Share these talks and lectures with your colleagues
Invite colleaguesWe noted you are experiencing viewing problems
-
Check with your IT department that JWPlatform, JWPlayer and Amazon AWS & CloudFront are not being blocked by your network. The relevant domains are *.jwplatform.com, *.jwpsrv.com, *.jwpcdn.com, jwpltx.com, jwpsrv.a.ssl.fastly.net, *.amazonaws.com and *.cloudfront.net. The relevant ports are 80 and 443.
-
Check the following talk links to see which ones work correctly:
Auto Mode
HTTP Progressive Download Send us your results from the above test links at access@hstalks.com and we will contact you with further advice on troubleshooting your viewing problems. -
No luck yet? More tips for troubleshooting viewing issues
-
Contact HST Support access@hstalks.com
-
Please review our troubleshooting guide for tips and advice on resolving your viewing problems.
-
For additional help, please don't hesitate to contact HST support access@hstalks.com
We hope you have enjoyed this limited-length demo
This is a limited length demo talk; you may
login or
review methods of
obtaining more access.
Printable Handouts
Navigable Slide Index
- Introduction
- Content
- Data science
- Motivation for data science
- Example 1: Retail sales data
- Retail sales data: Context
- Retail sales
- Retail sales data: Lessons learned
- Research case studies
- Example 2: Statistics in shipping sector
- Shipping
- Statistics in shipping sector: Context
- Statistics in shipping sector: Research method
- Open source data
- Use a decision tree to identify important variables
- Statistics in shipping sector: Research outcomes
- Statistics in shipping sector: Lessons learned
- Statistical process control
- Example 3: Digital marketing
- Digital marketing: Context
- Digital marketing: Research method
- A/B Testing
- Digital marketing: Research outcomes
- Digital marketing: Lessons learned
- Recap
- Suggested reading
- Enjoy your research!
This material is restricted to subscribers.
Topics Covered
- Data analytics
- IT (Information Technology)
- A/B testing
- Descriptive and exploratory data
Links
Series:
Categories:
Bite-size Case Studies:
External Links
Talk Citation
Coleman, S. (2022, October 30). Data science and industry research [Video file]. In The Business & Management Collection, Henry Stewart Talks. Retrieved October 12, 2024, from https://doi.org/10.69645/SYUB7317.Export Citation (RIS)
Publication History
Other Talks in the Series: Research Methodologies for Application in Business
Transcript
Please wait while the transcript is being prepared...
0:00
I'm delighted to be contributing
to this lecture series
on research methodologies
for application in business.
My talk is about
data science and
industry research
in relation to business
research methodology.
I'm the Technical Director of
NU Solve at Newcastle
University.
NU Solve is an engagement unit
in the School of
Mathematics, Statistics
and Physics,
dedicated to solving
research questions that
arise in industry.
We undertake consultancy,
training and research projects.
I'm also a founding
member of ENBIS,
the European Network for
Business and
Industrial Statistics.
I find that networking with
statistical practitioners and
other like-minded
people from around
the world is not only
enjoyable and rewarding,
but also helps me to deepen
my statistical knowledge.
0:47
In this talk, I will
draw on my experience of
collaborative research with
a wide range of companies.
I've had the privilege of
working on all
sorts of projects.
In particular, we'll offer
examples from the retail,
shipping management and
digital marketing sectors.
I aim to describe what
data science is and
how it can be used,
and how we can overcome
challenges to produce
useful results from our
industry research in practice.
1:12
First of all, what
is data science?
Data science uses a combination
of information
technology skills,
data analytics,
that is statistics,
mathematics, artificial
intelligence,
and business awareness.
In this talk, we
will focus mostly on
data analytics and
business awareness.
1:31
Why is data science so topical?
What is the motivation
for data science?
Well, there has been
an explosion in
the quantity of data collected
by business and industry.
The inexorable drive
towards digitisation in
modern manufacturing and process
industries is often referred
to as Industry 4.0,
and it's characterised by
increasing amounts
of measurement
and more detailed and complex
machine-to-machine interaction.
The availability of
such massive amounts
of data has led to
priceless opportunities
for companies and
business researchers to add
value through data science.
Data science enables
business and industry
to improve the quality of
their products and services,
reduce waste, save energy,
and tailor output to meet
customer needs, thereby
increasing profitability.
Data science and
industry research is
vital for business success.