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BCA in Data Science at ISBC College of Arts, Science and Commerce
Bengaluru, Karnataka
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About the Specialization
What is Data Science at ISBC College of Arts, Science and Commerce Bengaluru?
This Data Science program at ISBC College of Arts, Science and Commerce, affiliated with Bangalore University, focuses on equipping students with essential skills in statistical analysis, machine learning, and big data technologies. It aims to prepare graduates for the rapidly evolving data-driven landscape in India, emphasizing both theoretical foundations and practical application to real-world challenges. The curriculum is designed to foster analytical thinking and problem-solving abilities.
Who Should Apply?
This program is ideal for fresh 10+2 graduates who possess a strong analytical mindset, a keen interest in mathematics and statistics, and aspire to build a career in the burgeoning field of data science. It also caters to individuals seeking foundational knowledge in data analysis, business intelligence, or those looking to transition into data-centric roles within various industries across India.
Why Choose This Course?
Graduates of this program can expect to pursue rewarding career paths such as Data Analyst, Junior Data Scientist, Business Intelligence Developer, or Machine Learning Assistant. Entry-level salaries in India typically range from 3 to 6 LPA, with experienced professionals earning 8 to 15+ LPA. The program aligns with industry demand, paving the way for growth into specialized roles and leadership positions in data science.
Student Success Practices
Foundation Stage
Master Programming Logic and Fundamentals- (Semester 1-2)
Dedicate time to thoroughly understand programming concepts in C and Python. Practice daily on online coding platforms like HackerRank, GeeksforGeeks, and CodeChef to build strong problem-solving skills and algorithmic thinking early on.
Tools & Resources
HackerRank, GeeksforGeeks, CodeChef, Python.org Tutorials
Career Connection
A solid programming foundation is crucial for all data science roles, forming the basis for implementing complex algorithms and data processing tasks in future projects and job interviews.
Strengthen Mathematical and Statistical Foundations- (Semester 1-2)
Actively review and deepen understanding of 11th and 12th-grade mathematics, especially calculus, linear algebra, and basic probability. Supplement with NPTEL courses or online resources like Khan Academy for a stronger grasp of statistical concepts vital for data science.
Tools & Resources
Khan Academy, NPTEL Courses (Mathematics/Statistics), Textbooks for Probability and Statistics
Career Connection
A strong quantitative aptitude and statistical understanding are critical for interpreting data, building accurate models, and excelling in analytics-focused roles.
Engage in Peer Learning and Academic Clubs- (Semester 1-2)
Form study groups, participate actively in college coding clubs, and attend workshops. Collaborative learning helps clarify doubts, exposes you to different problem-solving approaches, and builds a supportive academic network within the institution.
Tools & Resources
College Coding Clubs, Study Groups, Departmental Workshops
Career Connection
Develops teamwork and communication skills, which are highly valued in industry, and fosters a competitive yet collaborative learning environment beneficial for technical growth.
Intermediate Stage
Undertake Hands-on Data Projects and Competitions- (Semester 3-5)
Apply theoretical knowledge by working on small to medium-sized data science projects using real-world datasets from platforms like Kaggle. Participate in college or online data challenges to gain practical experience in data cleaning, analysis, and model building.
Tools & Resources
Kaggle, GitHub, Datasets from UCI Machine Learning Repository
Career Connection
Building a project portfolio demonstrates practical skills to potential employers and provides tangible experience, enhancing employability in entry-level data science roles.
Master Industry-Relevant Tools and Libraries- (Semester 3-5)
Gain proficiency in essential data science tools beyond basic programming. Focus on Python libraries like Pandas, NumPy, Scikit-learn, and Matplotlib. Learn SQL for database interaction and explore data visualization tools such as Tableau or Power BI.
Tools & Resources
Jupyter Notebook, Anaconda Distribution, SQL Editors, Tableau Public, Microsoft Power BI Desktop
Career Connection
Proficiency in these tools is a fundamental requirement for most data analyst and data scientist positions, making you job-ready for various industry roles.
Build Professional Network and Seek Mentorship- (Semester 3-5)
Attend local tech meetups, webinars, and industry events in Bengaluru. Connect with alumni and industry professionals on LinkedIn. Seek mentorship from faculty or experts to gain insights into career paths and specialized fields within data science.
Tools & Resources
LinkedIn, Meetup.com (for local tech events), College Alumni Network
Career Connection
Networking opens doors to internship and job opportunities, while mentorship provides guidance and accelerates professional development in the competitive Indian job market.
Advanced Stage
Secure and Maximize Internship Experience- (Semester 6)
Actively search for and complete internships in data science, analytics, or related fields. Focus on applying learned concepts to real business problems, contributing meaningfully to projects, and building strong professional relationships with your mentors and team.
Tools & Resources
Internshala, LinkedIn Jobs, College Placement Cell
Career Connection
Internships are often the direct pathway to full-time employment in India and provide invaluable industry exposure, making you highly competitive for entry-level data scientist positions.
