The document discusses the evolving landscape of data science and big data trends for 2017, highlighting the rise of machine learning, the importance of integrating new analytics capabilities into businesses, and recruitment trends for data scientists. Key insights include the increasing demand for machine learning skills, the significance of IoT in business intelligence, and the projected growth in data science jobs amidst a skills shortage. Companies are encouraged to develop strategies to attract and retain data talent while adapting to rapid technological advancements in the field.

1. </>
market insights
data science

2. Each year, Parallel Consulting sits
down and has a conversation
about what is happening in our
industry. It is this discussion that
inspires us to write market insights.
Here are our thoughts on the
biggest and baddest big data
trends in 2017
As 2017 begins, we are seeing big data and data science communities engage
with new tools that specifically cater to data scientists and data engineers who
aren’t necessarily experts in these techniques. Given rapid technological
advances, the question for companies now is how to integrate new data
science capabilities into their operations and strategies—and position
themselves in a world where analytics can upend entire industries. Leading
companies are using their data science capabilities not only to improve their
core operations but also to launch entirely new business models.
data science insights in 2017

3. heard around the water cooler
Machine Learning Will Rule All Internet of Things (IoT) Will Conquer BI
Hadoop Isn’t Flying the Coop Rise of Big Data
Chief Data Officers Social Impact
Data Science and Machine Learning will be fused into one and
business analytics will not survive with our Machine Learning.
Soon – Machine Learning will become a mandatory skill within the
Data Science market.
Organizations are already attracting top Machine Learning Data
Science talent to enrich their departments in 2017.
According to Gartner, 50% of BI platforms will capitalize on event
data streams with the growth of sensor-driven devices. This trend
will harvest a new breed of BI solutions harbouring real-time data
troves from devices.
According to GE’s Industrial Internet Insights Report, the Data
Science marketplace will embrace the rising popularity of IoT skills,
with the IoT market contributing $10-$15 trillion to GDP in the next 20
years.
Hadoop is a beacon of light for Big Data solutions and its positive impact
on enterprise IT budgets has only encouraged its natural growth in the
marketplace. Offering applications from Predictive Analytics to ETL to
Data Visualization and Data Mining, Hadoop is considered the most
fiscally responsible and scalable Big Data Management systems on a
global level.
Today, Big Data technologies are critical for the success of Data
Science pursuits than ever before. Until now, only 30% of businesses
have experienced the Big Data revolution, but with the reduction in
cost of “volume, velocity and variety of data” , big data investments
will rise.
CDOs have been charged with Data Strategy, Governance,
Quality, & Management. With 25% of Enterprises estimated to
recruit CDOs by the end of this year, we can expect to see a
primary drive in data science and advanced analytics capabilities.
According to a study by Deloitte, as the proliferation in video
communicant and wearable digital products continues, there will
be an increase in the social impact Data Science can make in the
Healthcare and Customer satisfaction industries.

4. Top 10 Skills for Data Scientists in 2016
* Data from 3,490 worldwide data science jobs posted on LinkedIn in 2016
0 500 1000 1500 2000
SQL
HADOOP
PYTHON
JAVA
R
HIVE
MAPREDUCE
NOSQL
PIG
SAS
salaries
$165,000 - $200,000
*Increase of
6.4%
over 2016 salary levels
By 2018, There Will Be
Between 2012-2016, growth
of data science jobs by
Global Demand To
Increase In Next 2 Years
12%
490,000
50%
US Shortage of Data
Scientists:
157,000
teams
 The demand for data science savvy
business managers will increase
 Companies will build more data
science “teams”. Instead of hiring
one data science superman, they
will hire the data science Avengers
vs
the market
Interview requests in past
6 months is up by
33%
* Salary for Data Scientist 1-2 years of experience
* Data from McKinsey, The Age of Analytics, Dec. 2016
* Data from McKinsey, The Age of Analytics, Dec. 2016
* Data from Crowdsourcer report, 2016
Jobs in the US

5. Unique Job Postings
Per Month
2900
Added Most New Jobs
Since 2012
1. New York
2. San Francisco
3. Dallas
Top States Advertising JobsTop Metros Advertising Jobs
California
Washington
New York
San Jose
Seattle
New York
Washington DC
Chicago
San Francisco
Virginia
Massachusetts
Parallel Insight: data science job posts growth
Analytics Job Growth vs
STEM Graduates
24% vs 5%
* Emsi, 4th Quarter, 2016, data between 2003-2015
* Emsi, 4th Quarter, 2016
* Emsi, 4th Quarter, 2016
* Emsi, 4th Quarter, 2016
* Emsi, 4th Quarter, 2016
Top States Advertising Jobs
California
Washington
New York
Virginia
Massachusetts
* Emsi, 4th Quarter, 2016

