A1: Advanced Java
DSE-1A(1)
Course code: CS-511T
Chapters Covered:
- JDBC (Java Database Connectivity)
- Overview of Applet and Servlet API
- JSP (Java Server Pages)
- JSP Standard Tag Libraries (JSTL)
📝 Descriptive Questions:
Chapter 1: JDBC
- Explain the architecture of JDBC and describe the steps
involved in connecting a Java application to a database using JDBC.
- What are the different types of JDBC drivers? Compare
them in terms of performance and platform dependency.
- Describe the role of PreparedStatement in JDBC. How is it different from Statement
and why is it preferred in most cases?
Chapter 2: Overview of Applet and
Servlet API
- Differentiate between Applets and Servlets. In what
scenarios would you use one over the other?
- Describe the servlet life cycle and explain the purpose
of each method (init(), service(), and destroy()).
- What is the role of the HttpServletRequest and HttpServletResponse objects in servlets? Provide examples of their usage.
Chapter 3: JSP (JavaServer Pages)
- What is JSP? How does it differ from Servlets in terms
of syntax and functionality?
- Explain the life cycle of a JSP page. Include all
phases from translation to execution.
- Describe the different types of JSP scripting elements:
declarations, scriptlets, and expressions. Give examples of each.
Chapter 4: JSP Standard Tag
Libraries (JSTL)
B2: Data Mining
DSE-1A(2)
Course
code: CS-512T
Chapter 1: Introduction to Data
Mining
- What is data mining? Discuss its importance and how it
differs from traditional data analysis techniques.
- Explain the major steps involved in the data mining
process with an example.
- What are the various types of data mining tasks?
Differentiate between descriptive and predictive tasks.
Chapter 2: Data Preprocessing
- Why is data preprocessing important in data mining?
Explain the key steps involved in data preprocessing.
- Describe the different methods of handling missing
data. Provide scenarios where each method is appropriate.
- What are data normalization techniques? Explain any two
normalization methods with suitable examples.
Chapter 3: Concept Description
- Define concept description in data mining. How does it
help in understanding large datasets?
- Differentiate between data characterization and data
discrimination with examples.
Chapter 4: Classification and
Prediction
- What is the difference between classification and
prediction in data mining? Provide real-world examples of each.
- Explain the working of a decision tree algorithm. What
are the advantages and disadvantages of using decision trees for
classification?
A1: Software Engineering
DSE-2A(1)
Course
code: CS-521T
Chapter 1: Introduction to Software and Software Engineering
- Define software and explain the
characteristics that differentiate software from other engineering
products.
- What is software engineering?
Discuss its importance in the development of large-scale software systems.
- Describe the various categories
of software. Give examples for each category.
Chapter 2: Managing Software Projects
- What is software project
management? Explain the key responsibilities of a software project
manager.
- Discuss the major phases of a
software project life cycle. How does project planning contribute to
project success?
- Explain different types of
software project estimation techniques. What are the challenges in
accurate estimation?
Chapter 3: Software Coding and Testing
- What are the important
principles of good software coding? Explain the role of coding standards
in software development.
- Describe the different levels
of software testing. How do unit testing and integration testing differ?
- Explain the importance of test
case design. What are the characteristics of a good test case?
Chapter 4: Software Maintenance and Configuration Management
- What is software maintenance?
Explain the different types of maintenance activities with examples.
B1: Basic Data Science
DSE-2A (2)
Course code: CS-522T
Introduction
to Business Analytics, Big Data Analytics, Descriptive
Analytics, and Population and Sample. These questions
are designed for conceptual understanding, ideal for assignments, class
discussions, or exams.
📘 Introduction to Business
Analytics
Q.1 Define
Business Analytics. How is it used by organizations to improve decision-making?
Q. 2 Explain the
three types of analytics: Descriptive, Predictive, and Prescriptive. How do
they differ from each other?
📊 Big Data Analytics
Q 3 What is Big
Data? Discuss the characteristics of Big Data using the 5 V's.
Q 4 How is Big Data Analytics transforming
industries such as healthcare, retail, and finance?
Q 5 What are some of the major tools and
technologies used in Big Data Analytics?
📈 Descriptive Analytics
Q 6 What is
Descriptive Analytics? How does it help organizations understand past
performance?
Q 7 List and explain key
statistical techniques used in Descriptive Analytics.
Q 8 Differentiate between
univariate and multivariate descriptive analysis with examples.
📊 Population and Sample
Q 9 What is the difference between a population and
a sample in data science? Why is sampling important?
Q 10 Describe different sampling methods and discuss
the advantages and disadvantages of each.
A1: Artificial Intelligence
DSE-3A (1)
Course
code: CS-531T
Topics Covered:
- Introduction
to AI
- Problem Solving (State Space Search & Heuristic
Search)
- Knowledge Representation
- Symbolic Reasoning Under Uncertainty
🧠
1. Introduction to Artificial Intelligence
- What is Artificial Intelligence? Discuss its goals and
key applications in various fields.
- Explain the difference between Weak AI and Strong AI
with examples.
🔍
2. Problem Solving – State Space Search & Heuristic Search
- What is a state space in AI problem-solving? Explain
with an example.
- Describe the difference between uninformed (blind) and
informed (heuristic) search strategies.
- What is a heuristic function? How does it help in
search algorithms?
🧾
3. Knowledge Representation
- What is knowledge representation in AI? Why is it
important?
- Compare different types of knowledge representation
techniques.
- Explain the concept of ontologies in AI. How do they
aid in knowledge representation?
⚖️
4. Symbolic Reasoning Under Uncertainty
- What is symbolic reasoning? How is it used in AI for
decision-making?
- How is uncertainty handled in symbolic AI? Discuss any
two methods used.
C1: Power BI
DSE-3A (2)
Course
code: CS-532T
📊 Power BI – Descriptive Questions
1. Introduction to
Power BI
1. What is Power BI? Explain its importance in modern data
analytics and business intelligence.
2. Discuss the main components of the Power BI ecosystem and
their roles.
2. Power BI Components and Table Relationships
3. What are the key differences between Power BI Desktop and
Power BI Service?
4. Explain the concept of data modeling in Power BI. Why are
relationships between tables important?
5. Describe how to create and manage relationships between
tables in Power BI.
3. DAX (Data
Analysis Expressions) Functions
6. What is DAX in Power BI? Why is it essential for data
analysis?
7. Differentiate between calculated columns and measures in
Power BI with examples.
8. Explain the use of the following DAX functions: SUM(), CALCULATE(), and FILTER().
9. How does the RELATED() function work in Power BI? Provide a use case.
10. What are time intelligence functions in DAX? Explain with
an example.
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