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IS6146 Databases for Management Information Systems Lecture 12: Exam Revision Rob Gleasure robgleasure.com.

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Presentation on theme: "IS6146 Databases for Management Information Systems Lecture 12: Exam Revision Rob Gleasure robgleasure.com."— Presentation transcript:

1 IS6146 Databases for Management Information Systems Lecture 12: Exam Revision Rob Gleasure R.Gleasure@ucc.ie robgleasure.com

2 IS6146 Today’s session  Discussion of exam format and technique  Discussion of topics  Questions

3 Exam format Four questions, pick two (all worth equal marks) Time management is vital! Make sure you have a plan regarding how you will spend time. This will save you the stress of trying to think this out during the exam.  E.g. you have 45 minutes for this half of the exam – one possible plan for this could be: 5 minutes at the beginning to decide on which question Then  5 minutes to plan out answer, including key points to make  30 minutes to write answer  5 minutes to read over answer

4 Exam technique Do not go over time on one question and leave yourself short on another – two ‘good’ answers generally do better than one ‘excellent’ answer and one ‘ok’ answer Answer the question - marks are awarded for relevant points only*. Each sentence should have a purpose, either making or reinforcing a point. Note - this is not creative writing, just get your point across clearly. E.g. if something is difficult to describe, draw a picture. When arguing, take a position but don’t be over zealous. You are trying to be persuasive, so you must show you have considered different perspectives

5 Exam marking MarkCriteria 70% + Evidence of further reading and original arguments Clear relationships between statements Proficiency in all learning objectives in the area 60% - 69% Competent answer which addressed topic in question Apparent and easy to follow line of argument 50% - 59% Shows a knowledge and understanding of the area Argument made but not well supported or easy to follow Knowledge replicated but no sense of broad understanding 40% - 49% Shows some knowledge without really addressing question Awareness of issues in isolation 39% or less Fails to adequately address the topic or answer the question

6 Answering Questions Bring a pencil and eraser! Answer your best questions first Try and demonstrate both that you understand the phenomena and that your understanding has practical implications Have examples for everything you want to talk about. Seriously. Have examples for everything you want to talk about.

7 Exam topic 1: Structured data, DBMS, and SQL Why are structured SQL-based relational databases useful? What needs do they address that other technologies do not? What features can be used to differentiate DBMS? What are examples of DBMS that exemplify these features? How do DBMS handle large tables and what is the role of indices? Why would DBMS allow users to create new SQL procedures? How do they do this and what is an example of a plugin that allows new SQL procedures to be created?

8 Exam topic 2: Semi-structured data and XML What is semi-structured data, how is it different from structured and unstructured data, and why is it important for eBusiness? What are the opportunities associated with user-generated semi- structured data and folksonomies? Why is XML used and what is the difference between a well-formed and a valid document? What is the relationship between XML, XML Schema, and XPath? What is an example of a data store that uses XML?

9 Exam topic 3: Unstructured data, machine learning, and NoSQL What is unstructured data, what are the sources of unstructured data, and why is it important? What is regression, classification, and clustering and what types of problems do they solve? What are the challenges of text mining (e.g. social media, blogs)  Differences in context (identity, small worlds, tone)  Managing computational complexity (feature vectors, scale) Explain the mapreduce concept and how it is applied in the Hadoop framework?

10 Exam topic 4: Uses of data The difference between predictive, diagnostic, predictive, and prescriptive analytics How can users’ data be used to improve a product or service? What are the privacy implications of this? What examples of eBusinesses exemplify this? How do they compare in their use of data and why do they differ? How could they expand this use and what could they learn from one another?

11 Questions? This is your chance to ask!  Do you feel confident about the structure of the exam?  Do you feel confident you know what to revise?  Do you feel confident you know what I’m expecting in an answer?


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