Course Outcomes:
On completion of the module the student should be able to:
CO1. Understand a variety of techniques for designing algorithms.
Credits – 4
CO2. Understand a wide variety of data structures and should be able to use them appropriately to
solve problems
CO3. Understand some fundamental algorithms.
Course Outcomes:
Upon successful completion of the course, students will be able to
Credits – 3
CO1. Distinguish the major data mining problems as different types of computational tasks
(prediction, classification, clustering, etc.) and the algorithms appropriate for addressing these
tasks
CO2. Analyze data through statistical and graphical summarization, supervised and unsupervised
learning algorithms
CO3. Evaluate data mining algorithms and understand how to choose algorithms for different
analysis tasks
CO4. Analyse the methods and results from a data mining practice
CO5. Design and implement data mining applications using real-world datasets, and evaluate and
select proper data mining algorithms to apply to practical scenarios
Course Objectives
Text analytics concepts and applications
Fundamental of Information retrieval and natural language processing
Text analytics framework
Theoretical techniques and applications in text analytics (e.g. social media)
After completing this course, the student will
· understand basic knowledge in Python programming.
· learn how to design and program Python applications.
· acquire object-oriented skills in Python.
· able to work with python standard library.
Project Minor - Phase I
Review of Literature
Problem Statement
Objectives
Methodology & Datasets
Course Objectives
· To familiarize basic concepts of OO programming.
· To understand the concept of constructors, packages and multithreading.
· To inculcate concepts of GUI programming using swing.
· To be able to create applets and implement database connectivity.
On completion of course, students should be able:
· To learn about some Python functionality and techniques that are commonly used.
· To understand and use functionality of various Python libraries for different scientific and mathematical tasks.
· To gain basic insight of implementation of advanced concepts and use of various libraries for applying Machine Learning for problem solving.
· To acquire knowledge about the frameworks in Python.
· To analyze large data sets in Python from data science.
Learning operating systems (OS) is all about unlocking the secrets of how computers work gaining control over your digital experience. Your OS knowledge will be a stepping stone for exploring programming languages, cybersecurity and cloud computing.
After completing the course, you will be able to explain
- The fundamental concepts regarding an OS
- Concept of a process and management of processes
- Inter process synchronization methods and deadlock handling
- Various memory management techniques
- Concept of file and various file handling methods