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B-SC in Data Science at Government College for Women, Hisar
Hisar, Haryana
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About the Specialization
What is Data Science at Government College for Women, Hisar Hisar?
This B.Sc. Data Science program at Government College for Women, Hisar, focuses on equipping students with essential skills in data analysis, machine learning, and programming. Rooted in the growing demand for data professionals across India, this program emphasizes practical application and theoretical foundations, preparing graduates for key roles in various sectors from e-commerce to healthcare, addressing the critical need for data-driven insights.
Who Should Apply?
This program is ideal for 10+2 science graduates with a strong aptitude for mathematics and logical reasoning, seeking entry into the burgeoning field of data science. It also suits individuals passionate about problem-solving through data and those aiming for a career path that combines technology with analytical skills, aspiring to become data analysts, scientists, or machine learning engineers in the Indian market.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as data analysts, business intelligence developers, or junior data scientists, with typical entry-level salaries ranging from INR 3-6 lakhs per annum, growing significantly with experience. The curriculum aligns with industry demands, fostering skills in Python, SQL, and popular ML frameworks, offering strong growth trajectories in Indian IT and analytics companies, and paving the way for advanced studies.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Dedicate significant time to mastering C/C++ and foundational data structures. Practice daily coding challenges on platforms to solidify logic and problem-solving skills, building a robust base for advanced data science concepts.
Tools & Resources
HackerRank, LeetCode (easy), GeeksforGeeks, CodeChef, W3Schools (for C++)
Career Connection
Strong programming skills are the bedrock for any data science role, enhancing problem-solving and making one attractive to recruiters for analyst or junior developer positions.
Develop Strong Mathematical & Statistical Acumen- (Semester 1-2)
Focus intently on applied mathematics and probability courses, understanding the underlying principles. Supplement classroom learning with online resources to grasp concepts like linear algebra and calculus, crucial for advanced algorithms.
Tools & Resources
Khan Academy, NPTEL (for Mathematics/Statistics), NCERT books for fundamentals
Career Connection
A solid mathematical background is indispensable for understanding ML algorithms and statistical modeling, which are core to data science interviews and job roles.
Engage in Peer Learning & Collaborative Projects- (Semester 1-2)
Form study groups with peers to discuss complex topics, share insights, and work on small programming exercises together. Participate in college-level coding contests to build competitive spirit and teamwork skills.
Tools & Resources
GitHub for code sharing, Google Docs for collaborative notes, College coding clubs
Career Connection
Enhances communication, teamwork, and problem-solving abilities – soft skills highly valued in professional environments, preparing for collaborative project work.
Intermediate Stage
Build a Portfolio of Data Projects- (Semester 3-5)
Apply learned concepts from DBMS, Data Mining, and Python to create mini-projects. Use real-world datasets from platforms like Kaggle to solve practical problems, documenting your process and results meticulously.
Tools & Resources
Kaggle, Google Colab, Jupyter Notebook, GitHub for version control
Career Connection
A strong project portfolio is crucial for showcasing practical skills to potential employers, demonstrating your ability to apply theoretical knowledge to solve real-world data challenges.
Gain Practical Experience with Industry Tools- (Semester 3-5)
Beyond theory, spend time hands-on with tools like SQL, Python libraries (Pandas, NumPy, Scikit-learn), and data visualization software. Participate in workshops or online courses to build proficiency in these industry-standard technologies.
Tools & Resources
SQLZoo, Datacamp, Coursera, Official documentation for Python libraries, Tableau Public
Career Connection
Direct experience with industry tools is a key requirement for entry-level data roles, making you job-ready and reducing the learning curve for employers.
Seek Summer Internships & Mentorship- (Semester 3-5)
Actively look for summer internship opportunities (as per the curriculum, e.g., SSP-301) in startups or small to medium enterprises (SMEs). Connect with professionals on LinkedIn for mentorship, gaining insights into industry trends and career paths.
Tools & Resources
LinkedIn, Internshala, Company career pages, College placement cell
Career Connection
Internships provide invaluable real-world experience, expand your professional network, and often lead to pre-placement offers or strong referrals.
Advanced Stage
Specialize and Deepen Machine Learning/Deep Learning Skills- (Semester 6)
Choose elective subjects (DSE-I, DSE-II) wisely based on career interests. Dive deeper into advanced ML/DL topics, frameworks (TensorFlow, PyTorch), and specific applications like NLP or computer vision, building complex models.
Tools & Resources
TensorFlow, PyTorch, Keras documentation, Advanced Kaggle competitions, Specialized online courses
Career Connection
Specialization makes you a valuable asset for specific roles (e.g., ML Engineer, NLP Scientist), providing a competitive edge in a niche market with higher earning potential.
Excel in Capstone Projects & Industrial Training- (Semester 6)
Treat the final year project (DSP-603) and industrial training/internship (DSP-604) as opportunities to showcase your cumulative skills. Tackle challenging problems, deliver measurable results, and effectively present your findings.
