Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Data science is different from Data Analytics,Data Engineering,Big Data.
Presentation about Data Science.
What is Data Science its process future and scope.
Data Science Presentation By Amit Singh.
"Sexiest job of 21st century"
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Data Science is all about Data or information ,research and decision-making.Data Science refers to how well data match reality.
content: HISTORY,WHAT IS DATA SCIENCE, NEED OF DATA SCIENCE, APPLICATION OF DATA SCIENCE, CHALLENGES OF DATA SCIENCE, DATA SCIENCE CAREER, SKILL REQUIRED FOR DATA SCIENCE, COURSE, JOBS, SALARY,CONCLUSION
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Defining Data Science
• What Does a Data Science Professional Do?
• Data Science in Business
• Use Cases for Data Science
• Installation of R and R studio
Data Science is a wonderful technology that has applications in almost every field. Let's learn the basics of this domain on 16th March at (time).
Agenda
1. What is Data Science? How is it different from ML, DL, and AI
2. Why is this skill in demand?
3. What are some popular applications of Data Science
4. Popular tools and frameworks used in Data Science
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
My presentation at The Richmond Data Science Community (Jan 2018). The slides are slightly different than what I had presented last year at The Data Intelligence Conference.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
The slide aids to understand and provide insights on the following topics,
* Overview for Data Science
* Definition of Data and Information
* Types of Data and Representation
* Data Value Chain - [ Data Acquisition; Data Analysis; Data Curating; Data Storage; Data Usage ]
* Basic concepts of Big Data
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka!
This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
You can read the blog here: https://goo.gl/OoDCxz
You can also take a complete structured training, check out the details here: https://goo.gl/AfxwBc
Introduction to Data Science and AnalyticsSrinath Perera
This webinar serves as an introduction to WSO2 Summer School. It will discuss how to build a pipeline for your organization and for each use case, and the technology and tooling choices that need to be made for the same.
This session will explore analytics under four themes:
Hindsight (what happened)
Oversight (what is happening)
Insight (why is it happening)
Foresight (what will happen)
Recording http://t.co/WcMFEAJHok
Data Science is all about Data or information ,research and decision-making.Data Science refers to how well data match reality.
content: HISTORY,WHAT IS DATA SCIENCE, NEED OF DATA SCIENCE, APPLICATION OF DATA SCIENCE, CHALLENGES OF DATA SCIENCE, DATA SCIENCE CAREER, SKILL REQUIRED FOR DATA SCIENCE, COURSE, JOBS, SALARY,CONCLUSION
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
Introduction to various data science. From the very beginning of data science idea, to latest designs, changing trends, technologies what make then to the application that are already in real world use as we of now.
Big Data and Data Mining - Lecture 3 in Introduction to Computational Social ...Lauri Eloranta
Third lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015.(http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
This presentation was provided by Dave Kochalko of Artifacts during the NISO event, "Is This Still Working? Incentives to Publish, Metrics, and New Reward Systems," held on February 20, 2019.
Big data, new epistemologies and paradigm shiftsrobkitchin
This presentation examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines.
Presentation given at the HEA Social Sciences learning and teaching summit 'Exploring the implications of ‘the era of big data’ for learning and teaching'.
