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B-E in Computer Science Engineering Data Science at Acharya Institute of Technology
Bengaluru, Karnataka
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About the Specialization
What is Computer Science & Engineering (Data Science) at Acharya Institute of Technology Bengaluru?
This Computer Science & Engineering (Data Science) program at Acharya Institute of Technology focuses on equipping students with expertise in data analytics, machine learning, and big data technologies. In the burgeoning Indian digital economy, this program addresses the critical need for skilled professionals capable of extracting insights from complex datasets. Its robust curriculum blends theoretical foundations with practical applications, preparing graduates for high-demand roles.
Who Should Apply?
This program is ideal for fresh graduates with a strong aptitude for mathematics, statistics, and programming, seeking entry into data-driven careers. It also caters to working professionals aiming to upskill in cutting-edge data science methodologies or career changers transitioning into the rapidly expanding data and AI industry. A foundational understanding of computer science principles is beneficial.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India as Data Scientists, Machine Learning Engineers, Data Analysts, or Big Data Specialists. Entry-level salaries typically range from INR 4-8 lakhs per annum, with experienced professionals earning significantly more. The program aligns with industry certifications, fostering continuous growth in analytics and artificial intelligence domains within top Indian companies and startups.
Student Success Practices
Foundation Stage
Master Programming Fundamentals- (Semester 1-2)
Focus on building a strong foundation in C and Python programming. Regularly practice coding problems on platforms like HackerRank and LeetCode to develop problem-solving abilities and algorithmic thinking crucial for data science.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, C programming tutorials
Career Connection
Strong coding skills are fundamental for data science roles, enabling efficient data manipulation, algorithm implementation, and problem-solving during technical interviews.
Excel in Mathematics and Statistics- (Semester 1-4)
Pay close attention to Engineering Mathematics I, II, and Statistics for Data Science. Understand concepts like calculus, linear algebra, probability, and hypothesis testing thoroughly, as they are the backbone of machine learning algorithms.
Tools & Resources
Khan Academy, NPTEL courses on probability and linear algebra, Statistical software like R or Python''''s SciPy
Career Connection
A solid grasp of mathematical and statistical concepts is essential for understanding, developing, and interpreting data models, directly impacting success in analytical roles.
Engage in Peer Learning- (Semester 1-8)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and explain concepts to each other. This enhances understanding and develops communication skills vital for team-based data science projects.
Tools & Resources
Collaborative whiteboards (e.g., Miro), Shared code repositories (e.g., GitHub), Online discussion forums
Career Connection
Effective teamwork and communication are highly valued in industry, preparing students for collaborative development and cross-functional team interactions in their careers.
Intermediate Stage
Build a Data Science Portfolio- (Semester 3-5)
Start working on small data analysis and machine learning projects using publicly available datasets (e.g., Kaggle). Implement concepts learned in Data Structures, Python OOP, and Machine Learning courses to build practical solutions.
Tools & Resources
Kaggle, GitHub, Jupyter Notebooks, scikit-learn, pandas, numpy
Career Connection
A strong portfolio demonstrates practical skills and problem-solving abilities to potential employers, significantly enhancing internship and placement opportunities in Indian tech companies.
Seek Industry Exposure through Mini Projects and Internships- (Semester 5 (Internship I), Semester breaks)
Actively participate in Mini Projects and seek out summer internships. Even short-term internships or virtual internships provide invaluable real-world experience, exposure to industry tools, and networking opportunities within Indian companies.
Tools & Resources
LinkedIn, Internshala, College placement cell, Industry mentorship programs
Career Connection
Internships bridge the gap between academic learning and industry demands, often leading to pre-placement offers and providing a competitive edge in the job market.
Participate in Hackathons and Competitions- (Semester 4-6)
Join data science hackathons and coding competitions organized by colleges or external platforms. This fosters rapid problem-solving, teamwork, and exposure to diverse challenges, often under time pressure.
Tools & Resources
HackerEarth, Analytics Vidhya, Kaggle competitions, Local tech meetups
Career Connection
Success in competitions showcases talent, analytical prowess, and resilience, which are highly regarded by recruiters for roles in leading Indian analytics and IT firms.
Advanced Stage
Specialize and Engage in Advanced Projects- (Semester 7-8)
Choose professional electives aligned with specific interests (e.g., NLP, Computer Vision, Reinforcement Learning) and undertake a significant final year project. Focus on innovative solutions, research contributions, and deep technical implementation.
Tools & Resources
TensorFlow, PyTorch, Specialized libraries for chosen domain, Research papers (arXiv, Google Scholar)
Career Connection
Specialization makes graduates highly sought after for niche roles in AI/ML engineering, research, and advanced analytics, often commanding higher salaries in India''''s booming AI sector.
Network Professionally and Prepare for Placements- (Semester 6-8)
Attend industry seminars, workshops, and career fairs. Connect with professionals on LinkedIn and leverage alumni networks for insights and opportunities. Systematically prepare for technical interviews, aptitude tests, and group discussions focusing on data science concepts and coding.
