2. WUSS 2015
Experis | Friday, February 26, 2016 2
Our Time Today
– What is Data Science?
– Who needs a Data Scientist?
– What makes a Data Scientist?
– What kind of Data Scientist are
you?
Specialized
Talent and
Solutions
New
Work
Models
Shifts in
Business
4. WUSS 2015
Experis | Friday, February 26, 2016 4
What is Data Science, anyway?
5. WUSS 2015
Experis | Friday, February 26, 2016 5
Hal Varian, the chief economist at Google, is known to have
said, “The sexy job in the next 10 years will be statisticians.
People think I’m joking, but who would’ve guessed that
computer engineers would’ve been the sexy job of the 1990s?”
The Hot Job of the Decade
Source: HBR
6. WUSS 2015
Experis | Friday, February 26, 2016 6
The internet population now has over 2.1 billion people, and with every
website browsed, status shared, and photo uploaded, we leave a digital trail
that continually grows the hulking mass of big data.
Every minute, on average,
• YouTube users upload 48 hours of video
• Facebook users share 684,478 pieces of content,
• Instagram users share 3,600 new photos, and
• Tumblr sees 27,778 new posts published.
A perspective on how Big is Data
Source: That Conference, 2015
7. WUSS 2015
Experis | Friday, February 26, 2016 7
“By 2015, 4.4 million IT jobs globally will be created to support big
data, generating 1.9 million IT jobs in the United States,” said Peter
Sondergaard, senior vice president at Gartner and global head of
Research. “In addition, every big data-related role in the U.S. will
create employment for three people outside of IT, so over the next
four years a total of 6 million jobs in the U.S. will be generated by
the information economy.“
Source:
Gartner Symposium/ITxpo 2012
The Information Economy
16. WUSS 2015
Experis | Friday, February 26, 2016 16
A big data scientist understands how to integrate multiple systems and
data sets.
They need to be able to link and mash up distinctive data sets to
discover new insights.
This often requires connecting different types of data sets in different
forms as well as being able to work with potentially incomplete data
sources and cleaning data sets to be able to use them.
Sound familiar?
17. WUSS 2015
Experis | Friday, February 26, 2016 17
8 Data Skills to Get You Hired
• Basic Tools
• Basic Statistics
• Machine Learning
• Multivariable Calculus and Linear Algebra
• Data Munging
• Data Visualization & Communication
• Software Engineering
Source: Udacity
18. WUSS 2015
Experis | Friday, February 26, 2016 18
Most in-demand data skills
Source: WANTED Analytics, 2014
20. WUSS 2015
Experis | Friday, February 26, 2016 20
Source: Udacity
4 Types of Data
Science Roles
21. WUSS 2015
Experis | Friday, February 26, 2016 21
All joking aside, there are in fact some companies where being a data
scientist is synonymous with being a data analyst.
Your job might consist of tasks like pulling data out of MySQL
databases, becoming a master at Excel pivot tables, and producing
basic data visualizations (e.g., line and bar charts).
You may on occasion analyze the results of an A/B test or take the lead
on your company’s Google Analytics account.
.
A Data Scientist is a Data Analyst Who Lives in San Francisco:
22. WUSS 2015
Experis | Friday, February 26, 2016 22
You’ll see job postings listed under both “Data Scientist” and “Data
Engineer” for this type of position.
Since you’d be (one of) the first data hires, there are likely many low-
hanging fruit, making it less important that you’re a statistics or machine
learning expert.
A data scientist with a software engineering background might excel at
a company like this, where it’s more important that a data scientist make
meaningful data-like contributions to the production code and provide
basic insights and analyses.
Please Wrangle Our Data!
23. WUSS 2015
Experis | Friday, February 26, 2016 23
There are a number of companies for whom their data (or
their data analysis platform) is their product. In this case, the
data analysis or machine learning going on can be pretty
intense.
This is probably the ideal situation for someone who has a
formal mathematics, statistics, or physics background and is
hoping to continue down a more academic path.
Data Scientists in this setting likely focus more on producing
great data-driven products than they do answering operational
questions for the company.
We Are Data. Data Is Us:
24. WUSS 2015
Experis | Friday, February 26, 2016 24
A lot of companies fall into this bucket. In this type of role, you’re joining
an established team of other data scientists.
The company you’re interviewing for cares about data but probably isn’t a
data company. It’s equally important that you can perform analysis, touch
production code, visualize data, etc.
Generally, these companies are either looking for generalists or they’re
looking to fill a specific niche where they feel their team is lacking, such
as data visualization or machine learning.
Reasonably Sized Non-Data Companies Who Are Data-Driven:
25. WUSS 2015
Experis | Friday, February 26, 2016 25
Business Brain
• Concentrate on developing a “business brain” in addition to those
hard data skills.
• Data insights are useless without the foundational knowledge of the
business to which the data belongs.
• Keep your eyes and ears open to absorb as much understanding of
how a business and strategy works.
• Develop presentations to advise senior management in clear language
about the implications of their work for the organization.