Specialize in Advanced Data Science Domains- (Semester 6)
Beyond core machine learning, explore a specialization like Deep Learning, Natural Language Processing, or Big Data Engineering. Pursue advanced online certifications or electives that align with your career interests and deepen your expertise in a niche area.
Tools & Resources
Coursera, edX, Udemy (for specialized courses), Google AI Platform
Career Connection
Specialized skills differentiate you in the job market, enabling you to target specific, high-demand roles and potentially command higher salaries in advanced data science fields.
Intensive Placement and Interview Preparation- (Semester 6)
Engage in rigorous preparation for placements including mock interviews (technical and HR), aptitude tests, and resume building workshops. Focus on communicating your project experience effectively and clearly articulating your problem-solving approach to interviewers.
Tools & Resources
Mock Interview Platforms, Resume Building Services, Placement Guides from College
Career Connection
Thorough preparation ensures you can confidently showcase your skills and experience, maximizing your chances of securing desirable job offers from top companies in India.
Program Structure and Curriculum
Eligibility:
- Pass in 10+2 / PUC / equivalent with Mathematics or Computer Science or Business Mathematics or Statistics as one of the subjects.
Duration: 6 semesters / 3 years
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC1 | Fundamentals of Computer Science | Core | 4 | Introduction to Computers, Data Representation, Computer Memory and I/O, Operating Systems Concepts, Software and Programming Language Basics |
| DSC2 | Programming in C | Core | 4 | C Language Fundamentals, Control Structures, Functions and Pointers, Arrays and Strings, Structures and Union, File Handling |
| DSC3 | Discrete Mathematical Structures | Core | 4 | Set Theory, Relations and Functions, Logic and Propositional Calculus, Graph Theory, Combinatorics, Boolean Algebra |
| DSC2L | C Programming Lab | Lab | 2 | Program Design and Implementation, Conditional and Looping Constructs, Functions and Array Manipulation, Pointer Usage, File Operations |
| AECC1 | English | Ability Enhancement Compulsory Course | 2 | Communication Skills, Grammar and Vocabulary, Reading Comprehension, Report Writing, Presentation Skills |
| OE1 | Open Elective 1 | Open Elective | 3 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC4 | Data Structures | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Trees, Graphs and Traversals, Sorting and Searching Algorithms, Hashing |
| DSC5 | Object Oriented Programming with C++ | Core | 4 | OOP Concepts, Classes and Objects, Inheritance and Polymorphism, Abstraction and Encapsulation, Constructors and Destructors, Exception Handling |
| DSC6 | Database Management System | Core | 4 | DBMS Concepts and Architecture, ER Modeling, Relational Model and Algebra, SQL Queries, Normalization, Transaction Management |
| DSC4L | Data Structures Lab | Lab | 2 | Linked List Operations, Stack and Queue Implementation, Tree Traversal Algorithms, Graph Representation and Traversals, Sorting and Searching Practice |
| DSC5L | Object Oriented Programming with C++ Lab | Lab | 2 | Class and Object Creation, Inheritance Implementation, Polymorphism Demonstrations, File I/O Operations, Exception Handling Practice |
| AECC2 | Environmental Studies | Ability Enhancement Compulsory Course | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Environmental Ethics, Sustainable Development |
| OE2 | Open Elective 2 | Open Elective | 3 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC7 | Operating System | Core | 4 | OS Introduction and Types, Process Management, CPU Scheduling, Memory Management, File Systems, Deadlocks |
| DSC8 | Python Programming | Core | 4 | Python Basics and Data Types, Control Flow and Functions, Lists, Tuples, Dictionaries, Modules and Packages, File I/O, Object-Oriented Python |
| DSC9 | Computer Networks | Core | 4 | Network Topologies and Models, OSI and TCP/IP Models, Physical Layer Concepts, Data Link Layer Protocols, Network Layer Addressing and Routing, Transport Layer and Application Layer |
| DSC8L | Python Programming Lab | Lab | 2 | Basic Python Programs, Function and Module Usage, Data Structure Manipulation, File Handling, Simple OOP Concepts |
| SEC1 | Data Science Fundamentals | Skill Enhancement Course (Specialization) | 3 | Introduction to Data Science, Data Types and Sources, Data Collection and Cleaning, Exploratory Data Analysis, Basic Data Visualization, Data Science Tools Overview |
| OE3 | Open Elective 3 | Open Elective | 3 | |
| VAC1 | Value Added Course 1 | Value Added Course | 1 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC10 | Introduction to Data Analytics | Core (Specialization) | 4 | Data Analytics Process, Descriptive Statistics, Inferential Statistics, Hypothesis Testing, Regression Analysis, Data Driven Decision Making |