6. Description 2012 JOBS 2016 JOBS CHANGE
2012-2016
% CHANGE
2012-2016
AVERAGE
HOURLY $
EDUCATION
LEVEL
Management Analysts 748,075 817,740 69,665 9% $41.01 BSc
Computer Systems
Analysts
525,722 605,830 80,108 15% $42.83 BSc
Market Research
Analysts & Marketing
Specialists
488,305 560,765 72,460 15% $33.48 BSc
Financial Analysts 266,816 286,490 19,674 7% $45.98 BSc
Operations Research
Analysts
88,751 105,567 16,816 19% $40.47 BSc
Information Security
Analysts
83,119 93,658 10,539 13% $44.85 BSc
Statisticians 28,890 34,131 5,241 18% $40.59 MA
Computer &
Information Research
Scientists
25,400 27,947 2,547 10% $54.87 PhD or
Professional
degree
Economists 19,949 21,060 1,111 6% $52.97 MA
Mathematicians 3,516 3,958 442 13% $52.01 MA
TOTAL 2,278,542 2,557,146 278,604 12% $40.74
 Computer and information research
scientist is the highest-paying job on
the list (nearly $55 per hour average).
 Operations Research analyst is the
fastest-growing job: 19% growth
 Systems analysts added the most
new jobs: 80,000
 Management analysts is the largest:
820,000 total employment
*“Data Scientist” refers to a number of positions: many positions, ranging from financial analysts and
computer systems analysts to statisticians and economists, are increasingly engaged in the art of
data science.
Parallel Insight: data science job growth, salary growth

7. what to notice
0
1
2
3
4
5
6
7
8
9
All Data
Scientists
Researchers Business
Managers
Developers Creatives
I work alone 1 other person 2 other ppl 3-4 other ppl 5 + ppl
Satisfaction(0-10)
1 2 3 4 5
How Important is the Machine Learning Skill?
* Data from Scientists rating the importance of machine learning scale 1-5
* Data from Crowdflower Data Science report, 2016
How Important is Data Science Team Size
* Data from 3,490 worldwide data science jobs posted on LinkedIn in 2016
26%
29%
21%
14%
10%

8. skill report card
tech spotlight
Scikit-learn provides robust machine
learning models such as cluster,
classification, regression with a rich
set of functionality. It’s simple design
has enabled it to be easily accessible
to even non-experts in the machine
learning space.
Hadoop offers an excellent solution
to deal with Predictive Analytics, ETL,
Data Visualization, Data Mining, Data
Warehousing, IoT, or Clickstream
Analysis. It is considered one of the
most preferred as an alternatives to
commercial Big Data Management
systems.
SECRET SKILL!
human skills
o Intellectual curiosity is essential
o MA or PhD in Computer Science, Statistics,
Mathematics and Physics
o Consider data science bootcamp education
o Strong, effective communication skills
o Creative problem solving
o Business acumen
o Project management
o Team player
“The spirit of data science is discovery.” – Frank Lo, Data
Science Director, Wayfair
You want someone who codes.
Companies are looking for data
scientists who can code. We’ll
need more data scientists who
can touch production systems.
Think of it as a data scientist -
data engineer hybrid
Skill Summary
Python, Spark, R These contribute most to salary, center of new big data
SQL, R, Excel, Python Most commonly used tech for data scientist in 2017
Programming proficiency Increases salary for data scientists
Deep learning In-demand skill right now
IoT skills These will be highest in demand in 2017
Hadoop Most fiscally responsible and scalable tool
Predictive Modeling, NLP,
Machine Learning
All skills that make data scientists stand out from the crowd

9. the new “it” things
People Are Looking For...Machine Learning &
Cognitive Computing
Big Data Technology Spending Will Boom
- According to Information Week, this will grow to
over $200 million by 2020 says IDC
The Internet of Things (IoT) Market Will Soar
The hottest projects in data science in 2017 will focus on
streaming media analytics, embedded deep learning,
cognitive IoT, cognitive chat-bots, embodied robotic
cognition, autonomous vehicles, computer vision, etc.
geospatial contextualization
deep cloud-based
development
environment
IoT fog computing
R, Spark, Hadoop with
embedded deep
learning and Cognitive
IoT
Machine Learning
Artificial Intelligence
Projects & Products
Managing real-world
experiments
Public cloud data and streaming analytics services
will predominate
the new data scientist
Spark is the centrepiece of the new cloud data
services platform
predictive analytics
Flexibility to work
at home or office
Artificial Intelligence Will Liberate Insights From Big Data
I want to make
actionable insights!

