BDA112 - Data Science Fundamentals

Credit Points: 15 credit points

Workload: 48 hours

Prerequisite: N/A

Co-requisite: N/A

Aims & Objectives

This is a core unit out of a total of 24 units in the Bachelor of Data Analytics (BDA). This unit addresses the BDA course learning outcomes and complements other courses in related fields by developing students’ specialised knowledge of data analytics in Information Technology. For further course information, refer to: http://www.mit.edu.au/study-with-us/programs/Bachelor-DA. This unit is part of the AQF level 7 (BDA) course.

Students will learn the fundamentals of intelligent computing and smart applications with industry case examples. Students will be exposed to the significance of artificial intelligence and data analytics in current and future industries. Students will become familiar with the terms and references from smart applications domains. Students will be introduced to the basic concepts of mathematics and statistics required for their further studies in data analytics.

This unit will cover the following topics:

  • Data analytics for business improvements
  • Data analytics infrastructure including hardware, software, and data systems
  • Data analytics applications life cycles 
  • Multivariate random variables and probability distributions
  • Introduction to linear regression models and interpret model parameters
  • Combinational graphs, matrices and vectors

Learning Outcomes

4.1 Course Learning Outcomes
The Course learning outcomes applicable to this unit are listed on the Melbourne Institute of Technology’s website: www.mit.edu.au 
4.2 Unit Learning Outcomes
At the completion of this unit students should be able to:
a. Understand the fundamental concepts of data science;
b. Describe necessary tools and technologies in data analytics including mathematical and statistical modelling;
c. Describe data science life cycle, ethics and methodologies in industry applications;
d. Evaluate suitable tools and technologies in data science applications;
e. Collaboratively work on data analytics project and communicate results and milestones of data analytics applications in a professional manner.
 

Weekly Topics

Week Topics
1 Introduction to data science
2 Data analytic motivations and business automations
3 Data analytic infrastructure
4 Data science life cycle
5 Data science and data ethics in enterprise applications
6 Data analytic tools and platforms
7 Introduction to discrete, continuous and multivariate random variables
8 Probability distributions and statistical analysis
9 Permutations and combinations (Combinatory and probably in data analysis)
10 Vectors, matrices their role in data mining and pattern recognition
11 Linear regression and Interpolations
12 Future trends in data science and smart technologies

Assessment

Assessment Task Due Date A B Learning Outcomes Assessed
Formative Assignment 1 Part A
Assignment 1 Part B		 Week 3
Week 8		 5%
10%		   a-c
Assignment 2 Week 11 25%   d-e
Laboratory participation & submission Week 2 - 11 10%   a-e
Final Examination (2 hours) End of trimester   50% a-e
TOTALS   50% 50%  

Task Type: Type A: unsupervised, Type B: supervised.

Class Participation and Contribution
This unit has class participation and student contribution as an assessment. The assessment task and marking rubric will follow the Guidelines on Assessing Class Participation (https://www.mit.edu.au/about-us/governance/institute-rules-policies-and-plans/policies-procedures-and-guidelines/Guidelines_on_Assessing_Class_Participation). Further details will be provided in the assessment specification on the type of assessment tasks and the marking rubrics.

Presentations (if applicable)
For presentations conducted in class, students are required to wear business attire.

Textbook and Reference Materials

Textbook:

  • João Moreira, Andre Carvalho, Tomás Horvath, A General Introduction to Data Analytics, Wiley Press, 2018

References:

  • Maheshwari, Data Analytics Made Accessible, Packt Publishing, 2018

Adopted Reference Style: IEEE

Graduate Attributes

MIT is committed to ensure the course is current, practical and relevant so that graduates are “work ready” and equipped for life-long learning. In order to accomplish this, the MIT Graduate Attributes identify the required knowledge, skills and attributes that prepare students for the industry.
The level to which Graduate Attributes covered in this unit are as follows:

Ability to communicate Independent and Lifelong Learning Ethics Analytical and Problem Solving Cultural and Global Awareness Team work Specialist knowledge of a field of study

Legend

Levels of attainment Extent covered
The attribute is covered by theory and practice, and addressed by assessed activities in which the students always play an active role, e.g. workshops, lab submissions, assignments, demonstrations, tests, examinations.
The attribute is covered by theory or practice, and addressed by assessed activities in which the students mostly play an active role, e.g. discussions, reading, intepreting documents, tests, examinations.
The attribute is discussed in theory or practice; it is addressed by assessed activities in which the students may play an active role, e.g. lectures and discussions, reading, interpretation, workshops, presentations.
The attribute is presented as a side issue in theory or practice; it is not specifically assessed, but it is addressed by activities such as lectures or tutorials.
The attribute is not considered, there is no theory or practice or activities associated with this attribute.