Tools & Resources
LinkedIn, College alumni network, Glassdoor, Mock interview platforms, Resume building workshops
Career Connection
Strong networking can lead to referrals and hidden job opportunities, while rigorous preparation ensures readiness for placement drives at top Indian and global companies.
Develop Ethical and Responsible AI Practices- (Semester 7-8)
Beyond technical skills, cultivate an understanding of ethical considerations, bias, fairness, and transparency in AI and data science. Incorporate these principles into project work and discussions, reflecting responsible development.
Tools & Resources
AI Ethics guidelines, Relevant research papers, Discussions on industry forums
Career Connection
As AI becomes more pervasive, companies in India and globally prioritize ethical practitioners, making this a critical differentiator for leadership roles and socially impactful careers.
Program Structure and Curriculum
Eligibility:
- No eligibility criteria specified
Duration: 8 semesters / 4 years
Credits: 160 Credits
Assessment: Internal: 40%, External: 60%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MA11 | Engineering Mathematics - I | Core | 4 | Differential Calculus, Integral Calculus, Vector Calculus, Ordinary Differential Equations, Laplace Transforms |
| 18CH12 | Engineering Chemistry | Core | 4 | Electrochemistry, Corrosion, Engineering Materials, Water Technology, Chemical Fuels |
| 18CS13 | Programming for Problem Solving | Core | 3 | C language basics, Control statements, Functions, Arrays, Pointers and Structures |
| 18EL14 | Basic Electrical Engineering | Core | 3 | DC Circuits, AC Circuits, Transformers, Electrical Machines, Basic Electronic Components |
| 18ME15 | Elements of Mechanical Engineering | Core | 3 | Thermodynamics, IC Engines, Power Transmission, Manufacturing Processes, Refrigeration & Air Conditioning |
| 18CV16 | Elements of Civil Engineering | Core | 3 | Building Materials, Surveying, Structural Elements, Water Supply, Transportation Engineering |
| 18CSL17 | Computer Aided Engineering Drawing Laboratory | Lab | 2 | Orthographic Projections, Isometric Projections, Sectional Views, Assembly Drawings, CAD Software |
| 18CHL18 | Engineering Chemistry Laboratory | Lab | 1 | Volumetric analysis, Instrumental analysis, Water quality testing, Chemical synthesis, pH measurements |
| 18EGH19 | Technical English | Core | 1 | Basic Grammar, Vocabulary, Reading Comprehension, Technical Writing, Oral Communication |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18MA21 | Engineering Mathematics - II | Core | 4 | Linear Algebra, Multiple Integrals, Vector Calculus, Probability and Statistics, Complex Numbers |
| 18PH22 | Engineering Physics | Core | 4 | Quantum Mechanics, Solid State Physics, Lasers, Optical Fibers, Material Science |
| 18EC23 | Basic Electronics | Core | 3 | Diode applications, Transistors, Amplifiers, Oscillators, Digital Logic Gates |
| 18CS24 | Data Structures | Core | 3 | Arrays, Linked Lists, Stacks, Queues, Trees, Graphs |
| 18AE25 | Elements of Aerospace Engineering | Core | 3 | Aircraft structures, Aerodynamics, Propulsion, Flight Mechanics, Space Systems |
| 18ME26 | Manufacturing Process | Core | 3 | Casting, Forming, Welding, Machining, Additive Manufacturing |
| 18PHL27 | Engineering Physics Laboratory | Lab | 1 | Optical phenomena, Semiconductor characteristics, Magnetic fields, Thermal conductivity, Young''''s Modulus |
| 18CSL28 | Data Structures Laboratory | Lab | 2 | Array operations, Linked list implementation, Stack/Queue applications, Tree traversals, Graph algorithms |
| 18CIV29 | Environmental Studies | Core | 1 | Ecosystems, Biodiversity, Pollution, Renewable Energy Sources, Waste Management |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA31 | Engineering Mathematics - III | Core | 4 | Fourier Series, Fourier Transforms, Z-transforms, Partial Differential Equations, Statistical Methods |
| 18CSDA32 | Analog and Digital Electronics | Core | 4 | Diode Circuits, Transistor Biasing, Operational Amplifiers, Logic Gates, Combinational Logic |
| 18CSDA33 | Data Structures and Applications | Core | 4 | Advanced Trees, Graphs, Hashing, File Structures, Algorithm Analysis |
| 18CSDA34 | Computer Organization and Architecture | Core | 4 | Basic Computer Organization, CPU Design, Memory Hierarchy, I/O Organization, Pipelining |
| 18CSDA35 | Object Oriented Programming with Python | Core | 3 | Python Basics, Data Structures in Python, Functions, Classes and Objects, File Handling |
| 18CSDA36 | Python Programming Laboratory | Lab | 2 | Python Programs for Data Structures, OOP concepts, File I/O, Exception Handling, Module Usage |
