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B-SC in Computer Science Data Analytics at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science (Data Analytics) at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Computer Science (Data Analytics) program at SRM Institute of Science and Technology, Chengalpattu focuses on equipping students with essential skills in data management, analysis, and interpretation, crucial for India''''s rapidly expanding digital economy. The curriculum emphasizes both theoretical foundations and practical application, preparing graduates for the high demand in analytics roles across various sectors. It integrates core computer science principles with specialized data analytics techniques, making it a unique offering.
Who Should Apply?
This program is ideal for fresh graduates from science or commerce backgrounds with a strong aptitude for mathematics and problem-solving, seeking entry into the data-driven industry. It also caters to individuals aspiring to become Data Analysts, Business Intelligence Specialists, or Data Scientists, providing them with the necessary technical and analytical prowess. Prerequisites typically include a 10+2 qualification with Mathematics as a subject, ensuring a foundational quantitative ability.
Why Choose This Course?
Graduates of this program can expect promising career paths in India as Data Analysts, Business Intelligence Developers, Junior Data Scientists, or Analytics Consultants. Entry-level salaries typically range from INR 3.5 Lakhs to 6 Lakhs annually, with significant growth potential up to INR 10-15 Lakhs for experienced professionals in leading Indian companies and MNCs. The program also aligns with foundational certifications like Microsoft Certified: Azure Data Scientist Associate or Google''''s Professional Data Engineer.
Student Success Practices
Foundation Stage
Master Programming & Math Fundamentals- (Semester 1-2)
Dedicate significant time to mastering core programming (C, Python) and discrete mathematics concepts. Engage in daily coding challenges on platforms like HackerRank or LeetCode to build problem-solving skills and participate in college-level coding contests. Form study groups to tackle complex math problems and reinforce foundational logic, crucial for advanced data analytics.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Khan Academy, Local study groups
Career Connection
A strong foundation in programming and mathematics is indispensable for any data analytics role, directly impacting your ability to understand algorithms, manipulate data, and excel in technical interviews for placements.
Build a Data-Oriented Portfolio Early- (Semester 1-2)
Start building a personal portfolio by completing small data cleaning or visualization projects. Utilize publicly available datasets from Kaggle. Create interactive dashboards using tools like Tableau Public or Python''''s Streamlit to showcase basic analytical skills, even with limited theoretical knowledge. Document your projects on GitHub.
Tools & Resources
Kaggle, Tableau Public, GitHub, Streamlit, Python (Pandas, Matplotlib)
Career Connection
An early portfolio demonstrates proactive learning and practical skills to recruiters, making your profile stand out during internship and entry-level job applications in India''''s competitive market.
Active Participation in Tech Clubs & Workshops- (Semester 1-2)
Join the Computer Science or Data Science clubs at SRMIST. Actively participate in workshops on new tools (e.g., SQL basics, Excel for data), seminars by industry experts, and intra-college tech events. These activities provide exposure to industry trends and peer learning opportunities beyond the classroom curriculum.
Tools & Resources
SRMIST Tech Clubs, Departmental Workshops, LinkedIn Learning, NPTEL introductory courses
Career Connection
Networking within college clubs can lead to collaborative projects and mentorship, while workshops provide practical skills demanded by Indian IT and analytics companies, enhancing your readiness for industry roles.
Intermediate Stage
Hands-on with Analytics Tools and Platforms- (Semester 3-5)
Deep dive into analytical tools like R, advanced Python libraries (Scikit-learn, TensorFlow), and cloud platforms (AWS/Azure/GCP). Complete practical assignments and independent projects using real-world datasets. Obtain basic certifications (e.g., Python Data Science certificate) to validate your skills.
Tools & Resources
Coursera/edX (specializations), Kaggle Competitions, AWS Educate, Microsoft Learn, Udemy
Career Connection
Proficiency in these tools is a primary requirement for data analytics roles. Practical experience and certifications significantly boost your employability for internships and mid-level roles in Indian tech firms.
Engage in Mini-Projects and Group Studies- (Semester 3-5)
Collaborate on mini-projects with peers, focusing on specific data analytics challenges. Utilize version control (Git/GitHub) for group work. Participate in hackathons and data challenges, which simulate real-world problem-solving scenarios. These experiences strengthen teamwork and project management skills.
Tools & Resources
GitHub, Jupyter Notebooks, Google Colab, Hackathon platforms (Devpost)
Career Connection
Team-based projects and hackathon participation demonstrate your ability to apply theoretical knowledge, work collaboratively, and deliver under pressure, highly valued by Indian employers.
