JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

22BBA505: DATA SCIENCE

BBA (Data Analytics) V Semester (R22)

Course Objectives:

To expose the students to the Data Science Environment
To understand the data science process
To introduce to the concepts of data mining
To introduce to the concepts of machine learning
To introduce to the concepts of artificial intelligence Course outcomes: Students should be able to
Understand the scope of data science and its applications in the business.
Get acquainted with data science processes and to apply in business.
Familiarize with concepts of data mining and its applicants.
Familiarize with concepts of machine learning and its applicants.
Familiarize with concepts of artificial intelligence and its applicants.

UNIT - I: Introduction to Data Science

Defining Data Science and Big Data, Benefits and Uses of Data Science and Big Data, Facets of Data, Structured Data, Unstructured Data, Natural Language, Machine-generated Data, Graph-based or Network Data, Audio, Image, Video, Streaming data, Data Science Process, Big data ecosystem and data science.

UNIT - II: Data Science Processes

Six steps of data science processes, define research goals, data retrieval, cleansing data, correct errors as early as possible, integrating – combining data from different sources, transforming data, exploratory data analysis, Data modelling, model and variable selection, model execution, model diagnostic and model comparison, presentation and automation.

UNIT - III: Data Mining

Scope of Data Mining, Data Exploration and Reduction, Unsupervised learning – cluster analysis, Association rules, Supervised learning- Partition Data, Classification Accuracy, prediction Accuracy, k-nearest neighbors, Classification and regression trees, Logistics Regression.

UNIT - IV: Introduction to Machine Learning concepts

Machine Learning, Learning from Data, History of Machine Learning, Big Data for Machine Learning, Leveraging Machine Learning, Descriptive vs Predictive Analytics, Machine Learning and Statistics, Types of Machine Learning, Reinforcement Learning.

UNIT - V: Introduction to AI concepts

AI, Turing test, cognitive modeling approach, the law of thoughts, the relational agent approach, the underlying assumptions about intelligence, techniques required to solve AI problems, level of details required to model human intelligence, successfully building an intelligent problem, history of AI.