| DSC11 | Web Programming | Core | 4 | HTML5 and CSS3, JavaScript Fundamentals, DOM Manipulation, Responsive Design, Web Development Frameworks Overview, Server-Side Scripting Basics |
| DSC12 | Java Programming | Core | 4 | Java Fundamentals, OOP in Java, Inheritance and Interfaces, Exception Handling, Multithreading, JDBC and Database Connectivity |
| DSC10L | Data Analytics Lab | Lab (Specialization) | 2 | Data Cleaning and Preparation, Statistical Analysis using Python/R, Data Visualization with Libraries, Regression Model Building, Hypothesis Testing Implementation |
| DSC11L | Web Programming Lab | Lab | 2 | HTML/CSS Page Design, JavaScript Interactive Elements, Form Validation, AJAX Concepts, Simple Web Application Development |
| SEC2 | Machine Learning Essentials | Skill Enhancement Course (Specialization) | 3 | Introduction to Machine Learning, Supervised Learning, Unsupervised Learning, Regression and Classification Models, Model Evaluation Metrics, Introduction to Scikit-learn |
| OE4 | Open Elective 4 | Open Elective | 3 | |
| VAC2 | Value Added Course 2 | Value Added Course | 1 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC13 | Data Warehousing and Data Mining | Discipline Specific Core (Specialization) | 4 | Data Warehouse Architecture, ETL Processes, OLAP Operations, Data Preprocessing for Mining, Association Rule Mining, Classification and Clustering Techniques |
| DSC14 | Big Data Technologies | Discipline Specific Core (Specialization) | 4 | Introduction to Big Data, Hadoop Ecosystem, HDFS and MapReduce, Apache Spark Basics, NoSQL Databases (MongoDB/Cassandra), Data Streaming Concepts |
| DSC15 | Artificial Intelligence Concepts | Discipline Specific Core | 4 | Introduction to AI, Problem Solving by Searching, Knowledge Representation, Machine Learning Overview, Expert Systems, AI Ethics |
| DSC13L | Data Warehousing and Mining Lab | Lab (Specialization) | 2 | SQL for Data Extraction, ETL Tool Usage (e.g., Pentaho), Data Cube Creation, Data Mining Algorithm Implementation (Weka/Python), Reporting and Analysis |
| DSC14L | Big Data Technologies Lab | Lab (Specialization) | 2 | Hadoop Commands, MapReduce Programming, Spark RDD Operations, NoSQL Database Interaction, Data Ingestion into Big Data Systems |
| DSE1 | Deep Learning | Discipline Specific Elective (Specialization) | 3 | Neural Network Architecture, Perceptrons and Backpropagation, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), TensorFlow/Keras Basics, Deep Learning Applications |
| DSE2 | Data Visualization Techniques | Discipline Specific Elective (Specialization) | 3 | Principles of Data Visualization, Chart Types and Selection, Tools: Tableau/Power BI/D3.js, Interactive Dashboards, Data Storytelling, Infographics |
| SEC3 | Project Work (Data Science) | Skill Enhancement Course (Specialization Project) | 3 | Problem Identification, Data Collection and Preprocessing, Model Selection and Training, Result Analysis and Reporting, Presentation Skills |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC16 | Cloud Computing for Data Science | Discipline Specific Core (Specialization) | 4 | Cloud Computing Basics, Service and Deployment Models, Cloud Providers (AWS/Azure/GCP), Big Data on Cloud, Serverless Computing, Cloud Security Considerations |
| DSC17 | Business Intelligence | Discipline Specific Core (Specialization) | 4 | BI Concepts and Architecture, Data Mining for BI, Reporting and Dashboards, Performance Management, Predictive Analytics, BI Tools Overview |
| DSC16L | Cloud Computing Lab for Data Science | Lab (Specialization) | 2 | Cloud Platform Navigation, Setting up Virtual Machines, Using Cloud Storage Services, Deploying Data Science Models, Working with Cloud-based Big Data Services |
| DSC17L | Business Intelligence Lab | Lab (Specialization) | 2 | Data Extraction and Transformation, Creating Reports with BI Tools, Designing Interactive Dashboards, Performing Ad-hoc Analysis, Data Presentation Techniques |
| DSE3 | Natural Language Processing | Discipline Specific Elective (Specialization) | 3 | Text Preprocessing, Tokenization and Stemming, Word Embeddings, Sentiment Analysis, Text Classification, Chatbot Development Basics |
| DSE4 | Data Ethics and Governance | Discipline Specific Elective (Specialization) | 3 | Data Privacy and Security, Ethical AI Principles, Data Governance Frameworks, Regulatory Compliance (e.g., GDPR, PDP Bill), Bias in AI Systems, Responsible Data Handling |
| SEC4 | Internship / Industrial Training | Skill Enhancement Course (Internship) | 3 | Industry Exposure, Practical Application of Skills, Professional Networking, Project Implementation in Industry, Technical Report Writing |
| RM | Research Methodology | Research Methodology Course | 2 | Introduction to Research, Research Design, Data Collection Methods, Statistical Analysis in Research, Report Writing and Presentation |