Tools & Resources
Project management tools (Trello, Jira), Advanced analytics software, Presentation software
Career Connection
High-quality final projects and successful industrial training experiences are often the deciding factors for securing top placements and demonstrate readiness for professional responsibilities.
Master Interview Skills & Networking- (Semester 6)
Regularly practice technical interview questions focusing on data structures, algorithms, SQL, and machine learning concepts. Attend career fairs, network with alumni, and refine your resume and soft skills for placement success.
Tools & Resources
LeetCode (medium/hard), Pramp (mock interviews), Glassdoor (interview experiences), LinkedIn
Career Connection
Polished interview skills and a strong professional network are essential for converting opportunities into successful job offers and navigating the competitive Indian job market.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 3 years / 6 semesters
Credits: 136 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-101 | Computer Fundamentals | Core | 4 | Computer organization and architecture, Memory hierarchy and I/O devices, Number systems and data representation, Software types: System and Application, Operating system basics |
| DSC-102 | Introduction to Programming using C | Core | 4 | C language basics and structure, Data types, operators, expressions, Control statements: conditional, looping, Functions, arrays, pointers, Structures, unions, file handling |
| DSC-103 | Applied Mathematics-I | Core | 4 | Matrices and determinants, Differential calculus (limits, continuity, derivatives), Integral calculus (integration methods, definite integrals), Vectors and vector algebra, Boolean algebra and logic gates |
| DSC-104 | Communication Skills | Core | 4 | Grammar and vocabulary, Reading comprehension and writing skills, Listening skills and note-taking, Verbal and non-verbal communication, Presentation techniques and public speaking |
| EnvS-101 | Environmental Science | Core | 2 | Ecosystems and biodiversity, Natural resources and management, Environmental pollution and control, Global environmental issues, Sustainable development |
| DSP-101 | Computer Fundamentals Lab | Lab | 2 | Basic operating system commands, File and folder management, Word processing and spreadsheet applications, Presentation software usage, Internet and email usage |
| DSP-102 | Programming in C Lab | Lab | 2 | C program compilation and execution, Implementation of conditional statements, Looping structures and nested loops, Function calls and array manipulation, Pointer usage and basic file operations |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-201 | Data Structures | Core | 4 | Arrays, linked lists (single, double, circular), Stacks and queues (array and linked implementations), Trees (binary trees, BST, AVL trees), Graphs (representation, traversal algorithms), Sorting and searching algorithms |
| DSC-202 | Database Management Systems | Core | 4 | DBMS architecture and data models, Entity-Relationship (ER) model, Relational model and algebra, SQL (DDL, DML, DCL, TCL), Normalization, transactions, concurrency control |
| DSC-203 | Applied Mathematics-II | Core | 4 | Probability theory and distributions, Measures of central tendency and dispersion, Sampling theory and hypothesis testing, Correlation and regression analysis, Linear programming problems |
| DSC-204 | Object-Oriented Programming using C++ | Core | 4 | OOP concepts: encapsulation, inheritance, polymorphism, Classes, objects, constructors, destructors, Operator overloading and function overloading, Virtual functions and abstract classes, Templates, exception handling, file I/O |
| DSP-201 | Data Structures Lab | Lab | 2 | Implementation of linked lists and their operations, Stack and queue operations, Binary tree traversals, Graph representation and traversal, Sorting and searching algorithm implementations |
| DSP-202 | DBMS Lab | Lab | 2 | Creating tables with DDL commands, Inserting, updating, deleting data with DML, SQL queries (SELECT, JOINs, subqueries), Implementing primary and foreign keys, Database backup and restore |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-301 | Operating System | Core | 4 | OS functions and services, Process management and CPU scheduling, Deadlocks and deadlock handling, Memory management techniques, File system organization and I/O management |
| DSC-302 | Computer Networks | Core | 4 | Network models (OSI, TCP/IP), Physical and Data Link Layer concepts, Network Layer: IP addressing, routing protocols, Transport Layer: TCP, UDP, congestion control, Application Layer protocols (HTTP, FTP, DNS) |
| DSC-303 | Data Warehousing and Data Mining | Core | 4 | Data warehousing concepts and architecture, OLAP operations and multidimensional data models, Data mining functionalities and tasks, Association rule mining, Classification and clustering techniques |
| DSC-304 | Artificial Intelligence | Core | 4 | Introduction to AI and its applications, Problem-solving agents and search algorithms, Knowledge representation (logic, semantic nets), Expert systems and fuzzy logic, Machine learning overview |
| DSP-301 | Operating System Lab | Lab | 2 | Linux/Unix commands and utilities, Shell scripting basics, Process creation and management, File system permissions and links, System calls for process and file handling |
| DSP-302 | Data Mining Lab | Lab | 2 | Data preprocessing and cleaning, Implementing classification algorithms (e.g., Decision Tree), Applying clustering algorithms (e.g., K-Means), Discovering association rules, Using data mining tools (e.g., Weka) |
