Admissions 2019

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About the Program

The Five-year Integrated M.Sc. Program on Data Science is intended for the students who aspire to excel in data science. It provides a spectrum of basics to advanced mathematical methods and their applications to both conventional and IT industries that enable to tackle emerging problems. The curriculum covers the necessary basics in Mathematics, Statistics, Computer programming and Business analytics and it also includes the emerging topics such as Big Data Analytics, Neural Networks, Machine Learning, Artificial Intelligence, Data Visualization, and Data Modeling to deal with vast data from business, healthcare, sensors, Internet, surveys, social media, astronomy, human space exploration and operations, and aeronautics.

In addition to the regular curriculum, students are encouraged to take up summer/winter In-plant training/Internship. This will help students to gain real time processing of data by doing Prediction analysis, Pattern Recognition, Anomaly Detection, Clustering, Actionable Insights, Automated Processes and decision making, Segmentation, Optimization and Forecasting. A full length project is included in the curriculum to be completed in the final semester. Students are expected to take up such projects in industries. This helps students to gain knowledge on working conditions and industrial requirements when they are employed.

Harvard was right about data scientists. It’s an extremely important and high-demand role that can have significant impact on a business’ ability to achieve its goals, whether they are financial, operational, strategic, and so on.

  • Salient Features of the Program
    • Gain knowledge in Mathematics, Computer Programming and Business Analytics.
    • Placements in both conventional and software Industries.
    • Scope for doing research for those who aim to be a teacher, scientist or research associate in highly reputed national and international institutions.
    • One of the most wanted program in academics and industry.

Curriculum and syllabi

Data Science curriculum is carefully designed to cater the industrial needs and it includes Mathematics, Computer Science and Business Analytics. The syllabus starts with the basics in Calculus, Mathematical Logic, Probability, Statistics, Linear Algebra, Data Management and Analytics to advanced topics like Neural Networks, Artificial Intelligence, Machine Learning, Data Modeling and Big Data.

Program Outcomes

  • Knowledge in Mathematics and Computer Science: Understand the basic concepts, fundamental principles and the scientific theories related to Data Science.
  • Abstract thinking: Ability to absorb and understand the abstract concepts that lead to various advanced theories in Mathematics, Statistics and Computer science.
  • Modelling and solving: Ability in modelling and solving problems by identifying and employing the appropriate existing theories and methods.
  • Advanced theories and methods: Understand advanced theories and methods to design solutions for complex data science problems.
  • Applications in Engineering and Sciences: Understand the role of mathematical sciences and apply the same to solve the real life problems in fields of data science.
  • Modern software tool usage: Acquire the skills in handling scientific tools towards problem solving and solution analysis.
  • Environment and sustainability: Understand the significance of preserving the environment towards sustainable development.
  • Ethics: Imbibe ethical, moral and social values in personal and social life leading to highly cultured and civilized personality. Continue to enhance the knowledge and skills in mathematics and computer science for constructive activities, and demonstrate highest standards of professional ethics.
  • Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
  • Communication: Develop various communication skills such as reading, listening, and speaking which will help in expressing ideas and views clearly and effectively.
  • Project management and Research: Demonstrate knowledge, understand the scientific and management principles and apply these to one’s own work, as a member/ leader in a team to manage projects and multidisciplinary research environments. Also use the research-based knowledge to analyse and solve advanced problems in data science.
  • Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.

Job opportunities:

Employment opportunities for students with Data Science degree are plenty in the job market. The need for Data Scientists is growing every day in all fields. Harvard calls the Data Scientist job as the Sexiest job of 21st Century. They are also eligible to work as Scientists and Research Associates in highly reputed organizations such as ISRO, DRDO and CSIR. In addition, lucrative jobs are available in software industries. The prospective recruiters for the students of Integrated Data Science are:

Search Engine Companies

Social Network Companies

Engineering Related Companies

Financial Related Companies

Data Science Vendors

The other few companies offering data scientist jobs are

* More than 8000 companies hiring data scientist.

Curriculum

Semester 1
Course Code Course Title L T P Cr
18ENG101 Communicative English 2 0 2 3
  Language Paper I 1 0 2 2
18MAT101 Calculus 3 1 0 4
18MAT104 Linear Algebra 3 1 0 4
18PHY101 Physics 3 0 0 3
18CSE100 Problem Solving and Computer Programming 3 0 0 3
18CSE180 Problem solving computer Programming Lab 0 0 2 1
18PHY181 Physics Lab 0 0 2 1
18CUL101 Cultural Education I 2 0 0 2
  Total   23
Semester 2
Course Code Course Title L T P Cr
18ENG121 Professional Communication 1 0 2 2
  Language Paper II 1 0 2 2
18MAT115 Vector Calculus 3 1 0 4
18MAT112 Discrete Mathematics 3 0 2 4
18CSE116 Advanced Computer Programming 3 0 0 3
18CSE117 Digital Electronics 3 0 0 3
18CSE181 Advanced Computer Programming Lab 0 0 2 1
18CSE185 Digital Electronics Lab 0 0 2 1
18CUL111 Cultural Education II 2 0 0 2
  Total   22
Semester 3
Course Code Course Title L T P Cr
18CSC201 Data Structures 3 1 0 4
18MAT231 Optimization Techniques 3 1 0 4
18MAT232 Probability Theory 3 1 0 4
18MAT 233 Numerical Methods 3 1 0 4
18CSC202 Foundations Of Data science 2 0 2 3
18ENV300 Environmental Science and Sustainability 3 0 0 3
18MAT288 Data science Lab 1: Statistics and
Numerical Methods Lab
0 0 2 1
18AVP201 Amrita Values Programme I 1 0 0 1
18CSC281 Data Structures Lab 0 0 2 1
18SSK201 Life Skills I 1 0 2 2
  Total   27
Semester 4
Course Code Course Title L T P Cr
18MAT241 Statistical Inference Theory 3 0 0 3
18MAT242 Introduction to Modern Algebra 3 0 0 3
18CSC211 Convex Optimization 3 0 0 3
18CSC212 Design and Analysis of Algorithms 3 1 0 4
18CSC213 Database Management Systems 3 1 0 4
  Open Elective A* 3 0 0 3
18MAT289 Data Science Lab-II: Inference Theory 0 0 2 1
18SSK211 Life Skills II 1 0 2 2
18CSC282 Design and Analysis of Algorithms Lab 0 0 2 1
18AVP211 Amrita Values Programme II
 
