B. Tech. ‑ Data Science (Lateral)

programming system, structure, numerical method problems, function, pointers, array structures and files handing process,compiler, interpreter, shell, problem solving using C and/or R and/or python

OOPS, object oriented programming language and apply programming conept to create class & its objects for solving inheritance,polymorphism,applets, swing programming etc.

computational efficiency of the principal algorithms – sorting, searching, hashing, Identify appropriate data structure & algorithmic methods

Analyze the asymptotic performance of algorithms, Synthesize efficient algorithms in common engineering design situations

Analyze database requirements, determine relationships of entities, logical design of the database, DBMS/RDBMS and big data modeling and management.

statistical analysis of data, build and assess data-based models, execute statistical analyses with professional statistical software

matrix analysis, numerical methods of linear algebra, elements of functional analysis, mathematical statistics, modern machine learning and data analysis.

For presenting data to others, aspireing to be a business analyst or data scientist then the most effective with visualization tools used is tableu

For presenting data from the point of view of a business analyst

Learners are expected to apply principles of statistical analytics to solve problems and inform decision making.

To design and build a simple database system and demonstrate competence with the fundamental tasks involved with modeling, designing, and implementing a DBMS.

To analyze data, choose relevant models and algorithms for espective applications and evaluate different data mining techniques like classification, prediction, clustering and association rule mining

“Design data warehouse with dimensional modelling and apply
OLAP operations – Relational, Multidimentional, Hybrid”

acquire statistical techniques to analyze complex information and social networks

introduction to artificial intelligence, machine learning, current trends and characterization of knowledge-based systems, Search, knowledge representation schemes, production systems, and expert systems

Fundamentals of Soft Computing, Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms, optimization associated with neural network learning

Fundamentals of deep learning, computer learning model, perform classification tasks directly from images, text, or sound, Model training, testing, large set of labeled data, and multiple layers in neural network architectures

Natural Language Processing (NLP) develops statistical techniques and algorithms to automatically process natural languages (such as English). It includes a number of AI areas, such as text understanding and summarization, machine translation, and sentiment analysis.

HCI design principles, standards and guidelines, analyze and identify user models, user support, socio-organizational issues, stakeholder requirements of HCI systems.

computer organization & parallel architecture , interconnected networks, distributed system architecture, Cloud architecture and computing

mechanisms of OS, Network OS, Distributed OS, handle processes and threads, memory magagement, process management, exception handling

individual components of the Bitcoin protocol, whole system work: transactions, script, blocks, and the peer-to-peer network