Data Science,
Artificial Intelligence
& Big Data
Technology Overview
SESL
https://www.abc.net.au/news/2023-04-25/sales-tactics-rife-in-the-realestate-industry-why-they-work/102261072
Cognitive Biases in Business
Artificial Intelligence Research at Torrens University
https://www.torrens.edu.au/blog/torrens-university-researcher-recognised-asworld-best-ai-expert?mc_cid=b33f272423&mc_eid=c7bb19c69a
Professor Seyedali Mirjalili, Centre Director
https://www.abc.net.au/news/rural/2021-05-27/automated-farm-to-userobots-and-artificial-intelligence/100169302
https://www.abc.net.au/news/2022-06-19/whymany-people-arent-comfortable-with-facialrecognition/101157518
https://www.drive.com.au/news/facial-recognition-technology-genesishyu/?utm_source=Nine-Front-Page&utm_medium=Referral
Temporary Holt to AI Development
https://www.9news.com.au/world/ai-updates-elon-musk-bill-gatescall-for-artificial-intelligence-pause-in-out-of-control-race/0f5578ad-
7e80-49a7-a572-36962d877dc0
• Reinforcement learning is the training of machine learning models to
make a sequence of decisions.
• The agent learns to achieve a goal in an uncertain, potentially
complex environment. In reinforcement learning, an artificial
intelligence faces a game-like situation.
• The computer employs trial and error to come up with a solution to
the problem.
• To get the machine to do what the programmer wants, the artificial
intelligence gets either rewards or penalties for the actions it
performs. Its goal is to maximize the total reward.
The reinforcement learning problem
https://deepsense.ai/what-is-reinforcement-learning-the-complete-guide/
Blooms Digital Taxonomy
https://youtu.be/fqgTBwElPzU **
Reading Test
◼ Olny srmat poelpe can raed tihs. I cdnuolt blveiee taht I
cluod aulaclty uesdnatnrd waht I was rdanieg. The
phaonmneal pweor of the hmuan mnid, aoccdrnig to a
rscheearch at Cmabrigde Uinervtisy, it deosn’t mttaer in
waht oredr the ltteers in a wrod are, the olny iprmoatnt
tihng is taht the frist and lsat ltteer be in the rghit pclae.
The rset can be a taotl mses and you can sitll raed it
wouthit a porbelm. Tihs is bcuseae the huamn mnid
deos not raed ervey lteter by istlef, but the wrod as a
wlohe. Amzanig huh? yaeh and I awlyas tghuhot slpeling
was ipmorantt! if you can raed tihs psas it on!!
Recap from
Week 1: 4
purposes of
analysing data
https://www.linkedin.com/pulse/4-stages-data-analytics-maturity-challenginggartners-taras-kaduk/?trackingId=OB0BeGS2rIMMauGR56UIzA%3D%3D
A Predictive Data Science Example: Competitive Bidding
Demand factors set pricing ceiling
Costs set pricing floor
Final pricing
discretion
Market
conditions
Corporate objectives
Competitive
bidding
Example market conditions
Intensity of
competition
(BIDDERS)
Distance from
bidder’s base
(DISTANCE)
Degree of
corruption
(REGLTN)
Predict Competitors’ Bid Prices
but consult the team’s intuition
0.05 BIDDERS
0.95
e
5.31 SIZE
Price
•
•
=
• Develop a formula for predicting the lowest bid price
• Benefits derived from a case study:
→Win rate up by 400%
→$418m sales up by 75.8%

https://youtu.be/xO7xJ1sTyPI **
Data-Driven Tools For Decision-Making
Adapted from Kaduk (2016)
Data Science,
Data Mining &
Big Data
Forecasting
(Machine Learning &
Artificial Intelligence)
(Insight &
Visualisation)
Relationship between AI, ML, DL, Data Science and Big Data
(Press, 2016)
Relationship between AI, ML, DL, Data Science and Big Data
continued
(Press, 2016)
https://youtu.be/X3paOmcrTjQ
(Piatetsky, 2016)
(Piatetsky, 2018)
Python
https://youtu.be/Ti3e5BJwbL0 *
RapidMiner
https://youtu.be/ma14K56fNAM
Modelling of structured data in Data Science
https://youtu.be/QpdhBUYk7Kk **
Big data also deals with unstructured data
http://bigdata.black/wp-content/uploads/2016/04/structured-vs-unstructured-data.png
Now, let’s look at Big Data applications/apps
https://youtu.be/eVSfJhssXUA *
https://image.slidesharecdn.com/c733ac39-349d-4911-8824-456c8db82dee-160908004643/95/introduction-to-big-data-and-data-science-8-1024.jpg?cb=1473295760
https://image.slidesharecdn.com/c733ac39-349d-4911-8824-456c8db82dee-160908004643/95/introduction-to-big-data-and-data-science-9-1024.jpg?cb=1473295760
Big Data
https://image.slidesharecdn.com/c733ac39-349d-4911-8824-456c8db82dee-160908004643/95/introduction-to-big-data-and-data-science-4-638.jpg?cb=1473295760
http://www.bluecoppertech.com/demo/wp-content/uploads/2017/06/blog-post.png
How Is Digitization Changing the Business World?
• Artificial Intelligence (AI)
• Flexible Work
• Innovation
• New Business Models
• Communication
https://youtu.be/ad79nYk2keg **
Now, let’s look at Artificial Intelligence (AI)
https://youtu.be/w-8MTXT_N6A **
Recap From Week 2: Artificial Neural Networks
https://www.planetmainframe.com/2018/10/analytics-and-machine-learning-in-the-modern-datacenter/
• A biological brain neuron is a single cell with dedicated electrical
inputs and electrical outputs
• A biological neuron will “fire” or pass on information based on certain energy levels
within the neuron, induced by its inputs
• An artificial neuron is a mathematical function with one or more
dedicated value inputs and a value output
• An artificial firing is based on its mathematical function and how it is affected by its
weighted inputs
Neural Networks
• Synapses in the human brain alter signals sent by neurons through influencing
signal strength to cause excitation or inhibition of a subsequent neuron
• Information processing recognises patterns of activities (pattern recognition)
rather than following a systematic algorithm
• Learning produces the required values of the weights, which make the
computed outputs equal (or close) to desired outputs. Learning is done by using
historical data and running it on adjusted weights to see if the outputs match the
standards
Adapted from http://www.cognos.com/busintell/products/4thought.html
Deep learning with multi-layer neural networks
https://youtu.be/aircAruvnKk ** Up to 5.5 minutes
https://youtu.be/cfj6yaYE86U
• We can let AI run over data without skilled human
oversight at several stages of the process
• Data science = big data = artificial intelligence
• AI software is easy to use & doesn’t require deep domain
expertise
• AI pays for itself quickly even in the absence of a wellunderstood business problem and appropriate data
Artificial Intelligence Myths
Kelleher and Tierney (2018)
https://www.abc.net.au/news/2022-12-12/robodebt-algorithms-blackbox-explainer/101215902
Artificial Intelligence Challenges