10. The Modern Data Scientist
AT A G LAN CE
Y O U R M A T H / S T A T S I D E
Machine Learning
Statistical / Predictive Modelling
Bayesian Inference
Supervised Learning: Decision Trees, Random Forests, Logistic
Regression
Supervised Learning: Clustering, Dimensionality Reduction
Optimisation: Gradient Descent & Variants
Y O U R S O F T E R S I D E
Leadership / Team Building
Curious About New Tech / Data
Creativity / Creative Problems
Project Management
Critical Thinking / Strategic
Persuasive Communications
Y O U R T E C H S I D E
Scripting Language - ex: Python
Statistical Computing Package - ex: R
Databases: SQL & NoSQL
Mapreduce Concepts
Hadoop & Hive / Pig
Parallel Databases & Parallel Query Processing
Experience with XaaS- ex: AWS
Y O U R V I S U A L I S A T I O N
Communication With Senior Management & Stakeholders
Story Telling Skills
Translate Data - Driven Insights Into Decisions & Actions
Visual Art Design
R Packages- ex: ggplot, lattice
Visualisation Tools – ex: Flare, D3.js, Tableau

11. retain your data science talent
What Data Scientists Do During the Day
* Information from Deloitte, Analytics Trends 2016: The Next Evolution
3%
60%
19%
9%
4%
5%
Building training sets: 3%
Cleaning and organizing data: 60%
Collecting data sets: 19%
Mining data for patterns: 9%
Refining algorithms: 4%
Other: 5%
Least Enjoyable Part of Data Science?
* Information from Deloitte, Analytics Trends 2016: The Next Evolution
10%
57%
21%
3%
4%
5%
Building training sets: 10%
Cleaning and organizing data: 57%
Collecting data sets: 21%
Mining data for patterns: 3%
Refining algorithms: 4%
Other: 5%
THE ISSUE
Note how these two charts
mirror each other: The things
data scientists do most are the
things they enjoy least.
Last year, CrowdFlower found
that data scientists far
prefer doing the more
creative, interesting parts of
their job, things like predictive
analysis and mining data for
patterns. That’s where the real
value comes.
BUT, you simply can’t do that
work unless the data is properly
labeled. And nobody likes
labeling data.

12. Parallel Insight:
data science talent strategy
o Training Programs in Data Science for employees
o Well-defined career path for data scientists so they
can see their future impact at the company
o Executive training programs on data & analytics
insights in the industry to train leaders who are
passionate about future data science initiatives
o Create Data Labs that help data scientists act on
the opportunities in different parts of the company
identified through analytics.
o Give forward-looking projects to data scientists-
allow them to invent the ways the company can
benefit from big data.
Core Strategic Data Science Company
Strategy:
* Information from Deloitte, Analytics Trends 2016: The Next Evolution
We know from McKinsey’s, the Age of Analytics report that in 3-4 years,
data cleansing will likely be automated. However, in the meantime,
data science remains one of the most sought after jobs in tech. in the
interim- we’re advising our clients to:
Take the best core
engineering talent
with passion for big
data space
Offer them opportunity
on the proviso that for
1st 12-18 months,
cleaning data
Frees up Senior Data
Scientists to spend
more time on
bleeding edge work
Motivates both core
engineering team and
data science team
Retain top data
science talent & core
engineering talent
How to Best Use Data Science Talent:

13. Although in existence for some time, Data Science has recently entered a remarkable phase of
transition and is now an integral function within almost every global organization across multiple
industries. We are proud of our expertise in the market – from the newest technologies to emerging
educational programs. We appreciate the importance of complimenting technical excellence with
commercial awareness and the significance of your team’s ability to communicate technical
findings to non-technical audiences. We help you create a data science recruitment strategy, as
well as a data science core strategy throughout your company to help you retain and grow your
data science program.
machine learning
quantitative analysis
big datapredictive analytics
statistical modelling
open source programming
internet of things data visualization data analytics
data mining
data scientist chief data scientist
data engineer
data science manager
machine learning specialist
lead data scientist head of data science
data engineering
how we help
machine learning engineer director of data science
data science consultant machine learning data scientist
# data scientists in
our system
Parallel Placements
2014-2016:
Avg. Length to
Hire:
167
8173
15 days
Jobs Filled in
as Little as
24 hrs
VP data science CDOs / CIOs

14. get in touch
Team_USA@parallelconsulting.com
T: +1(646) 491-6860
USA
analytics@parallelconsulting.com
T: +44 (0) 20 3326 4100
UK
www.parallelconsulting.com