data scientist: the sexiest job in the 21st century
TRANSCRIPT
Presenter’s Name
Data Scientist: The Sexiest Job in the 21st Century
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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
What is Data Science?
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What is Data Science, anyway?
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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
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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
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“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
Who needs a Data Scientist?
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Chart via Forbes
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Source: Data Science Central, 2015
Top Companies hiring Data Scientists
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The Best Big Data And Business Analytics Companies To Work For In 2015
Source: Forbes
What makes a Data Scientist?
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Source: Brendan Tierney - Oralytics Blog
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Source: EMC
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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?
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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
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Most in-demand data skills
Source: WANTED Analytics, 2014
Which Data Scientist are you?
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Source: Udacity
4 Types of Data Science Roles
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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.
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A Data Scientist is a Data Analyst Who Lives in San Francisco:
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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!
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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:
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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:
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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.
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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)
Resources to Learn More
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Resources for ongoing learning
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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
Presenter’s Name
Thank you