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Uploaded byMohammad Al Madhani
Mohammed AL Madhani
Setting up a Big Data Team requires best practices including building a team with diverse skills in areas like math, computer science, statistics and domain expertise. Data scientists fulfill key roles like generating prototypes to demonstrate ideas, decomposing problems, and communicating with stakeholders. Effective teams require data preparation, using tools like statistical systems and data management systems. Certification can increase a team's maturity, while frameworks like CRISP-DM and Six Sigma's DMAIC provide processes to optimize the data workflow. Talent management is also important to support the team over time.
Mohammed AL Madhani
- 1. Setting up a BigData TeamBest Practices (*) perspective from Mohammed AL Madhani.
- 2. Sources of thispresentation
- 3. Sources used inthis presentation Building Data Science Teams By Paco Nathan Publisher: O'Reilly Media
- 4. Big Data trendin Google trends
- 5. Bigdata vs IOT 36different types of sensors = 30$ in Amazon.com!!
- 7. Bigdata vs IOTvs data science trends
- 8. The Business Impactsof Data Science
- 9. What is aData Scientist?
- 12. Business Intelligence vsData Science
- 13. Some commonly usedmethods/model Discrete event simulation Queuing model Monte Carlo simulation Agent-based modeling System dynamics Game theory Probabilities Economic analysis, IRR , NPV , FV Linear regression Stepwise regression–method Logistic regression Confidence intervals Hypothesis testing Statistical inferences Design of experiments Analysis of variance Principal component analysis (PCA) Data mining Forecasting Artificial neural networks Fuzzy logic Expert systems Decision trees Markov chain Revenue management (yield management) Optimization Linear programming Integer programming
- 14. DataScientest Data artist ofturning Data into action Skills: 65% 30% 45% 75% Math Computer science Statistics Domain expertise Experience: Doing data representations and Using algorithms for optimization and validation, communicante with the team to make sure data avaiability, rediness, completness, work with data researcher in decomposition the problem. 14 30% Machine Learning
- 15. The Data ScienceVenn Diagram
- 16. How we canreduce the traffic Jam? How we can reduce waiting time for the patient?
- 17. UNLOCKING DATA The Datascientist mission 17 ? Solution Answer ROI Question Data INPUTS OUTPUTS
- 18. Generating the momentum Description: Proofsof concepts can generate the critical momentum needed to jump start any Data Science Capability Problem solving Critical ThinkingBuy-in Necessary Data Clear ROI Dedication & focus Fail often and learn quickly 18 Limited Complexity and Duration
- 19. Dealing with theproblem ( Informs – CAP Approach) 1 2 Decomposition & Datafication
- 20. DESIGN THINKING METHODOLOGY(Alternative Approach)
- 21. BOOZ ALLEN’S DESIGNTHINKING TOOL BOX FOR ANALYTICS
- 22. Big DataResearcher Domain expertwith data science knowledge Skills: 65% 10% Math Computer science Domain expertise 22 70% Communication Statistics 30% 30% Mission: Generates low-fidelity prototypes to demonstrate applicability and test ideas quickly and cheaply before making significant investments
- 23. The Four KeyActivities of a Data Science
- 24. Respondents who saidthere weren’t enough data scientists to go around
- 27. Do Data ScientistsHave What They Need?
- 28.
