MS PowerPoint, Database Operations with MS Access, Integrating Office Tools |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 4 | Data Structures | Core | 4 | Arrays and Pointers, Stacks and Queues, Linked Lists, Trees and Graphs, Sorting and Searching Algorithms |
| DSC 5 | Object Oriented Programming using Java | Core | 4 | OOP Concepts: Classes and Objects, Inheritance and Polymorphism, Interfaces and Packages, Exception Handling and Multithreading, GUI Programming with AWT/Swing |
| DSC 6 | Logic and Discrete Mathematics | Core | 4 | Set Theory and Relations, Mathematical Logic and Proofs, Functions and Sequences, Graph Theory, Combinatorics and Recurrence Relations |
| AECC 3 | Language (Kannada/Sanskrit/Hindi etc.) | AECC | 2 | Regional Language Grammar, Communication Skills, Cultural Context, Literature Appreciation, Writing Practice |
| SEC 1 | Web Designing | Skill Enhancement | 2 | HTML5 Structure and Elements, CSS3 Styling and Layouts, JavaScript Basics and DOM Manipulation, Responsive Web Design, Web Hosting and Deployment |
| DSC 4P | Data Structures Lab | Lab | 2 | Implementation of Stacks and Queues, Linked List Operations, Tree Traversal Algorithms, Graph Representation and Traversal, Sorting and Searching Practice |
| DSC 5P | Java Programming Lab | Lab | 2 | Class and Object Creation, Inheritance and Polymorphism Exercises, Exception Handling Implementations, File I/O Operations, GUI Application Development |
| SEC 1P | Web Designing Lab | Lab | 2 | HTML Document Structure, CSS Styling of Web Pages, JavaScript Interactive Elements, Form Design and Validation, Building Simple Web Pages |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 7 | Database Management System | Core | 4 | Database Concepts and Architecture, ER Modeling and Relational Model, SQL Queries and Operations, Normalization Techniques, Transaction Management and Concurrency Control |
| DSC 8 | Operating System | Core | 4 | OS Functions and Types, Process Management and CPU Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency |
| DSC 9 | Software Engineering | Core | 4 | Software Life Cycle Models, Requirements Engineering, Software Design Principles, Software Testing Strategies, Software Project Management |
| AECC 4 | Environmental Studies | AECC | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Climate Change and Global Warming, Environmental Ethics and Legislation |
| SEC 2 | Python Programming | Skill Enhancement | 2 | Python Syntax and Data Structures, Functions and Modules, Object-Oriented Programming in Python, Error Handling, Libraries for Data Manipulation |
| DSC 7P | DBMS Lab | Lab | 2 | SQL DDL and DML Commands, Joining Tables, Aggregate Functions, Stored Procedures and Triggers, Database Application Development |
| DSC 8P | Operating System Lab | Lab | 2 | Shell Scripting, Process Management Commands, CPU Scheduling Algorithms Simulation, Memory Management Techniques, File System Operations |
| SEC 2P | Python Programming Lab | Lab | 2 | Python Basic Programs, Working with Lists, Tuples, Dictionaries, Function Implementation, File I/O in Python, Using Python Libraries |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 10 | Computer Networks | Core | 4 | Network Topologies and Devices, OSI and TCP/IP Models, Data Link Layer Protocols, Network Layer: IP Addressing and Routing, Transport Layer and Application Layer Protocols |
| DSC 11 | Artificial Intelligence | Core | 4 | Introduction to AI and Intelligent Agents, Search Algorithms (BFS, DFS, A*), Knowledge Representation, Expert Systems, Introduction to Machine Learning |
| DSC 12 | Theory of Computation | Core | 4 | Finite Automata and Regular Expressions, Context-Free Grammars and Pushdown Automata, Turing Machines, Decidability and Undecidability, Complexity Classes (P, NP) |
| OE 1 | Open Elective - I | Elective | 3 | Interdisciplinary subject choice, Enhancing general knowledge, Non-core domain exploration, Developing diverse skill sets, Broadening academic perspective |