Seek Early Industry Exposure through Internships- (Semester 3-5)
Actively search for summer internships (even unpaid initially) in local startups, SMEs, or even larger companies if possible. Focus on roles involving data entry, data cleaning, or basic reporting to gain initial corporate experience and understand industry workflows. Leverage SRMIST''''s placement cell for leads.
Tools & Resources
LinkedIn Jobs, Internshala, SRMIST Placement Cell, Naukri.com
Career Connection
Early internships are crucial for building a professional network and gaining practical experience. They often lead to pre-placement offers or provide a significant advantage during final placements in India.
Advanced Stage
Specialize and Undertake Capstone Projects- (Semester 6)
Focus on a specific area within data analytics (e.g., NLP, computer vision, financial analytics) by choosing relevant electives and dedicating your major project to it. Work on a comprehensive capstone project that addresses a real-world problem, showcasing end-to-end data analytics pipeline implementation. Publish your work on GitHub.
Tools & Resources
Advanced ML/DL frameworks, Domain-specific datasets, Research papers, Academic Mentors
Career Connection
A strong, specialized capstone project is a powerful asset for demonstrating expertise to potential employers, especially for specialized data science or analytics roles, and can differentiate you for higher-paying positions.
Intensive Placement Preparation & Mock Interviews- (Semester 6)
Begin rigorous preparation for placement tests covering aptitude, logical reasoning, and technical skills specific to data analytics. Participate in mock interviews (technical, HR, case study) organized by the college or through professional platforms. Refine your resume and LinkedIn profile to highlight projects and skills effectively.
Tools & Resources
Placement Training Modules, InterviewBit, GeeksforGeeks Interview Prep, LinkedIn, Resume Builders
Career Connection
Thorough preparation for placement processes is critical for securing coveted roles in India''''s top tech companies. Mock interviews help in performance enhancement and confidence building.
Network Strategically & Continuous Learning- (Semester 6 & Beyond)
Attend industry conferences, webinars, and workshops. Connect with professionals on LinkedIn, seeking mentorship and insights. Explore advanced topics via MOOCs and stay updated with the latest trends in AI and data analytics through blogs and research. Consider pursuing further education like an M.Sc. or MBA in Data Science.
Tools & Resources
LinkedIn, Industry Meetups (e.g., PyData meetups), Medium blogs, MOOCs (DeepLearning.AI), Professional Associations
Career Connection
Strategic networking opens doors to advanced job opportunities and collaborative ventures. Continuous learning ensures long-term career growth and adaptability in the dynamic Indian analytics landscape, keeping you relevant for future roles.
Program Structure and Curriculum
Eligibility:
- Candidate must have passed 10+2 / HSC / Equivalent Examination with Mathematics as one of the subjects.
Duration: 3 years (6 semesters)
Credits: 140 Credits
Assessment: Internal: 50% (Continuous Assessment), External: 50% (End Semester Examination)
Semester-wise Curriculum Table
Semester 1
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH102T | Technical English | Core | 3 | Technical Report Writing, Business Correspondence, Presentation Skills, Effective Communication, Resume Building |
| 21MAT102T | Discrete Mathematics | Core | 4 | Set Theory and Logic, Relations and Functions, Graph Theory, Combinatorics, Recurrence Relations |
| 21CSS105T | Data Structures and Algorithms | Core | 4 | Arrays and Linked Lists, Stacks and Queues, Trees and Graphs, Sorting Algorithms, Searching Algorithms |
| 21CSS105L | Data Structures and Algorithms Lab | Lab | 2 | Implementation of Data Structures, Algorithm Design, Time and Space Complexity, Problem Solving, Debugging |
| 21CSS106T | Operating Systems | Core | 4 | OS Concepts, Process Management, Memory Management, File Systems, I/O Management |
| 21CSS106L | Operating Systems Lab | Lab | 2 | Linux Commands, Shell Scripting, Process Management Utilities, System Calls, Basic Network Configuration |
| 21CSS107T | Data Visualization | Core | 2 | Principles of Data Visualization, Chart Types and Selection, Data Storytelling, Interactive Visualizations, Tools like Tableau/Power BI |
| 21CSS107L | Data Visualization Lab | Lab | 2 | Python Libraries for Visualization (Matplotlib, Seaborn), Data Exploration with Visuals, Dashboard Design, Geo-spatial Visualization, Web-based Visualization |
