[an error occurred while processing this directive] [an error occurred while processing this directive]
[an error occurred while processing this directive]
[an error occurred while processing this directive]

FIT5145 Introduction to data science - Monash Online Teaching Period 5, 2015

This unit looks at processes and case studies to understand the many facets of working with data, and the significant effort in Data Science over and above the core task of Data Analysis. Working with data as part of a business model and the lifecycle in an organisation is considered, as well as business processes and case studies. Data and its handling is also introduced: characteristic kinds of data and its collection, data storage and basic kinds of data preparation, data cleaning and data stream processing. Curation and management are reviewed: archival and architectural practice, policy, legal and ethical issues. Styles of data analysis and outcomes of successful data exploration and analysis are reviewed. Standards, tools and resources are also reviewed.

Mode of Delivery

Monash Online (Online)

Workload Requirements

Minimum total expected workload equals 144 hours per semester comprising:

  1. Contact hours for on-campus students:
    • Two hours/week lectures
    • Two hours/week laboratories
  1. Contact hours for Monash Online students:
    • Two hours/week online group sessions

Online students generally do not attend lecture, tutorial and laboratory sessions, however should plan to spend equivalent time working through resources and participating in discussions.

  1. Additional requirements (all students):
    • A minimum of 8 hours per week of personal study (22 hours per week for Monash online students) for completing lab/tutorial activities, assignments, private study and revision, and for online students, participating in discussions.

See also Unit timetable information

Additional workload requirements

Expected total workload 24 hours per week.  This includes a 2 hours per week online group session with the lecturer.

Unit Relationships

Prerequisites

(FIT5131 or FIT9131) and (FIT5132 or FIT9132) or equivalent

Chief Examiner

Campus Lecturer

Monash Online

Wray Buntine

Consultation hours: Tue 10am-12 noon; Wed 7pm-9pm

Your feedback to Us

Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through the Student Evaluation of Teaching and Units (SETU) survey. The University’s student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.

For more information on Monash’s educational strategy, see:

www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

Academic Overview

Learning Outcomes

On successful completion of this unit a student should be able to:
  1. analyse the role of data in different styles of business;
  2. demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use;
  3. assess tasks for data curation and management in an organisation;
  4. classify participants in a data science project: such as statistician, archivist, analyst, and systems architect;
  5. classify the kinds of data analysis and statistical methods available for a data science project;
  6. locate and assess resources, software and tools for a data science project.

Unit Schedule

Week Activities Assessment
0 unit orientation No formal assessment or activities are undertaken in week 0
1 Overview of data science and its impact, and data science projects  
2 Data business models, application areas and case studies.  
3 Characterising data and "big" data, data sources and case studies. Online test 1.
4 Resources, standards, and case studies.  
5 Data analysis theory and the process. Online test 2.
6 Data management issues and frameworks. Assignment 1, Assignment 2, Assignment 3 part 1 and 2 due.
7    
8    
9    
10    
11    
12    
     
  Examination period LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/
academic/education/assessment/
assessment-in-coursework-policy.html

*Unit Schedule details will be maintained and communicated to you via your learning system.

Teaching Approach

Other
All unit interactions are online. Content (videos, papers) are delivered online with forum discussions, and there is a 2 hour group interactive session online each week.

Assessment Summary

In-semester assessment: 100%

Assessment Task Value Due Date
Assignment 1: Data Science and me. 10% Friday Week 6
Assignment 2: Data Science Resources 20% Friday Week 6
Assignment 3: Business and data case study 40%+10% Wednesday Week 6 + Sunday Week 6
Test 1: demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use 10% Friday Week 3
Test 2: classify the kinds of data analysis and statistical methods available for a data science project; 10% Friday Week 5

Assessment Requirements

Assessment Policy

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Assignment 1: Data Science and me.
    Description:
    Learning outcomes 4 and 6.  For this assessment task, students are required to create and maintain a reflective journal that specifies their goals and aspirations for a career in data science.  In the journal, students must also specify training and resources required for them to achieve their goal.  Approximately 200 words are to be entered 6 times (totalling approx 1200 words) during the teaching period.

     
    Weighting:
    10%
    Criteria for assessment:

    The completed journal will be assessed on your ability to:

    1. Classify roles in data science project such as statistician, archivist, analyst and systems architect

    2. Identify and assess resources, software and tools.

     

    Due date:
    Friday Week 6
  • Assessment task 2
    Title:
    Assignment 2: Data Science Resources
    Description:
    Learning outcomes 5 and 6.  For this assessment task, students are required to create and maintain their own detailed annotated list of resources (software, tools, data) required for a proposed data science project as well as a list of the particular standards they may use.  Annotation should explain justifications. They are not doing the data science project, they just have to describe it sufficiently to then present resources. 
    Weighting:
    20%
    Criteria for assessment:

    The report will be assessed on demonstration of:

    1. Classifying the kinds of data analysis and statistical methods available for a data science project.

    2. Identifying and assessing resources, software and tools for a data science project.

     

    Due date:
    Friday Week 6
  • Assessment task 3
    Title:
    Assignment 3: Business and data case study
    Description:
    Learning outcomes 1, 2, 3 and 4.  For this assessment task, students are required to prepare and present a detailed report based on a business and data case study.  The report must explain:
    • how the case study fits into the classification and lifecycle models
    • what sorts of data is required, and its ‘V” characteristics’
    • the enabling factors behind the success of the project
    • data curation and management
    • its business value to the organisation.
    The report will be in the form of both a 1000 word report and a 3 minute video presentation.

    Second, the student is required to do a peer review of another student's report.
    Weighting:
    40%+10%
    Criteria for assessment:

    The report and video will be assessed on the demonstration and knowledge of unit outcomes.  The peer review will be assessed on the analysis of same.

    Due date:
    Wednesday Week 6 + Sunday Week 6
  • Assessment task 4
    Title:
    Test 1: demonstrate the size and scope of data storage and data processing, and classify the basic technologies in use
    Description:
    Learning outcome 2.  A multiple choice test of 25 questions will be done.
    Weighting:
    10%
    Criteria for assessment:

    Correctness.

    Due date:
    Friday Week 3
  • Assessment task 5
    Title:
    Test 2: classify the kinds of data analysis and statistical methods available for a data science project;
    Description:
    Learning outcome 5.  A multiple choice test of 25 questions will be done.
    Weighting:
    10%
    Criteria for assessment:

    Correctness.

    Due date:
    Friday Week 5

Learning resources

Monash Library Unit Reading List (if applicable to the unit)
http://readinglists.lib.monash.edu/index.html

Feedback to you

Types of feedback you can expect to receive in this unit are:

  • Graded assignments with comments
  • Test results and feedback
  • Quiz results

Extensions and penalties

Returning assignments

Assignment submission

It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an electronic submission). Please note that it is your responsibility to retain copies of your assessments.

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Technological Requirements

Students must regularly check Moodle for announcements.  Video, audio, PDF and ePUBs for unit material will be made available through Moodle so access to a laptop or similar to view the material is necessary.  Some work will be done in the language Python so you must have access to a machine running Python with appropriate libraries.

Other Information

Policies

Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University’s academic standards, and to provide advice on how they might uphold them. You can find Monash’s Education Policies at: www.policy.monash.edu.au/policy-bank/academic/education/index.html

Faculty resources and policies

Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/

Graduate Attributes Policy

Student Charter

Student services

Monash University Library

Disability Liaison Unit

Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis.

[an error occurred while processing this directive]