| 18CSDA37 | Analog and Digital Electronics Laboratory | Lab | 2 | Diode characteristics, Transistor circuits, Logic gate verification, Combinational circuits, Sequential circuits |
| 18CSDA38 | Skill Development Course | Core | 1 | Soft Skills, Communication, Aptitude, Critical Thinking, Problem Solving |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA41 | Engineering Mathematics - IV | Core | 4 | Numerical Methods, Graph Theory, Probability Distributions, Sampling Theory, Stochastic Processes |
| 18CSDA42 | Design and Analysis of Algorithms | Core | 4 | Algorithm Design Techniques, Sorting, Searching, Graph Algorithms, NP-Completeness |
| 18CSDA43 | Operating Systems | Core | 4 | Process Management, Memory Management, File Systems, I/O Systems, Deadlocks |
| 18CSDA44 | Database Management Systems | Core | 4 | Database Concepts, ER Model, Relational Model, SQL, Normalization |
| 18CSDA45 | Statistics for Data Science | Core | 3 | Descriptive Statistics, Probability, Hypothesis Testing, Regression, ANOVA |
| 18CSDA46 | Database Management Systems Laboratory | Lab | 2 | SQL Queries, PL/SQL, Database Design, ER Diagrams, Relational Algebra |
| 18CSDA47 | Algorithms Laboratory | Lab | 2 | Implementation of Sorting, Searching, Graph Algorithms, Dynamic Programming, Greedy Algorithms |
| 18CSDA48 | Skill Development Course | Core | 1 | Professional Ethics, Teamwork, Time Management, Presentation Skills, Interview Preparation |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA51 | Data Warehousing and Mining | Core | 4 | Data Warehouse Architecture, OLAP, Data Preprocessing, Association Rules, Classification, Clustering |
| 18CSDA52 | Machine Learning | Core | 4 | Supervised Learning, Unsupervised Learning, Regression, Classification, Model Evaluation, Ensemble Methods |
| 18CSDA53 | Professional Elective - 1 | Elective | 3 | Advanced Python Programming OR, Web Programming OR, Computer Networks OR, Data Visualization |
| 18CSDA54 | Open Elective - 1 | Elective | 3 | |
| 18CSDA55 | Machine Learning Laboratory | Lab | 2 | Implementing Regression, Classification, Clustering, Neural Networks, Model Tuning |
| 18CSDA56 | Data Mining Laboratory | Lab | 2 | Data Preprocessing, Association Rule Mining, Classification Algorithms, Clustering Algorithms, Weka Tool |
| 18CSDA57 | Mini Project - I | Project | 2 | Project Planning, Literature Survey, Problem Definition, Design, Implementation |
| 18CSDA58 | Internship - I / Skill Development | Internship/Core | 2 | Industry Exposure, Report Writing, Presentation Skills, Project Management, Software Development Life Cycle |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA61 | Big Data Analytics | Core | 4 | Hadoop Ecosystem, MapReduce, HDFS, Spark, NoSQL Databases, Stream Processing |
| 18CSDA62 | Deep Learning | Core | 4 | Neural Networks, CNNs, RNNs, LSTMs, Autoencoders, Generative Models |
| 18CSDA63 | Professional Elective - 2 | Elective | 3 | Natural Language Processing OR, Cloud Computing OR, Optimization Techniques OR, Computer Vision |
| 18CSDA64 | Open Elective - 2 | Elective | 3 | |
| 18CSDA65 | Big Data Analytics Laboratory | Lab | 2 | Hadoop Installation, MapReduce Programming, HDFS Operations, Spark Applications, Hive Queries |
| 18CSDA66 | Deep Learning Laboratory | Lab | 2 | Implementing CNNs, RNNs, LSTMs, Image Classification, Text Generation, TensorFlow/Keras |
| 18CSDA67 | Mini Project - II | Project | 2 | Advanced Project Design, Data Collection, Model Building, Evaluation, Documentation |
| 18CSDA68 | Internship - II / Skill Development | Internship/Core | 2 | Industry Best Practices, Collaborative Projects, Technical Report Writing, Professional Networking, Problem Solving |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA71 | Applied Data Science | Core | 4 | Data Science Lifecycle, Case Studies, Industry Applications, Ethical AI, Data Governance |
| 18CSDA72 | Research Methodology and IPR | Core | 3 | Research Design, Data Collection Methods, Statistical Analysis, Report Writing, Intellectual Property Rights |
| 18CSDA73 | Professional Elective - 3 | Elective | 3 | Reinforcement Learning OR, Blockchain Technology OR, Internet of Things OR, Quantum Computing |
| 18CSDA74 | Professional Elective - 4 | Elective | 3 | Human Computer Interaction OR, Information Retrieval OR, Soft Computing OR, Digital Image Processing |
| 18CSDA75 | Project Work - Phase 1 | Project | 6 | Project Proposal, Literature Review, Problem Analysis, System Design, Methodology Selection |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 18CSDA81 | Professional Elective - 5 | Elective | 3 | Business Intelligence OR, Ethical Hacking for Data Security OR, Artificial Intelligence OR, Speech Processing |
| 18CSDA82 | Project Work - Phase 2 | Project | 6 | Implementation, Testing, Results Analysis, Thesis Writing, Project Defense |
