Year 10 Data Science Work Experience Week

Welcome!

Acknowledgement of country

UNSW Kensington Campus is located on the unceded lands of the Bedigal people. We pay our respects to their Elders, past and present, as the Traditional Custodians of this land.

Health and Safety Induction

  • We are following the HS414 Visitors to UNSW Facilities Guideline
  • Visitors will be met by the person organizing the visit
  • Visitors need to be accompanied and supervised by UNSW staff members or teaching assistants
  • Visitors are not allowed in medium-or high-risk areas
  • Please fill the HS630 Visitor Induction Form with your contact details and check all items that apply to the induction
  • All visitors must agree to follow any reasonable instruction in relation to health and safety.
  • Report all work related hazards, incidents, injuries and illnesses to supervising staff members or teaching assistants

General safety in the classroom

  • No special gear is required
  • No food or drinks in the classroom
  • Please keep the classroom clean and tidy
  • Locate first aid equipment
  • Locate fire extinguishers

You are responsible for the safety of you and others around you - Take care!

  • if you see something unsafe, tell your teacher or other staff member

Cold & Flu safety

  • You are welcome to use face masks and are even encouraged to use them
  • Sanitise/wash hands regularly
  • if you feel unwell, please stay at home

Emergency Procedure

In case of an emergency

  • call UNSW Security Services
  • do not call 000

Security Services

  • in an emergency 9385 6666

  • everything else 9385 6000

  • Security Office located at Gate 2, open 24/7

Emergency Evacuation

  • BEEP BEEP: prepare to evacuate. Do not leave until WHOOP WHOOP alarm
  • WHOOP WHOOP: evacuate immediately. Follow staff and building warden instructions
  • Assemble infront of John Clancy auditorium

First Aid - Physical

  • Location of first aid kit and first aid room (Rm G003, E26)
  • Location of Automatic External Defibrillator
  • Any incident requiring the use of first aid, however minor, must be reported online to UNSW

First Aid - Mental

UNSW First Responders are students and staff who are trained to offer you confidential support. They understand that reporting gendered violence can be difficult and can provide you with guidance and support.

Scott Sisson, uDASH director

You can also contact a certified ally@UNSW in the School of Maths and Stats, Faculty of Science. The ally@UNSWnetwork aims to ensure UNSW is a safe and welcoming place for all LGBTIQ+ students and staff.


Map by MazeMap

UNSW Year 10 Data Science Work Experience Week

Aim

Learn the basic principles of data science. Exploratory data analysis, statistical modelling and visualisation.

Students will

  • learn basic programming concepts
  • get hands-on experience in analysing real-world datasets
  • work in independent teams on data science projects

UNSW Data Science Hub (uDASH)

An official UNSW Research Centre in the School of Mathematics & Statistics

  • formally established in 2021

Aim

  • Bring together UNSW’s full spectrum of data specialists to solve complex, real-world challenges, faced by governments and businesses

Data experts across UNSW

  • 100+ data experts across UNSW’s broad and diverse faculties (Science, Arts, Engineering, Medicine, Law, Business, and Aus. Defence Force Academy)
  • All experts researching cutting edge applications using data science tools

We translate large volumes of data into knowledge to support decision-making. ​

uDASH

Our expertise:

  • Mathematics and Statistics
  • Machine Learning and Artificial Intelligence
  • Data Visualisation
  • Computational modelling and simulation
  • Non-linear dynamics and optimisation
  • Data privacy
  • Probablistic modelling
  • Risk quantification and management
  • Business, economics, and marketing
  • Spatial modelling
  • Big and complex data
  • Genomics and medical data
  • Ecological, environmental and climate data
  • Defence research

Workshop Program

Morning sessions 9:30am - 12:00pm

  • Interactive lecture style sessions 10:00am - 12:00pm
  • Special talks 9:30 - 10:00am

Lunch 12:00pm - 1:30pm

  • Free time to explore, get food, etc.

  • Optional but recommended fun activities

    • School of Mathematics and Statistics outreach workshops (45min)
    • Datasoc campus tour (30min)

Afternoon sessions 1:30pm - 4:00pm

  • Independent project work
  • groups of 5-6 students
  • Goal: visualise patterns, postulate hypothesis, statistical analysis and discussion
  • Demonstrators and instructors will give advice and recommendations

Monday

Morning session:

  • Induction
  • Introduction to data science
  • Software + Intro to programming with R

Afternoon session:

  • Intro to datasets
  • Project group selection

Tuesday

Morning session:

  • Meet a data scientist
  • Exploratory data analysis

Afternoon session:

  • Afternoon session: Work on projects (data exploration)

Wednesday

Morning session:

  • Meet a data scientist
    • Peter Hartmann, Westpac Group
  • Statistical Modelling

Afternoon session:

  • Work on projects (statistical modelling)

Thursday

Morning session:

  • Datasoc: who they are, how can they improve your student experience
  • Data visualisation

Afternoon session:

  • Work on projects (visualising results and wrap up)

Friday

  • Presentations
  • Program wrap-up

Your instructors

Dr. Steefan Contractor

Dr. Ziyang Lyu

Dr. José Ferrer

Dr. Maeve McGillycuddy

Dr. Sean Gardiner

Dr. Peng Zhong

Dr. Boris Beranger

Dr. Prosha Rahman

Dr. Anikó Tóth

Contacts and Socials

For urgent matters find my on (Slack)

BREAK

Quick stretch, walk around, switch tables

What are you most excited for during the coming week?

I go on a hike, and everytime I spot a cockatoo, I note down the temperature and atmospheric pressure. Can I use this data to investigate the relationship between temperature and pressure?

What is data science?

Survey

Get inspired!

  • Go to //historyofdatascience.com and browse some profiles…

  • Or scroll through the timeline

  • Who is your favourite data science icon?

    • an 18th century pioneer?
    • a data revolutionary? a data hero?
    • an artificial intelligence Jedi?

The four paradigms of research

Paradigm 1: Experimentation

Father of Modern science

Paradigm 2: Theory led experimentation

Paradigm 3: Numerical modelling

Paradigm 4: Data-intensive scientific discovery

The concept of these four paradigms of research was coined by Jim Gray, a 1998 Turing Award winner, in 2007.

Data science is more than just prediction

Introduction to programming with R and Rstudio