ETC5510: Introduction to Data Analysis
Semester 1 2020
- Lecturer: Stuart Lee
- Chief Examiner: Di Cook
- Tutors: Steph Kobakian, Sherry Zhang
This is the course website. This page shows information about the consultation times, MoVE, and Schedule and lecture notes.
The “Assessments” tab above provides information about all assessment for ETC5510.
Expectations
- Unless your question is a personal issue or an administrative matter, please do not send us direct email. The ED site is appropriate for questions related to the course material.
- For personal or administrative issues, the email contact is: ETC5510.Clayton-x@monash.edu
- ED is used for asking for help, reading and lab exercise quizzes and assignment turn in. Only your name and email is stored in this system.
- If you register after the start of the semester or if you miss a lecture/lab, it is your responsibility to catch up with missed material, learn about due dates for material to be turned in, and getting assigned to a group for team work.
- This class is now online, due to extenuating circumstances. Please do not attend in person, lectures will be made available online via moodle. Given the current circumstances, it may be best to enrol second semester. An alternative is to complete a microcredential offered online. Completing this is equivalent and gives you credit for ETC5510, and would. keep you on track for MBAt. This started Mar 2 but it is still possible to enrol up until Mar 11. More information is available at https://www.futurelearn.com/microcredentials/data-driven-decision-making.
MoVE
Borrow a laptop: If you are enrolled in a MoVE unit and forget your laptop, or do not own one as yet, please visit Monash Connect to borrow a laptop for an activity or part of a day:
Clayton: 7.45AM - 5PM
You may be required to provide proof of ID (student card or personal ID) in order to borrow a laptop. We have a limited amount of laptops available for students to borrow (during semester only). Collect an IT Services ticket when you go to Monash Connect.
Consultation times
Consultations begin from Week 2 (16th March)
- Steph Kobakian: Tuesday 12pm-1.30pm and Friday 1:30pm-3:00pm
- Sherry Zhang: Thursday 9am-12pm
- Stuart Lee: Monday 4:30pm-5:30pm and Wednesday 4:30pm-5:30pm
Practical Exam
Download the practical exam at this link (Opens at 6pm).
Project
Instructions for the group project is available via the assessments tab.
Schedule
The first week of semester March 9 - March 13 is online only
The lecturer and the tutors will be available this week via the ED forum and Zoom to answer any questions you have about the course or content.
The lecturer is available for consultation at the following times
- Monday 09/03 3-5pm
- Tuesday 10/03 4-6pm
- Wednesday 11/03 4-6pm
The tutors will be monitoring the ED forum during class times.
Lecture recordings for this week will be posted on Moodle. These are based on lectures recorded for ETC1010, so please ignore any reference to Rstudio cloud and instead refer to the Setup R handout.
From the second week onwards
There are two lectorials every week:
- Monday 6-8pm (Room 321 - LTB Bldg 92)
- Wednesday 6 - 8pm (Room 321 - LTB Bldg 92)
There are no lectorials during the midsemester break.
| Week | Lecture | Date | Slides | Topic | Exercise | Readings | Assessment |
|---|---|---|---|---|---|---|---|
| 1 | A | 2020-03-09 | Introduction to the language of data analysis | Chapter 4 (very short) | NA | ||
| 1 | B | 2020-03-11 | Chapters 1 - 4 in Rmarkdown for Scientists | NA | |||
| 2 | A | 2020-03-16 | Tidy data principles, reshaping your data into tidy form, and basic data wrangling | Chapter 12: Tidy Data | NA | ||
| 2 | B | 2020-03-18 | Chapter 5: Data Transformation | NA | |||
| 3 | A | 2020-03-30 | Plotting your data, and wrangling temporal data | Chapter 3: Data visualisation (again!) | NA | ||
| 3 | B | 2020-04-01 | Chapter 16: Dates and Times | NA | |||
| 4 | A | 2020-04-06 | Advanced wrangling, joining tables, and advanced data visualisation | Chapter 13: Relational Data | NA | ||
| 4 | B | 2020-04-08 | Chapter 1 of Data Visualisation: A Practical Introduction | Assignment 1 | |||
| Midsemester Break | |||||||
| 5 | A | 2020-04-20 | Handling missing values and scraping data |
Getting Started with Missing values Gallery of Missing Data Visualisations |
NA | ||
| 5 | B | 2020-04-22 |
rvest R package for web scraping README rvest description of selectorGadget |
NA | |||
| 6 | A | 2020-04-27 | Introduction to programming | Functions | NA | ||
| 6 | B | 2020-04-29 | Iteration | Mid Semester Electronic Exam | |||
| 7 | A | 2020-05-04 | Introuction to linear models | Modelling Basics | NA | ||
| 7 | B | 2020-05-06 | Many Models | NA | |||
| 8 | A | 2020-05-11 | Analysing text data |
Introduction to tidy text analysis Sentiment Analysis with tidy text |
NA | ||
| 8 | B | 2020-05-13 | n-grams | NA | |||
| 9 | A | 2020-05-18 | Wrangling, plotting and modeling network data | None | NA | ||
| 9 | B | 2020-05-20 | Project Developement / Making Dashboards | None | Assignment 2 | ||
| 10 | A | 2020-05-25 | Computational modeling - classification and regression trees | None | NA | ||
| 10 | B | 2020-05-27 | None | NA | |||
| 11 | A | 2020-06-01 | Project Development | None | NA | ||
| 11 | B | 2020-06-03 | Project Development | None | Takehome Exam | ||
| 12 | A | 2020-06-08 | Submission and Peer Evaluation of Projects | None | Project Due | ||
| 12 | B | 2020-06-10 | None | Project Peer Evaluation | |||