A blog post outlining the issues discussed at the summit is available via: http://bit.ly/1lCBUIB
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
Basic phrases for greeting and assisting costumers
What is Data Science
1. What
is
Data
Science
Looking
for
an
objective,
complete,
inclusive,
accurate
and
succinct
definition
of
this
emerging
field
Ioannis
Kourouklides
www.kourouklides.com
2. Contents
• Introduction
• History
• Related
terms
• Definitions
by
various
individuals
• Domain
expertise
• Data
Science
in
the
job
market
• How
Data
Scientists
are
self-‐defined
• Summary
• Conclusion
• References
&
Bibliography
3. Introduction
• In
a
Forbes
article,
Gil
Press
(2013)
admits
himself,
among
others,
that
Data
Science
(DS)
is
a
buzzword without
a
clear
definition
• A
quick
search
in
online
and
print
resources
verifies
this
lack
of
description
• Several
people
and
companies
expressed
their
own
opinion
on
the
matter
• Nonetheless,
most
definitions
overlap
with
each
other
• Data
Science
is
not
concerned
with
everything
that
has
to
do
with
data
• A
brief
look
at
the
recent
history
can
give
more
insight
• The
proper
(concrete)
definition
of
this
science
would
have
to
come
from
the
industry rather
than
academia and
might
keep
evolving
through
time
4. History
• The
term
“Data
Science”
has
been
around
for
more
than
30
years
• It
did
not
always
have
the
same
meaning,
but
it
picked
up
since
then
• Gil
Press
(2013)
authored
an
article
about
the
evolution
of
the
term
• 1966:
Peter
Naur
used
the
term
“Science
of
Data”
interchangeably
with
“Datalogy”
as
a
synonym
of
Computer
Science
in
his
courses
(Naur,
1968)
• 1974:
Naur
published
the
book
‘Concise
Survey
of
Computer
Methods’
which
is
a
survey
of
modern
data
processing
methods
• 1989:
Gregory
Piatetsky-‐Shapiro
organized
and
chaired
the
first
Knowledge
Discovery
in
Databases
workshop.
In
1995,
it
became
the
annual
ACM
Conference
on
Knowledge
Discovery
and
Data
Mining
(KDD).
5. History
• 1996:
International
Federation
of
Classification
Societies
(IFCS)
used
the
term
“Data
Science”
for
the
first
time
in
the
title
of
the
conference
(“Data
science,
classification,
and
related
methods”)
• 1997: C.F.
Jeff
Wu
gave
his
inaugural
lecture
entitled
‘Statistics
=
Data
Science?’
(“Identity
of
statistics
in
science
examined,”
1997)
• 2001:
William
S.
Cleveland
published
‘Data
Science:
An
Action
Plan
for
Expanding
the
Technical
Areas
of
the
Field
of
Statistics’
• 2002:
Launch
of
‘Data
Science
Journal’
by
CODATA
of
ICSU
• 2003:
Launch
of
‘Journal
of
Data
Science’
by
Columbia
University
• 2005:
National
Science
Board
defined
what
a
Data
Scientist
is
• 2007:
Nathan
Yau
wrote
about
the
“Rise
of
the
Data
Scientist”
6. Related
terms
• But
let’s
look
at
some
related
(possibly
overlapping)
terms:
• Machine
Learning
• Data
Mining
• Predictive
Analytics
• Statistics
• Big
Data
• Data
Analysis
• Business
Intelligence
• Data
Engineering
• Business
Analytics
• Knowledge
Discovery
in
Databases
• For
a
comparison
of
these
terms
with
Data
Science:
http://goo.gl/uW15El
7. Definition
by
M.
Loukides
• Loukides
(2010)
wrote
an
article
about
‘What
is
data
science?’
• “Data
science
requires
skills
ranging
from
traditional
computer science to
mathematics to
art.”
• “Data
scientists
combine
entrepreneurship with
patience,
the
willingness
to
build
data
products
incrementally,
the
ability
to
explore,
and
the
ability
to
iterate
over
a
solution.
They
are
inherently
interdisciplinary.
They
can
tackle
all
aspects
of
a
problem,
from
initial
data
collection
and
data
conditioning
to
drawing
conclusions.”
• This
is
not
a
very
precise
definition,
but
it
is
insightful
enough
• He
also
highlighted
the
industry’s
perspective
and
the
escalated
job
trends
8. Definition
by
D.
Conway
• Conway
(2010)
gave
a
less
vague
definition:
“…one
needs
to
learn
a
lot
as
they
aspire
to
become
a
fully
competent
data
scientist.
Unfortunately,
simply
enumerating
texts
and
tutorials
does
not
untangle
the
knots.
Therefore,
in
an
effort
to
simplify
the
discussion,
and
add
my
own
thoughts
to
what
is
already
a
crowded
market
of
ideas,
I
present
the Data
Science
Venn
Diagram…
hacking
skills,
math
and
stats
knowledge,
and
substantive
expertise.”
9. Definition
by
P.
