Everyone is a data scientist today, but that is impossible. How do you spot the real data scientist from the fake? Some people just lie. Don't be fooled this presentation will help find the fools

1. You said you
were a data
scientist
How to
know lies
from truth
By Paul Ormonde-James

2. Seems every analyst or would be, is a DATA SCIENTIST…….
“The sexiest job of the 21st Century”
Made up of many parts…..
Lies
Damn Lies
& data scientists?

3. What is the mix of skills
Certainly many skills
• What is the important mix for your
organisation?
• What level of experience in which skill is
critical?
• Technical skills are not enough
• How many years of what experience
makes a data scientist useful?
• How do recruiters who do not understand
the disciplines advise you?

4. Data Scientist – Gartner definition
The question is how old do
you have to be to gain all
these skills & experience?
Probably much more than
10 years?
More like 15 years?

5. Which industries lead the data scientist revolution
So how does this differ
from the Australian
perspective.
Listen on………

6. Sector approach to Big Data and Advanced Analytics
Lets analyse the differences
and the opportunities

7. Tenure short as disillusionment sets in

8. Demand does not equal supply

9. IF YOU HIRE A DATA SCIENTIST, DO YOU KNOW HOW TO USE ONE?
It becomes challenging when a so called
Head of Analytics does not understand
Data Science and cannot even allocate
challenging work. Probably more
surprising when senior management buy a
token data scientist because others have
them.
Good news for the quick minded to fool
recruiters and hiring management. They
will not know the difference, and you can
ask for more money!

10. Tools & role of tools for data scientists
A FOOL WITH
A TOOL
IS STILL A FOOL

11. What Data Scientists do not use?
Research shows few, if any, Data
Scientists use excel.
So if excel is the tool of choice, it could
be that person is not a Data Scientist
Which leads to another question, do
analysts use excel? Is the excel tool just
a reporting tool to manipulate integer
data as finance teams do?

12. Fools with tools or tools of trade?
Source: O’Reilly data science survey 2015
Analytic Power users
The clustering can be considered as
Top 3 clusters to approximately
“Power Analysts”, so a business user
who is able to use tools for analysis
but is not a developer.
The lower right quadrant corresponds
to a developer, an individual with an
engineering background able to work
actively in hardcore programming
languages
Hive and business objects fall into a
middle category neither tool is
accessible to most business users
without some significant
commitment and training

13. And so US survey data on job comparisons, luckily not Australian…..
Source: Forbes

14. Typical skills for Data scientists

15. True data scientist has technical, commercial & problem solving skills
Or am I?
Similar to a business/data analyst, data scientists
combines knowledge of computer science and
applications, modelling, statistics, analytics and math
to uncover insights in data. Evolving beyond the
business/data analyst, the data scientist takes those
insights and combines them with strong business
acumen and effective communication to change the
way an organisation approach challenges.
The average day of a data scientist involves extracting
data from multiple sources, running it through an
analytics platform and then creating visualizations of
the data. They will then spend hours cleansing and
analysing the data from multiple angles, looking for
trends that highlight problems or opportunities. Any
insight is communicated to business and IT leaders
with recommendations to adapt existing business
strategies

16. Data scientist measurement – what have they achieved?
HISTORY
EXPERIENCE
DIVERSE SKILLS
COMMERCIAL EXPEIENCE
CURIOSITY & COMMUNICATION

17. So what does an Actuary actually do?
• While data analysts can be found in many types of private and public
sector organizations, actuaries work in the insurance industry.
• They use similar analytical methods to those used by data analysts,
but actuaries' work focuses on the financial losses associated with
accidents, illnesses and natural disasters.
• They then work with businesses and other clients to develop policies
that minimize these risks.
• By assessing the costs associated with risks, actuaries help
insurance carriers to design coverage and estimate the premiums
that should be charged.
• They may not have skills in advanced analytic tools, and may
specialise in excel.

18. Data Analyst
• Data analysts work for technology firms, health-care organizations,
banks, government agencies, educational institutions, consulting
firms and other organizations that collect and handle large amounts
of data. Analysts also have various titles.
• The U.S. Bureau of Labour Statistics classifies many data analysts
as statisticians.
• They apply mathematical and statistical techniques to extract,
analyse and summarise data.
• They use spreadsheet and statistical software, work with relational
databases and prepare charts and reports of their findings.
• Through their work, data analysts transform large, complicated data
sets into usable information that informs organizational leadership
decisions and policies.

19. So pulling it all together we have
• A data scientist represents an evolution from the business or data analyst
role.
• The formal training is similar, with a solid foundation typically in computer
science and applications, modelling, statistics, analytics and maths.
• What sets the data scientist apart is strong business acumen, coupled with
the ability to communicate findings to both business and IT leaders in a way
that can influence how an organisation approaches a business challenge.
• Good data scientists will not just address business problems, they will pick
the right problems that have the most value to the organization.
• The data scientist role has been described as “part analyst, part artist.”
• “A data scientist is somebody who is inquisitive, who can stare at data and
spot trends. It's almost like a Renaissance individual who really wants to
learn and bring change to an organization."

20. Conclusions continued
• Whereas a traditional data analyst may look only at data from a
single source – a CRM system, for example – a data scientist will
most likely explore and examine data from multiple disparate
sources.
• The data scientist will sift through all incoming data with the goal of
discovering a previously hidden insight, which in turn can provide a
competitive advantage or address a pressing business problem.
• A data scientist does not simply collect and report on data, but also
looks at it from many angles, determines what it means, then
recommends ways to apply the data.
• Data scientists are inquisitive: exploring, asking questions, doing
“what if” analysis, questioning existing assumptions and processes.
• Armed with data and analytical results, a top-tier data scientist will
then communicate informed conclusions and recommendations
across an organization’s leadership structure.

21. EMPLOY THE
“A” TEAM
NO Fools allowed

22. Presentation by Paul Ormonde-James
Analytics specialist & regular global speaker on Big Data, advanced
analytics and Business Intelligence.
Held senior Analytics & Business Intelligence roles globally,
including The World Bank in Washington DC.
Board member with Analyst First, Global analytic think tank.
Started life with degrees with honours in Cybernetic Engineering
(robotics & AI) & computer sciences. MBA in Finance and post grad
in Law.
Loves his time using R to just dig into data
Data is his passion, so loves to have a say.
Twitter pormondejames
Linkedin