| SSP-301 | Summer Internship | Project | 3 | Practical exposure to industry environment, Application of theoretical knowledge, Project report writing, Presentation and communication skills, Real-world problem-solving |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-401 | Internet and Web Technology | Core | 4 | Internet architecture and protocols, HTML for web page structuring, CSS for styling web pages, JavaScript for client-side scripting, Web servers and hosting |
| DSC-402 | Design and Analysis of Algorithms | Core | 4 | Algorithm analysis and asymptotic notations, Divide and Conquer algorithms, Dynamic Programming, Greedy algorithms, Graph algorithms (DFS, BFS, shortest path) |
| DSC-403 | Python Programming | Core | 4 | Python language fundamentals, Data types and data structures (lists, tuples, dicts), Functions, modules, packages, File I/O and exception handling, Object-Oriented Programming in Python |
| DSC-404 | Computer Graphics | Core | 4 | Graphics primitives and display devices, Line drawing algorithms (Bresenham''''s, DDA), Circle generation algorithms, 2D and 3D transformations, Clipping and hidden surface removal |
| DSP-401 | Python Programming Lab | Lab | 2 | Basic Python scripting, Data manipulation using lists and dictionaries, Functions and module usage, File operations and error handling, Introduction to NumPy and Pandas |
| DSP-402 | Web Technology Lab | Lab | 2 | Designing static web pages with HTML, Styling with CSS (internal, external, inline), Client-side scripting with JavaScript, Form validation and event handling, Introduction to web frameworks |
| GE-401 | Generic Elective | Elective | 3 | Elective options defined by institution/university at the time of offering |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-501 | Machine Learning | Core | 4 | Introduction to machine learning types, Supervised learning (Regression, Classification), Unsupervised learning (Clustering, PCA), Model evaluation and cross-validation, Ensemble methods and neural network basics |
| DSC-502 | Big Data Analytics | Core | 4 | Big Data characteristics (5 V''''s), Hadoop ecosystem (HDFS, MapReduce), Spark architecture and RDDs, Data ingestion and processing techniques, NoSQL databases overview |
| DSC-503 | Data Visualization | Core | 4 | Principles of effective data visualization, Types of charts and graphs, Tools for data visualization (Tableau, Power BI, Matplotlib), Interactive dashboards and storytelling, Visual encoding and perception |
| DSC-504 | Discipline Specific Elective-I | Elective | 4 | Distributed Systems (Architecture, Concurrency, Distributed File Systems), Mobile Computing (Mobile OS, Wireless Technologies, Mobile Application Development), Image Processing (Image Fundamentals, Enhancement, Restoration, Segmentation) |
| DSP-501 | Machine Learning Lab | Lab | 2 | Implementing regression models (linear, logistic), Implementing classification algorithms (SVM, Decision Tree), Applying clustering algorithms (K-Means, hierarchical), Using Scikit-learn and TensorFlow/Keras, Model evaluation and hyperparameter tuning |
| DSP-502 | Big Data Analytics Lab | Lab | 2 | Hadoop/Spark setup and configuration, HDFS commands and file operations, Writing MapReduce programs, Data processing with Spark RDDs/DataFrames, Basic operations with Hive/Pig |
| DSP-503 | Data Visualization Lab | Lab | 2 | Creating static plots with Matplotlib/Seaborn, Interactive visualizations with Plotly/Bokeh, Designing dashboards with Tableau Public, Geospatial data visualization, Storytelling through visual analytics |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-601 | Cloud Computing | Core | 4 | Cloud computing models (IaaS, PaaS, SaaS), Cloud deployment models (private, public, hybrid), Virtualization concepts and technologies, Cloud security challenges and solutions, Introduction to AWS/Azure services |
| DSC-602 | Deep Learning | Core | 4 | Neural network architectures (ANNs, CNNs, RNNs), Backpropagation algorithm, Convolutional Neural Networks for image processing, Recurrent Neural Networks for sequential data, Deep learning frameworks (TensorFlow, Keras, PyTorch) |
| DSC-603 | Discipline Specific Elective-II | Elective | 4 | IoT (IoT Architecture, Sensors & Actuators, Communication Protocols), Natural Language Processing (Text Preprocessing, Word Embeddings, Language Models), Blockchain (Cryptography, Distributed Ledger Technology, Smart Contracts) |
| DSP-601 | Cloud Computing Lab | Lab | 2 | Deploying virtual machines on cloud platforms (e.g., AWS EC2), Configuring cloud storage services (e.g., S3), Setting up web servers in the cloud, Managing cloud resources and monitoring, Basic cloud security configurations |
| DSP-602 | Deep Learning Lab | Lab | 2 | Implementing basic neural networks, Building and training CNNs for image classification, Implementing RNNs for sequence prediction, Using TensorFlow/Keras for deep learning tasks, Hyperparameter tuning for deep models |
| DSP-603 | Project Work | Project | 6 | Project proposal and design, System implementation and development, Testing and debugging, Documentation and report writing, Project presentation and viva-voce |
| DSP-604 | Industrial Training / Internship (Six to Eight Weeks) | Internship | 1 | On-the-job training in a professional environment, Exposure to industry practices and workflows, Application of academic knowledge to real-world problems, Developing professional communication skills, Understanding organizational structure and dynamics |