• Develop ability to create examples, prototypes, demonstrations to help
management better understand the work.
26. WUSS 2015
Experis | Friday, February 26, 2016 26
The Changing Role of Analysts
• Statistics done by non-Statisticians,
• The growth of Statistics into new areas such as healthcare and financial
applications,
• Greater expectations by management for statisticians to “be responsive
and vital to today's business needs and to be able to prove their
contributions quantitatively,”
• The requirements of analyses to be timely as well as appropriate,
• The need to work with immense databases, and
• Adapting to new forms of communication. (Hahn & Hoerl, 1998)
28. WUSS 2015
Experis | Friday, February 26, 2016 28
Resources for ongoing learning
29. WUSS 2015
Experis | Friday, February 26, 2016 29
As Peter Sondergaard, global head of research at Gartner, said in a
2012 statement,
The most valued data analysts of tomorrow will be able not only to
derive insights from existing data sets, but also to tell the quantitative
future:
“Dark data is the data being collected, but going unused despite its
value. Leading organizations of the future will be distinguished by the
quality of their predictive algorithms. This is the CIO challenge, and
opportunity.”
In conclusion
The investment made in data collection demands putting the data to good use to drive business results forward.
Big data and analytics are all around these days. IBM projects that every day we generate 2.5 quintillion bytes of data. This means that 90% of the data in the world has been created in the last two years.
In 2013, Gartner projected that by 2015, 85% of Fortune 500 organizations would be unable to exploit big data for competitive advantage and that 6M jobs would be created as a result of the data explosion.
Couple of key points:
Using data for predictive analysis
30+% believe that their demand will exceed the supply of available talent
Also- traditional BI professionals may not have the profile required to impact these new requirements. Like SAS says- looking through the windshield instead of the rear view mirror.
According to this graph from Forbes, the greatest preponderance of Data Scientists are working in the services information industry-
Search engines (Google, Microsoft), social networks (Twitter, Facebook, LinkedIn), financial institutions, Amazon, Apple, eBay, the health care industry, engineering companies (Boeing, Intel, Oil industry), retail analytics, mobile analytics, marketing agencies, data science vendors (for instance, Pivotal, Teradata, Tableau, SAS, Alpine Labs), environment, utilities government and defense routinely hire data scientists, though the job title is sometimes different.
Traditional companies (manufacturing) tend to call them operations research analysts.
This article cited that there is very little difference in result between 2013 and 2015, except that there appears for be more growth of hiring in smaller companies.
But these companies corroborate the previous graph; platform and software developers, as well as service/consulting companies are the largest employers of this skills set.
In this graphic, It is this outer ring of skills that are fundamental in becoming a data scientist.
The skills in the inner part of the diagram are skills that most people will have some experience in one or more of them.
The other skills can be developed and learned over time, all depending on the type of person you are.
When it comes to being a data scientist it might be fair to say you are a ‘A jack of all trades and a master of some’.
This graph outlines some of the characteristics that EMC thinks makes a Data Scientist- notice that none of these are technical skills
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
The big data scientist needs to be able to program, preferably in different programming languages such as Python, R, Java, Ruby, Clojure, Matlab, Pig or SQL.
You need to have an understanding of Hadoop, Hive and/or MapReduce.
Many resources out there may lead you to believe that becoming a data scientist requires comprehensive mastery of a number of fields, such as software development, data munging, databases, statistics, machine learning and data visualization.
Don’t worry. You don’t need to learn a lifetime’s worth of data-related information and skills as quickly as possible. Instead, learn to read data science job descriptions closely. This will enable you to apply to jobs for which you already have necessary skills, or develop specific data skill sets to match the jobs you want.
Hopefully this gives you a sense of just how broad the title ‘data scientist’ is. Each of the four company ‘personalities’ above are seeking different skillsets, expertise, and experience levels. Despite that, all of these job postings would likely say “Data Scientist,” so look closely at the job description for a sense of what kind of team you’ll join and what skills to develop.
A company like this is a great place for an aspiring data scientist to learn the ropes. Once you have a handle on your day-to-day responsibilities, a company like this can be a great environment to try new things and expand your skillset
It seems like a number of companies get to the point where they have a lot of traffic (and an increasingly large amount of data), and they’re looking for someone to set up a lot of the data infrastructure that the company will need moving forward. They’re also looking for someone to provide analysis.
There will be less guidance and you may face a greater risk of flopping or stagnating.
Companies like CoreLogic, Lexus Nexus, RAND Corporation make their business out of identifying trends
Companies that fall into this group could be consumer-facing companies with massive amounts of data or companies that are offering a data-based service.
Some of the more important skills when interviewing at these firms are familiarity with tools designed for ‘big data’ (e.g., Hive or Pig) and experience with messy, ‘real-life’ datasets.
Being able to advice senior management in clear language about the implications of their work for the organization;
Having an, at least basic, understanding of how a business and strategy works;
Being able to create examples, prototypes, demonstrations to help management better understand the work;
[Use this slide to summarize what you know about the client’s current business challenges]
