MIT631 Data Analytics
MIT631 Data Analytics
- May 12, 2022
- Posted by: Nigel Finch
Unit outline
| Important Update: | Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines. This may include a mix of online and face-to-face. Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. |
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| Enrolment modes: | Year 2, Semester 1 |
| Credit point(s): | 12.5 |
| EFTSL value: | 0.125 |
| Prerequisite: | MIT501 Programming |
| Typical study commitment: | Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit. |
| Scheduled learning activities: | 4 timetabled hours per week, 6 personal study hours per week |
| Other resource requirements: | Students will need access to lab computers or will need their own laptops in order to carry out lab exercises and assignments. Students will need to use the latest version of Python IDE such as Spyder, PyCharm or IDLE as well as Jupyter Notebook tool. |
Unit description
This unit covers the fundamentals of data analytics. It aims to develop foundation skills and knowledge required for data driven, evidence-based approaches to decision making and performance analysis. Topics include data collection, preprocessing and transformation, visualization and exploratory analysis, and the mathematical and statistical foundations for data modeling. These will help students develop the understanding they will need to make informed decisions using data analysis and communicate the results effectively. The programming language used is Python which is an excellent environment for building many kinds of analytical applications.
Unit learning outcomes (ULO)
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On the successful completion of this units student will be able to: |
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| ULO1 | Demonstrate knowledge of coding, debugging, and running Python programs. |
| ULO2 | Use Python modules and tools to collect, reshape, analysis, and visualize data. |
| ULO3 | Solve a broad set of data analysis problems effectively. |
| ULO4 | Develop programs for various real-world problems by applying data analytics. |
| ULO5 | Evaluate data results and make optimal decisions. |
Topics to be included in the unit
| 1. | Unit Introduction, Python fundamentals |
| 2. | Lists, tuples, sets, and dictionaries |
| 3. | Functions, classes, and modules |
| 4. | Numpy and vectorized computation |
| 5. | Statistics and probability |
| 6. | Manipulating data with pandas |
| 7. | Data cleaning, preparation, and wrangling |
| 8. | Visualising data with Matplotlib |
| 9. | Data aggregation and group operations |
| 10. | Working with Jupyter notebooks |
| 11. | Data analysis examples |
| 12. | Network analysis & Revision |
Assessment
| Assessment Description | Grading and weighting (% total mark for unit) |
Indicative due week |
| Assessment 1: Class Participation | 10% | 12 |
| Assessment 2: Online Quiz | 10% | 5 |
| Assessment 3: Individual Assignment 1 | 20% | 7 |
| Assessment 4: Individual Assignment 2 | 20% | 12 |
| Assessment 5: Final Exam | 40% | Final exam week |
The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.