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CECS Professional Skills Mapping

COMP2420 — Introduction to Data Management, Analysis and Security

code: COMP2420
name: Introduction to Data Management, Analysis and Security
unit value: 6
description: Commerce and research are being transformed by data-driven discovery and prediction. Skills required for data analytics at massive levels – scalable data management on and off the cloud, parallel algorithms, statistical modeling, and proficiency with a complex ecosystem of tools and platforms – span a variety of disciplines and are not easy to obtain through conventional curricula. Tour the basic techniques of data science, including both SQL and NoSQL solutions for massive data management, basic statistical modeling (e.g., descriptive statistics, linear and non-linear regression), algorithms for machine learning and optimization, and fundamentals of knowledge representation and search. Learn key concepts in security and the use of cryptographic techniques in securing data.
P&C: https://programsandcourses.anu.edu.au/course/COMP2420
course learning outcomes:
  1. Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
  2. Define, query and manipulate a relational database
  3. Demonstrate basic knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation
  4. Formulate and extract descriptive and predictive statistics from data
  5. Analyse and interpret results from descriptive and predictive data analysis
  6. Apply their knowledge to a given problem domain and articulate potential data analysis problems. Identify potential pitfalls, and social and ethical implications of data science
  7. Explain key security concepts and the use of cryptographic techniques, digital signatures and PKI in security
assessment:
  1. Assignment 1 (10%)
  2. Assignment 2 (20%)
  3. Labtest (2%)
  4. Mid-semester exam (18%)
  5. Final exam (50%)

Mapped learning outcomes

learning outcome1. KNOWLEDGE AND SKILL BASE2. ENGINEERING APPLICATION ABILITY3. PROFESSIONAL AND PERSONAL ATTRIBUTESassessment tasks
1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.612345
  1. Demonstrate a conceptual understanding of database systems and architecture, data models and declarative query languages
✓✓✓✓
  1. Define, query and manipulate a relational database
✓✓✓✓✓
  1. Demonstrate basic knowledge and understanding of descriptive and predictive data analysis methods, optimization and search, and knowledge representation
✓✓✓✓✓✓✓✓
  1. Formulate and extract descriptive and predictive statistics from data
✓✓✓✓✓✓✓✓
  1. Analyse and interpret results from descriptive and predictive data analysis
✓✓✓✓✓✓✓✓
  1. Apply their knowledge to a given problem domain and articulate potential data analysis problems. Identify potential pitfalls, and social and ethical implications of data science
✓✓✓✓✓✓✓✓✓✓✓
  1. Explain key security concepts and the use of cryptographic techniques, digital signatures and PKI in security
✓✓✓✓✓

Course contribution towards the Engineers Australia Stage 1 Competency Standard

This table depicts the relative contribution of this course towards the Engineers Australia Stage 1 Competency Standard. Note that this illustration is indicative only, and may not take into account any recent changes to the course. You are advised to review the official course page on P&C for current information..

1. KNOWLEDGE AND SKILL BASE
1.1
 
1.2
 
1.3
 
1.4
1.5
1.6
2. ENGINEERING APPLICATION ABILITY
2.1
 
2.2
 
2.3
2.4
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1
 
3.2
3.3
3.4
 
3.5
3.6

Engineers Australia Stage 1 Competency Standard — summary

1. KNOWLEDGE AND SKILL BASE
✓1.1Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
✓1.2Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
✓1.3In depth understanding of specialist bodies of knowledge within the engineering discipline.
1.4Discernment of knowledge development and research directions within the engineering discipline.
1.5Knowledge of contextual factors impacting the engineering discipline.
1.6Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the engineering discipline.
2. ENGINEERING APPLICATION ABILITY
✓2.1Application of established engineering methods to complex engineering problem solving.
✓2.2Fluent application of engineering techniques, tools and resources.
2.3Application of systematic engineering synthesis and design processes.
2.4Application of systematic approaches to the conduct and management of engineering projects.
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
✓3.1Ethical conduct and professional accountability.
3.2Effective oral and written communication in professional and lay domains.
3.3Creative, innovative and pro-active demeanour.
✓3.4Professional use and management of information.
3.5Orderly management of self, and professional conduct.
3.6Effective team membership and team leadership.

Updated:  18 February 2021/ Responsible Officer:  Dean, CECS/ Page Contact:  CECS Academic Education Services