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- 30. 30 If you haveperfect information or zero information then your task is easy – it is in between those two extremes that the trouble begins “ ”
- 31. Maslow’s of Needcould by applied to Data Optimized Measured Defined managed Performed
- 32. Enhance the datamanagement maturity ( Data Preparation )
- 33. DataManagement Team (Data preparation part ) Ready to Go ! Develop and execute all data flow jobs, business rules, matching, Scrapping, Cleaning, munging, joining and wrangling Responsible about data flow , data solutions and Data Models and architectures Responsible about managing the data elements and data metadata. Running the maturity model components Big Data Steward Big Data Architect Big Data Engineer 33
- 34. SomeCertificats Targeting to Certifyyour team will increase the maturity 1. Certified Analytics Professional (CAP) created in 2013 by the Institute for Operations Research and the Management Sciences (INFORMS) and is targeted towards data scientists. 2. EMC: Data Science Associate (EMCDSA) tests the ability to apply common techniques and tools required for big data analytics. 3. SAS Certified Predictive Modeler : designed for SAS Enterprise Miner users who perform predictive analytics. 34 SAS Certified Predictive Modeler u EMCDSA CAP Certified
- 35. Tools to supportthe bigdata team • • Spreadsheet systems • • Statistical systems • • Optimization systems • • Simulation systems • • Business intelligence systems • • Data management systems • ▪ Structured data • ▪ Unstructured data • • Data integration systems • • Operating systems such as HADOOP
- 36. BOOZ ALLEN TALENTMANAGEMENT MODEL
- 37. BOOZ ALLEN TALENTMANAGEMENT MODEL
- 39. CRISP-DM (cross- industry standard processfor data mining)
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- 41. News:Metis Bootcamp TuitionIncrease • Effective June 20, 2016, the tuition for the Metis Data Science Bootcamp in New York and San Francisco will increase to $15,500. Accepted students who have signed and returned their enrollment agreements on or before June 20, 2016 will receive the current tuition of $14,000. • This is the first tuition increase for Metis, and is the result of continued investments to ensure that our students are best prepared for careers in data science.
Editor's Notes
- #2 Alright thank you very much for having me this afternoon. It’s big pleasure and honor to be here My Name is Mohammed AL Madhani, information management director at Federal demographic council.
- #3 I would like to start telling you about the great sources helping me to do this presentation. 1- a great guide from booz aleen, it’s available free on the internet. 2nd one is building data science capabilities. The third is a report from Crowdflower .
- #4 Also I used the study guide from CAP , an Informs certificate for data scientist. 5- any a video by paco Nathan about building data science team.
- #5 If you do a small search in google trends we will start looking to people interests. I start searching about Big Data So we know that the big date is a big trend,
- #6 Beside the bigdata , I search about IOT, it’s shows that IOT is making a bigger trend which really scare me, cause that’s mean another huge data is coming.
- #8 Organizations want to get all their data, they have a lot of data, they're not doing as much as they could be doing with it? if you wanna do big data, you have to have a data scientist. The second reason they're asking this question, is they wanna know what the skills sit are in data scientist because they're finding it hard to hire them. If you make another search about data science, it’s shows it’s also trending but not as like as IOT. So do you need to hire a PhD in mathematics or statistics?! Or perhaps you can grow a data scientist within your existing organization?
- #9 Studies shows that there is a big improvement in performance, when bigdata and data science being adapted.
- #11 a data scientist is someone who finds new discoveries That’s what a scientists does. they make a hypothesis, And they try to investigate that hypothesis. in case of data scientist, they do it with Data They look for meaning knowledge in the data and they do that in a couple of a different ways
- #12 One, is they visualize the data, they look at the data they create reports and look for patterns in the data that's very similar to what you might, think of as a traditional business intelligence analyst or data analyst. so that's one of the tools that data scientist use.
- #13 The two capabilities are additive and complementary, each offering a necessary view of business operations and the operating environment.
- #14 but what really distinguishes a data scientist is they are using algorithms, Advanced algorithms. That actually run through the data, looking for all this meaning. And you may have heard things like machine learning algorithms or you may have heard algorithm such as neural networks or regression or K-means There’s dozens of these algorithms out there, and essentially they run through the data looking for the meaning That is one of the fundamental tools data scientist
- #15 so to use those algorithms ,the data scientist has to have a strong Foundational knowledge in mathematics and statistics And in some cases computer science and domain knowledge
- #17 that might be a very good question for data scientist to answer and the data scientist would go about that by gathering all the data, running algorithms till they can find some reliable pattern that can answer that question.
- #18 You give the data scientest a data and question and he will start the journey to give you an answer and also a technology that will last supporting the answer.
- #19 In order to generate the momentum to start seriously working in the problem,
- #30 We actually need the data scientist to spend more of their time in representation, optimization and evaluation.