Warden
• An
other
description
of
DS
(Warden,
2011)
appears
to
be
the
following:
• “There
is
no
widely
accepted
boundary
for
what’s
inside
and
outside
of
data
science’s
scope.
Is
it
just
a
faddish
rebranding
of
statistics?
I
don’t
think
so,
but
I
also
don’t
have
a
full
definition.
I
believe
that
the
recent
abundance
of
data
has
sparked
something
new
in
the
world,
and
when
I
look
around
I
see
people
with
shared
characteristics
who
don’t
fit
into
traditional
categories.
These
people
tend
to
work
beyond
the
narrow
specialties
that
dominate
the
corporate
and
institutional
world,
handling
everything
from
finding the
data,
processing it
at
scale,
visualizing it
and
writing
it
up
as
a
story.
They
also
seem
to
start
by
looking
at
what
the
data
can
tell
them,
and
then
picking
interesting
threads
to
follow,
rather
than
the
traditional
scientist’s
approach
of
choosing
the
problem
first
and
then
finding
data
to
shed
light
on
it.”
12. Definition
by
F.
Lo
• When
searching
online
the
phrase
‘define
data
science’,
an
excellent
article
(Lo,
2013)
appears
as
the
suggested/endorsed
answer
by
Google
• “Data
science
is
multidisciplinary;
the
skill
set
of
a
data
scientist
lies
at
the
intersection
of
3
main
competencies.”
• “Also,
a
big
misconception
is
that
data
science
is
all
about
statistics.
While
statistics
are
important,
it
is
not
the
only
type
of
mathematics
that
should
be
well-‐understood
by
a
data
scientist.”
• “A
defining
personality
trait
of
data
scientists
is
they
are
deep
thinkers
with intense
intellectual
curiosity.”
13. Definition
by
M.
Mut
• Mut
(2013)
went
a
step
further
and
classified
Data
Scientists
into
3
distinct
specialties
with
very
little
overlap:
• “Advanced
Analysis:
Math,
Stats,
Pattern
Recognition/Learning,
Uncertainty,
Visualization,
Data
Mining” – let’s
call
them
Data
Researchers
• “Computer
Systems
-‐ Advanced
Computing,
High
Performance
Computing,
Visualization,
Data
Mining” – let’s
call
them
Data
Hackers
• “Databases -‐ Data
Engineering,
Data
Warehousing”
– let’s
call
them
Data Developers
• He
claimed
that
DS
is
defined
to
include
all
these
specialties
and
thus
makes
life
confusing
for
employers
and
applicants
• He
proposed
a
solution
would
be
to
educate
HR
and
employers
that
they
need
to
break
DS
into
specialties
14. Definition
by
V.
Granville
• However,
Granville
(2014)
and
others
disagreed
with
Mut.
They
maintained
that combining
these
different
areas
is
not
impossible
and
they
forecasted
that
in
the
future
there
will
be
more
skills
overlap
within
individuals
• In
his
book
‘Developing
Analytic
Talent:
Becoming
a
Data
Scientist’
he
seems
to
provide
the
most
convincing
and
conforming
definition:
• “Data
Science
is
the
intersection
of
computer
science,
business
engineering,
statistics,
data
mining,
machine
learning,
operations
research,
Six
Sigma,
automation
and
domain
expertise.”
• “…
people
interested
in
a
data
science
career
don’t
need
to
learn
[…]
everything
from
these
domains.”
15. Domain
expertise
• Domain
expertise
and
business
acumen
are
totally
essential
for
DS
• This
depends
on
the
kind
of
data
and
their
source,
such
as:
• Bioinformatics
&
Genomics
• Information
Security
• Computer
Vision
&
Image
Processing
• Finance
&
Econometrics
• Insurance
• Marketing
• Medicine,
Health
&
Biomedical
applications
• Particle
Physics
• Social
Networks
• Telecoms
&
Utilities
• Web
&
Text
Mining
16. Data
Science
in
the
job
market
• Data
Scientist
roles
can
be
referred
to
by
various
names
according
to
the
seniority
level,
the
specific
skillset
and
area
of
expertise
• Frequently
required
skills
are:
• Hadoop/MapReduce/MongoDB/Hive
(not
always
necessary,
sometimes
as
a
plus)
• SQL
(though
less
popular
than
NoSQL)
• Perl/Java/PHP/.NET/Ruby/C++
• Machine
Learning
techniques
• Python/R/MATLAB/Octave/SPSS/SAS/Stata/Mathematica
• Advanced
level
degree:
MSc
or
PhD
• Work
experience
(typically
more
than
1-‐3
years)
• Communications
skills
18. How
Data
Scientists
are
self-‐defined
• Harris
et
al.
