4. 4
NEW BREED
Data Scientist are an
emerging new breed of
engineers with specialised
skills in mathematics,
statistics, computer science,
who use their specialised
tool box to tame and draw
inference from unstructured
data and provide insight to
businesses.
5. MORE DATA SCIENTISTS
?
▹ In the 21st century we are drowning in
data.
▹ Business need people to sift through
the data to find information that helps
them.
▹ Shopping patterns, movie
recommendations, search
optimization.
▹ Data Scientist come into the picture
here save the day and the companies!!
5
6. Data scientists want
to be in the thick of
things. They want to
add value to the
company other than
being in an advisory
roles.
TWO KEY INSIGHTS
Data Scientists come
from a varied
backgrounds of
physics, mathematics,
economics, computer
science.
6
7. WHO ARE THESE PEOPLE?
As of right now there is a scarcity of specialized
courses in universities that teach the Data Science.
So most of the employed data scientist are
graduates in physics, mathematics, computer
science, systems biology. Although they are not
specialized in data science they come with the tools
that are used to do data science. With a little on-job
training and a certain mindset that caters to the
needs of the customer, these people become great
data scientist as we can see from the example of
Goldman in Linkedin given in the article who had a
PhD in Physics from Stanford.
7
8. 8
What do they do ?
● Identifying the data-analytics problems that
offer the greatest opportunities to the
organization
● Determining the correct data sets and
variables
● Collecting large sets of structured and
unstructured data from disparate sources
● Cleaning and validating the data to ensure
accuracy, completeness, and uniformity
● Devising and applying models and
algorithms to mine the stores of big data
● Analyzing the data to identify patterns and
trends
● Communicating findings to stakeholders
using visualization and other means
9. 9
What’s in a data scientist’s toolbox?
● Data visualization: the presentation of data in a pictorial or graphical
format so it can be easily analyzed.
● Machine learning: a branch of artificial intelligence based on
mathematical algorithms and automation.
● Deep learning: an area of machine learning research that uses data to
model complex abstractions.
● Pattern recognition: technology that recognizes patterns in data (often used
interchangeably with machine learning).
● Data preparation: the process of converting raw data into another format so it
can be more easily consumed.
● Text analytics: the process of examining unstructured data to glean key
business insights.
10. 10
When is a business ready to hire a data
scientist?
● Does it deal with large amounts of data and have complex issues that need to be solved?
Organizations that truly need data scientists have two things in common: They manage massive
amounts of data, and they face weighty issues on a day-to-day basis. They’re typically in industries such
as finance, government and pharma.
● Does it value data? A company's culture has an impact on whether it should hire a data scientist. Does
it have an environment that supports analytics? Does it have executive buy-in? If not, investing in a data
scientist would be money down the drain.
● Is it ready to change? As a data scientist, you expect to be taken seriously, and part of that entails
seeing your work come to fruition. You devote your time to finding ways your business can better
function. In response, a business needs to be ready – and willing – to follow through with the results of
your findings.
11. HOW CAN MANAGERS
USE THIS INSIGHT?
● Hire graduate
students with
specialization in
the sciences and
engineering.
Provide them with a
little on-job training.
Provide them with a
little on-job training.
11
Give them free reign
and room to innovate
and help the business
grow in new and
innovative ways.
12. WHERE TO FIND
THEM?
There is a lack of
specialized courses on
Data Science.
● the sciences and
engineering.
Business should not wait
for universities to offer
the courses.
They might fall behind.
12
Instead they should
directly hire university
graduates specialized in
mathematical subjects.
13. OTHER SKILLS THEY
SHOULD HAVE.
Data Scientist shouldn’t
only be good at taming
the data.
▹ They should be
great
communicators.
.
They should
communicate their
inferences to the
stakeholders in a
language they
understand.
13
Visual or verbal
communication are a
must.
14. 14
WORD OF
CAUTION!
▫ Business should ensure that data scientist keep up with
the new trends.
▫ They might lose their edge if they spend too much time
with management.
▫ Keeping up with industry specialist helps keep their
skills finely tuned.
▫ Keeping them on too big a leash might hurt the
company.