| SEC 3 | R Programming | Skill Enhancement | 2 | R Environment and Basics, Data Types and Structures in R, Control Structures and Functions, Data Manipulation with R, Statistical Graphics in R |
| DSC 10P | Computer Networks Lab | Lab | 2 | Network Configuration Commands, Socket Programming, Packet Analysis using Wireshark, Routing Protocols Simulation, Network Security Tools |
| DSC 11P | Artificial Intelligence Lab | Lab | 2 | Implementing Search Algorithms, Prolog/Python for AI, Knowledge Representation Examples, Simple Expert Systems, Mini AI Project |
| SEC 3P | R Programming Lab | Lab | 2 | Data Import and Export in R, Data Cleaning and Transformation, Basic Statistical Analysis, Creating Visualizations with ggplot2, Writing R Scripts |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 13 | Full Stack Development | Core | 4 | Frontend Frameworks (e.g., React/Angular), Backend Frameworks (e.g., Node.js/Django), Database Integration, RESTful API Development, Deployment and Hosting |
| DSC 14 | Data Warehousing and Data Mining | Core - Data Science | 4 | Data Warehouse Architecture and Design, ETL Process and OLAP, Data Preprocessing Techniques, Association Rule Mining, Classification and Clustering Algorithms |
| DSE 5.1 | Big Data Analytics | Elective - Data Science | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Spark Framework, NoSQL Databases, Data Stream Processing |
| OE 2 | Open Elective - II | Elective | 3 | Diverse subject selection, Skill enhancement in non-core areas, Personal interest pursuits, Interdisciplinary learning, Building complementary knowledge |
| DSC 13P | Full Stack Development Lab | Lab | 2 | Building Frontend Components, Developing Backend APIs, Integrating Frontend and Backend, Database Operations via API, Simple Web Application Deployment |
| DSC 14P | Data Warehousing and Data Mining Lab | Lab - Data Science | 2 | Data Preprocessing using Tools, Implementing Association Rules, Classification Algorithms Practice, Clustering Techniques, OLAP Cube Creation |
| DSE 5.1P | Big Data Analytics Lab | Lab - Data Science | 2 | HDFS Commands and Operations, MapReduce Programming, Spark RDD and DataFrames, Working with NoSQL Databases, Implementing Big Data Workflows |
| Project I | Project Work - I | Project | 2 | Problem Identification, Requirements Gathering, System Design, Module Development, Documentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC 15 | Cyber Security | Core | 4 | Information Security Principles, Network Security Threats and Defenses, Cryptography and Encryption, Web Security Vulnerabilities, Cyber Laws and Ethics |
| DSC 16 | Machine Learning | Core - Data Science | 4 | Introduction to Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation and Validation, Ensemble Methods |
| DSE 6.1 | Deep Learning | Elective - Data Science | 3 | Neural Network Fundamentals, Perceptrons and Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transfer Learning and Fine-tuning |
| OE 3 | Open Elective - III | Elective | 3 | Diverse subject choices for personal growth, Complementary skill development, Exploring new academic domains, Application of theoretical knowledge, Preparing for varied career paths |
| DSC 15P | Cyber Security Lab | Lab | 2 | Network Scanning Tools, Vulnerability Assessment, Basic Cryptography Implementation, Firewall Configuration, Web Application Security Testing |
| DSC 16P | Machine Learning Lab | Lab - Data Science | 2 | Implementing Regression Models, Classification Algorithms in Python, Clustering Techniques Application, Model Hyperparameter Tuning, Using Scikit-learn Library |
| DSE 6.1P | Deep Learning Lab | Lab - Data Science | 2 | Building Neural Networks with Keras/TensorFlow, Implementing CNN for Image Classification, RNN for Sequence Data, Deep Learning Model Training, Using Pre-trained Models |
| Project II | Project Work - II | Project | 2 | Advanced System Development, Testing and Debugging, Deployment and Presentation, Report Writing, Team Collaboration |
