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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.

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.

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Data science market insights usa

  1. 1. </> market insights data science
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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

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