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Data scientists - Who the hell are they V3 @20160501

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Data scientists - Who the hell are they V3 @20160501

  1. 1. You said you were a data scientist How to know lies from truth By Paul Ormonde-James
  2. 2. Seems every analyst or would be, is a DATA SCIENTIST……. “The sexiest job of the 21st Century” Made up of many parts….. Lies Damn Lies & data scientists?
  3. 3. What is the mix of skills Certainly many skills • What is the important mix for your organisation? • What level of experience in which skill is critical? • Technical skills are not enough • How many years of what experience makes a data scientist useful? • How do recruiters who do not understand the disciplines advise you?
  4. 4. Data Scientist – Gartner definition The question is how old do you have to be to gain all these skills & experience? Probably much more than 10 years? More like 15 years?
  5. 5. Which industries lead the data scientist revolution So how does this differ from the Australian perspective. Listen on………
  6. 6. Sector approach to Big Data and Advanced Analytics Lets analyse the differences and the opportunities
  7. 7. Tenure short as disillusionment sets in
  8. 8. Demand does not equal supply
  9. 9. IF YOU HIRE A DATA SCIENTIST, DO YOU KNOW HOW TO USE ONE? It becomes challenging when a so called Head of Analytics does not understand Data Science and cannot even allocate challenging work. Probably more surprising when senior management buy a token data scientist because others have them. Good news for the quick minded to fool recruiters and hiring management. They will not know the difference, and you can ask for more money!
  10. 10. Tools & role of tools for data scientists A FOOL WITH A TOOL IS STILL A FOOL
  11. 11. What Data Scientists do not use? Research shows few, if any, Data Scientists use excel. So if excel is the tool of choice, it could be that person is not a Data Scientist Which leads to another question, do analysts use excel? Is the excel tool just a reporting tool to manipulate integer data as finance teams do?
  12. 12. Fools with tools or tools of trade? Source: O’Reilly data science survey 2015 Analytic Power users The clustering can be considered as Top 3 clusters to approximately “Power Analysts”, so a business user who is able to use tools for analysis but is not a developer. The lower right quadrant corresponds to a developer, an individual with an engineering background able to work actively in hardcore programming languages Hive and business objects fall into a middle category neither tool is accessible to most business users without some significant commitment and training
  13. 13. And so US survey data on job comparisons, luckily not Australian….. Source: Forbes
  14. 14. Typical skills for Data scientists
  15. 15. True data scientist has technical, commercial & problem solving skills Or am I? Similar to a business/data analyst, data scientists combines knowledge of computer science and applications, modelling, statistics, analytics and math to uncover insights in data. Evolving beyond the business/data analyst, the data scientist takes those insights and combines them with strong business acumen and effective communication to change the way an organisation approach challenges. The average day of a data scientist involves extracting data from multiple sources, running it through an analytics platform and then creating visualizations of the data. They will then spend hours cleansing and analysing the data from multiple angles, looking for trends that highlight problems or opportunities. Any insight is communicated to business and IT leaders with recommendations to adapt existing business strategies
  16. 16. Data scientist measurement – what have they achieved? HISTORY EXPERIENCE DIVERSE SKILLS COMMERCIAL EXPEIENCE CURIOSITY & COMMUNICATION
  17. 17. So what does an Actuary actually do? • While data analysts can be found in many types of private and public sector organizations, actuaries work in the insurance industry. • They use similar analytical methods to those used by data analysts, but actuaries' work focuses on the financial losses associated with accidents, illnesses and natural disasters. • They then work with businesses and other clients to develop policies that minimize these risks. • By assessing the costs associated with risks, actuaries help insurance carriers to design coverage and estimate the premiums that should be charged. • They may not have skills in advanced analytic tools, and may specialise in excel.
  18. 18. Data Analyst • Data analysts work for technology firms, health-care organizations, banks, government agencies, educational institutions, consulting firms and other organizations that collect and handle large amounts of data. Analysts also have various titles. • The U.S. Bureau of Labour Statistics classifies many data analysts as statisticians. • They apply mathematical and statistical techniques to extract, analyse and summarise data. • They use spreadsheet and statistical software, work with relational databases and prepare charts and reports of their findings. • Through their work, data analysts transform large, complicated data sets into usable information that informs organizational leadership decisions and policies.
  19. 19. So pulling it all together we have • A data scientist represents an evolution from the business or data analyst role. • The formal training is similar, with a solid foundation typically in computer science and applications, modelling, statistics, analytics and maths. • What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organisation approaches a business challenge. • Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization. • The data scientist role has been described as “part analyst, part artist.” • “A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It's almost like a Renaissance individual who really wants to learn and bring change to an organization."
  20. 20. Conclusions continued • Whereas a traditional data analyst may look only at data from a single source – a CRM system, for example – a data scientist will most likely explore and examine data from multiple disparate sources. • The data scientist will sift through all incoming data with the goal of discovering a previously hidden insight, which in turn can provide a competitive advantage or address a pressing business problem. • A data scientist does not simply collect and report on data, but also looks at it from many angles, determines what it means, then recommends ways to apply the data. • Data scientists are inquisitive: exploring, asking questions, doing “what if” analysis, questioning existing assumptions and processes. • Armed with data and analytical results, a top-tier data scientist will then communicate informed conclusions and recommendations across an organization’s leadership structure.
  21. 21. EMPLOY THE “A” TEAM NO Fools allowed
  22. 22. Presentation by Paul Ormonde-James Analytics specialist & regular global speaker on Big Data, advanced analytics and Business Intelligence. Held senior Analytics & Business Intelligence roles globally, including The World Bank in Washington DC. Board member with Analyst First, Global analytic think tank. Started life with degrees with honours in Cybernetic Engineering (robotics & AI) & computer sciences. MBA in Finance and post grad in Law. Loves his time using R to just dig into data Data is his passion, so loves to have a say. Twitter pormondejames Linkedin

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