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
HUM 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
HUM 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
CSE Data Structures 3 1 0 4
MAT Optimization Techniques 3 1 0 4
MAT Probability Theory 3 1 0 4
MAT Numerical Methods 3 1 0 4
CSE Foundations Of Data science 2 0 2 3
HUM Environmental Science and Sustainability 3 0 0 3
MAT Data science Lab 1: Statistics and
Numerical Methods Lab
0 0 2 1
HUM Amrita Values Programme I 1 0 0 1
CSE Data Structures Lab 0 0 2 1
       
  Total   25
Semester 4
Course Code Course Title L T P Cr
MAT Statistical Inference Theory 3 0 0 3
MAT Introdcution to Modern Algebra 3 0 0 3
CEN Convex Optimization 3 0 0 3
CSE Design and Analysis of Algorithms 3 1 0 4
CSE Database Management Systems 3 1 0 4
HUM Open Elective A* 3 0 0 3
MAT Data Science Lab-II: Inference Theory 0 0 2 1
  Life Skills I 1 0 2 2
CSE Design and Analysis of Algorithms Lab 0 0 2 1
HUM Amrita Values Programme II 1 0 0 1
  Total   25
Semester 5
Course Code Course Title L T P Cr
CSE Operating Systems 3 0 0 3
MAT Transfrom Technique 3 1 0 4
CYB Number Theory and Information
Security
3 1 0 4
MAT Random Process 3 0 0 3
CSE Database Design 3 1 0 4
HUM Live-in-Lab.@ / Open Elective B* 3 0 0 3
  Life Skills II 1 0 2 2
CSE DBMS Lab 0 0 2 1
MAT/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
MAT Graph Analytics and Algorithms 3 0 2 4
MAT Regression Analysis 3 1 0 4
CEN Machine Learning 3 1 0 4
CSE Data Visualization 3 0 2 4
CSE Theory of Computation 3 1 0 4
CEN Machine Learning-Lab 0 0 2 1
  Life Skills III 1 0 2 2
MAT/CSE Ethics for Data Scientist 1 0 0 1
CSE Open Lab II (Algorithms.io /Hadoop /
Cascading / web design)
0 0 2 1
       
  Total   25
Semester 7
Course Code Course Title L T P Cr
CSE Parallel and Distributed Systems 3 1 0 4
CEN Deep Learning 3 0 2 4
CSE Practical Techniques for Big Data Processing 3 0 2 4
CSE Reinforcement Learning 3 0 0 3
CYB Data Security 3 0 0 3
MAT/CSE Elective I 3 0 0 3
MAT/CSE Elective II 3 0 0 3
MAT/CSE Open Lab III 0 0 2 1
  Total   25
Semester 8
Course Code Course Title L T P Cr
CSE Software Engineering 3 1 0 4
CEN Deep Learning for Natural Language Processing 3 0 2 4
MAT/CSE Elective III 3 0 0 3
MAT/CSE Elective IV 3 0 0 3
MAT/CSE Elective V 3 0 0 3
MAT/CSE Mini Project   5
       
  Total   23
Semester 9
Course Code Course Title L T P Cr
  Project–I (Internship in Industries / Universities)   8
MAT/CSE Elective VI 3 0 0 3
MAT/CSE Elective VII 3 0 0 3
       
       
  Total   14
Semester 10
Course Code Course Title L T P Cr
  Project–II (Research based) Dissertation   12
       
       
       
       
  Total   12
Total Credits of all Semesters 219
Mathematics Stream
MAT Advanced Algebra 3 0 0 3
MAT Advanced Big Data Analytics 3 0 0 3
MAT Differential Equations 3 0 0 3
MAT Multivariate Statistics and Time Series Analysis 3 0 0 3
MAT Wavelets 3 0 0 3
MAT Computational Geometry 3 0 0 3
MAT Queuing Theory and Inventory Control Theory 3 0 0 3
MAT Theory of Sampling and Design of Experiments for
Data Analysis
3 0 0 3
MAT Computational Financial Mathematics 3 0 0 3
MAT Data Analytics in Computational Biology 3 0 0 3
MAT Computer Aided Drug Designing 3 0 0 3
MAT Statistical Quality Control 3 0 0 3
MAT Six Sigma Analysis 3 0 0 3
MAT Statistical Patter Classifications 3 0 0 3
Computer Science Stream
CEN Soft Computing 3 0 0 3
CYB Cryptography 3 0 0 3
CSE Business Analytics 3 0 0 3
CSE Deep Learning for Image Processing 3 0 0 3
CSE Predictive Analytics 3 0 0 3
CSE Mining of Massive Datasets 3 0 0 3
CSE Data Compression 3 0 0 3
CSE Computer Networks 3 0 0 3
CSE IoT (workshop based course) 3 0 0 3
CSE Introduction to Embedded Systems 3 0 0 3
CSE Information retrieval (NL) 3 0 0 3
CSE Social Network Analytics 3 0 0 3
CSE Big Data Storage and Anaysis 3 0 0 3
CSE Probabilistic Graphical Models 3 0 0 3
You can join this program at
Degree: 
Integrated Degree
School: 
School of Engineering
Campuses: 
Coimbatore