1 0 0 1
  Total   25
Semester 5
Course Code Course Title L T P Cr
18CSC301 Operating Systems 3 0 0 3
18MAT331 Transfrom Techniques 3 1 0 4
18CSC302 Number Theory and Information
Security
3 1 0 4
18MAT332 Random Process 3 0 0 3
18CSC303 Database Design 3 1 0 4
18MAT390 Live-in-Lab.@ / Open Elective B* 3 0 0 3
18SSK301 Life Skills III 1 0 2 2
18CSC381 DBMS Lab 0 0 2 1
18CSC382/CSE Open Lab-I (JAVA/C/C++/R programming/
Azure...)
0 0 2 1
  Total   25
Semester 6
Course Code Course Title L T P Cr
18MAT333 Graph Analytics and Algorithms 3 0 2 4
18MAT334 Regression Analysis 3 1 0 4
18CSC311 Machine Learning 3 1 0 4
18CSC312 Data Visualization 3 0 2 4
18CSC313 Theory of Computation 3 1 0 4
18CSC383 Machine Learning-Lab 0 0 2 1
18CSC314 Ethics for Data Scientist 1 0 0 1
18CSC384 Open Lab II (Algorithms.io /Hadoop /
Cascading / web design)
 
0 0 2 1
       
  Total   23
Semester 7
Course Code Course Title L T P Cr
18CSC401 Parallel and Distributed Systems 3 1 0 4
18CSC402 Deep Learning 3 0 2 4
18CSC403 Practical Techniques for Big Data Processing 3 0 2 4
18CSC404 Reinforcement Learning 3 0 0 3
18CSC405 Data Security 3 0 0 3
  Elective I 3 0 0 3
  Elective II 3 0 0 3
18CSC481 Open Lab III 0 0 2 1
  Total   25
Semester 8
Course Code Course Title L T P Cr
18CSC411 Software Engineering 3 1 0 4
18CSC412 Deep Learning for Natural Language Processing 3 0 2 4
  Elective III 3 0 0 3
  Elective IV 3 0 0 3
  Elective V 3 0 0 3
18CSC491 Mini Project   5
       
  Total   22
Semester 9
Course Code Course Title L T P Cr
18CSC591 Project–I (Internship in Industries / Universities)   8
  Elective VI 3 0 0 3
  Elective VII 3 0 0 3
  Total   14
Semester 10
Course Code Course Title L T P Cr
18CSC599 Project–II (Research based) Dissertation   12
       
       
  Total   12
Total Credits 218
Mathematics Stream
18MAT441 Advanced Algebra 3 0 0 3
18MAT442 Advanced Big Data Analytics 3 0 0 3
18MAT443 Differential Equations 3 0 0 3
18MAT444 Multivariate Statistics and Time Series Analysis 3 0 0 3
18MAT445 Wavelets 3 0 0 3
18MAT446 Computational Geometry 3 0 0 3
18MAT447 Queuing Theory and Inventory Control Theory 3 0 0 3
18MAT448 Theory of Sampling and Design of Experiments for
Data Analysis
3 0 0 3
18MAT449 Computational Financial Mathematics 3 0 0 3
18MAT450 Data Analytics in Computational Biology 3 0 0 3
18MAT451 Computer Aided Drug Designing 3 0 0 3
18MAT452 Statistical Quality Control 3 0 0 3
18MAT453 Six Sigma Analysis 3 0 0 3
18MAT454 Statistical Pattern Recognition 3 0 0 3
Computer Science Stream
18CSC441 Soft Computing 3 0 0 3
18CSC442 Cryptography 3 0 0 3
18CSC443 Business Analytics 3 0 0 3
18CSC444 Deep Learning for Image Processing 3 0 0 3
18CSC445 Predictive Analytics 3 0 0 3
18CSC446 Mining of Massive Datasets 3 0 0 3
18CSC447 Data Compression 3 0 0 3
18CSC448 Computer Networks 3 0 0 3
18CSC449 IoT (workshop based course) 3 0 0 3
18CSC450 Introduction to Embedded Systems 3 0 0 3
18CSC451 Information retrieval (NL) 3 0 0 3
18CSC452 Social Network Analytics 3 0 0 3
18CSC453 Big Data Storage and Analysis 3 0 0 3
18CSC454 Probabilistic Graphical Models
 
3 0 0 3

Languages

  Paper I      
18HIN101 Hindi I 1 0 2 2 B
18KAN101 Kannada I 1 0 2 2 B
18MAL101 Malayalam I 1 0 2 2 B
18SAN101 Sanskrit I 1 0 2 2 B
  Paper I      
18HIN111 Hindi II 1 0 2 2 B
18KAN111 Kannada II 1 0 2 2 B
18MAL111 Malayalam II 1 0 2 2 B
18SAN111 Sanskrit II 1 0 2 2 B
You can join this program at
Degree: 
Integrated Degree
School: 
School of Engineering
Campuses: 
Coimbatore