(2013)
identified
four clusters
(latent
factors)
of
Data
Scientists
in
their
book,
using
Non-‐negative
Matrix
Factorization:
• The
three
specializations
overlap
with
the
ones
mentioned
by
Mut (2013)
• The
forth
one
refers
mostly
to
CDOs
(Chief
Data
Officers),
self-‐identified
as:
Leaders,
Businesspersons,
or
Entrepreneurs
Data Researcher Researcher Scientist Statistician
Data Hacker Hacker Artist Jack
of
All
Trades
Data
Developer Developer Engineer -‐
19. How
Data
Scientists
are
self-‐defined
• The three specializations have started to emerge as three job positions:
• Nothing stops a person who studied Science from becoming a Data
Developer or Data Hacker and nothing stops a person who studied
Engineering from becominga Data Researcher
• Thus, it is the author’s belief that the terms ‘Scientist’ and ‘Engineer’
should not have been used, as they are misleading
Data Researcher Data
Scientist
Data Hacker Machine Learning
Engineer
Data
Developer Data Engineer
20. Summary
• In
brief,
one
can
split
down
the
skills
defining
DS
into
three
groups:
Note:
Each
column
above
is
not
related
to
the
adjacent
ones
Soft
skills
Communication
Business
knowledge
Domain
expertise
Knowledge &
Research
skills
Machine
Learning
– Data
Mining
Statistics
&
other
Maths
Relational
Databases
High
Performance
Computing
Data
Visualization
Coding
skills
Perl/Java/C#/PHP/Ruby/C++
Python/R/MATLAB/Octave
SPSS/SAS/Stata/Mathematica
Hadoop/MongoDB/Hive
SQL/JSON/XML/HTML/CSS
21. Conclusion
• To
sum
up,
DS
is
an
interdisciplinary science,
but
without
a
clear
definition
• It
can
be
defined
as
a
set
of
skills
from
Computer
Science,
Statistics,
…
• It
definitely
requires
some
Research qualities,
but
also
Domain Expertise
• Machine
Learning
is
at
the
epicentre
of
this
newly
coined
term
• Different
Data
Scientists
used
to
focus
or
specialize
in
one
area
of
expertise
• It
is
the
author’s
belief
that
future
Data
Professionals
will
be
required
to
have
three
distinct specializations
similar
to
Quantitative
Professionals,
i.e.
Quant
Researchers,
Quant
Traders and
Quant
Developers
corresponding
to
Data
Scientists,
Machine
Learning
Engineers
and
Data
Engineers respectively
• More
resources
can
be
found
at
the
next
slides
22. References
&
Bibliography
1. Gil
Press
http://www.forbes.com/sites/gilpress/2013/05/28/a-‐very-‐short-‐
history-‐of-‐data-‐science/
2. . Naur,
P.,
“'Datalogy',
the
science
of
data
and
data
processes.” IFIP
Congress
2,
1968,
pp.
1383-‐1387.
3. "Identity
of
statistics
in
science
examined".
The
University
Records,
9
November
1997,
The
University
of
Michigan.
http://ur.umich.edu/9899/Nov09_98/4.htmRetrieved
8
August
2014.
4. Cleveland,
W.
S.
(2001).
"Data
Science:
An
Action
Plan
for
Expanding
the
Technical
Areas
of
the
Field
of
Statistics". International
Statistical
Review
/
Revue
Internationale
de
Statistique 69 (1).
5. .
http://radar.oreilly.com/2010/06/what-‐is-‐data-‐science.html