Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

Smart Data Slides: Data Science and Business Analysis - A Look at Best Practices for Roles, Skills, and Processes

1.816 visualizaciones

Publicado el

Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights. We definitely produce more of the former than the latter, which has created demand for more powerful tools to simplify the process and lower the skills requirement for analysis. As vendors build systems to meet this demand, we hear about the coming ”democratization” of big data as more people at varying levels within organizations are empowered to find meaning and improve their own performance with data-driven insights. This is a good thing, but it does require caution.

To paraphrase Col Jessup in A Few Good Men: You want answers? You can’t handle the data.

In this webinar, we will survey emerging approaches to simplifying analysis, and discuss the benefits, dangers, and skills required for individuals and organizations to thrive in the brave new world of analytics everywhere, for everyone.

Publicado en: Empresariales
  • DOWNLOAD FULL BOOKS, INTO AVAILABLE FORMAT ......................................................................................................................... ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y3nhqquc } ......................................................................................................................... ......................................................................................................................... ......................................................................................................................... .............. Browse by Genre Available eBooks ......................................................................................................................... Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult,
       Responder 
    ¿Estás seguro?    No
    Tu mensaje aparecerá aquí
  • DOWNLOAD FULL BOOKS, INTO AVAILABLE FORMAT ......................................................................................................................... ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. PDF EBOOK here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. EPUB Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... 1.DOWNLOAD FULL. doc Ebook here { https://tinyurl.com/y8nn3gmc } ......................................................................................................................... ......................................................................................................................... ......................................................................................................................... .............. Browse by Genre Available eBooks ......................................................................................................................... Art, Biography, Business, Chick Lit, Children's, Christian, Classics, Comics, Contemporary, Cookbooks, Crime, Ebooks, Fantasy, Fiction, Graphic Novels, Historical Fiction, History, Horror, Humor And Comedy, Manga, Memoir, Music, Mystery, Non Fiction, Paranormal, Philosophy, Poetry, Psychology, Religion, Romance, Science, Science Fiction, Self Help, Suspense, Spirituality, Sports, Thriller, Travel, Young Adult,
       Responder 
    ¿Estás seguro?    No
    Tu mensaje aparecerá aquí
  • I went from getting $3 surveys to $500 surveys every day!! learn more...  https://tinyurl.com/make2793amonth
       Responder 
    ¿Estás seguro?    No
    Tu mensaje aparecerá aquí
  • And if you've ever taken a girl home, gotten hot and heavy and then felt embarrassment and PANIC when you take off your pants and see the look of DISAPPOINTMENT on her face, you need to go check this out right now. ■■■ https://tinyurl.com/getpebible2019
       Responder 
    ¿Estás seguro?    No
    Tu mensaje aparecerá aquí
  • Proof Of REAL Growth. Women Lie: Size DOES Matter, They HATE It When It's Small. Don't Disapoint Her With Your Little Guy. ▲▲▲ https://tinyurl.com/yaygh4xh
       Responder 
    ¿Estás seguro?    No
    Tu mensaje aparecerá aquí

Smart Data Slides: Data Science and Business Analysis - A Look at Best Practices for Roles, Skills, and Processes

  1. 1. Data Science and Business Analysis: A Look at Best Practices for Roles, Skills, and Processes Bob. E. Hayes, PhD bob@appuri.com @bobehayes
  2. 2. Bob E. Hayes, PhD Chief Research Officer Email: bob@appuri.com Web: www.appuri.com Twitter: @bobehayes • Author of three books on customer experience management and analytics • PhD in industrial-organizational psychology • #1 blogger overall on CustomerThink (http://customerthink.com/author/bobehayes/) • #1 blogger on the topic of customer analytics (http://customerthink.com/top-authors-category/) • Top expert in Big Data and Data Science • https://www.maptive.com/the-top-100-big-data-experts/ • http://www.kdnuggets.com/2015/02/top-big-data- influencers-brands.html
  3. 3. 3 What is Data Science? Data science is way of extracting insights from data using the powers of computer science and statistics applied to data from a specific field of study Involves the collection, analysis and interpretation of data to extract empirically-based insights that augment and enhance human decisions and algorithms
  4. 4. 4 Data Science Study Invited data professionals via: • AnalyticsWeek Newsletter • Blog post • Social media (Twitter, LinkedIn, Google+) 600+ completed surveys • Self-assessment rating of proficiency of 25 skills across five skill areas: • Business, Technology, Programming, Math & Modeling, Statistics • 9 additional questions • Overall satisfaction with outcome of analytics projects
  5. 5. 5 Data Science Skills Assessed Area Skills* Business 1. Product design and development 2. Project management 3. Business development 4. Budgeting 5. Governance & Compliance (e.g., security) Technology 6. Managing unstructured data (e.g., noSQL) 7. Managing structured data (e.g., SQL, JSON, XML) 8. Natural Language Processing (NLP) and text mining 9. Machine Learning (e.g., decision trees, neural nets, Support Vector Machine, clustering) 10. Big and Distributed Data (e.g., Hadoop, Map/Reduce, Spark) Math & Modeling 11. Optimization (e.g., linear, integer, convex, global) 12. Math (e.g., linear algebra, real analysis, calculus) 13. Graphical Models (e.g., social networks) 14. Algorithms (e.g., computational complexity, Computer Science theory) and Simulations (e.g., discrete, agent-based, continuous) 15. Bayesian Statistics (e.g., Markov Chain Monte Carlo) Programming 16. Systems Administration (e.g., UNIX) and Design 17. Database Administration (MySQL, NOSQL) 18. Cloud Management 19. Back-End Programming (e.g., JAVA/Rails/Objective C) 20. Front-End Programming (e.g., JavaScript, HTML, CSS) Statistics 21. Data Management (e.g., recoding, de-duplicating, Integrating disparate data sources, Web scraping) 22. Data Mining (e.g. R, Python, SPSS, SAS) and Visualization (e.g., graphics, mapping, web-based data visualization) tools 23. Statistics and statistical modeling (e.g., general linear model, ANOVA, MANOVA, Spatio-temporal, Geographical Information System (GIS)) 24. Science/Scientific Method (e.g., experimental design, research design) 25. Communication (e.g., sharing results, writing/publishing, presentations, blogging) * List of skills adapted from Analyzing the Analyzers by Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman
  6. 6. 6 Proficiency Ratings* Proficiency Level Scale Value Description Don't know 0 You possess no knowledge Fundamental Awareness 20 You have a common knowledge or an understanding of basic techniques and concepts. Novice 40 You have the level of experience gained in a classroom and/or experimental scenarios or as a trainee on-the-job. You are expected to need help when performing this skill. Intermediate 60 You are able to successfully complete tasks in this competency as requested. Help from an expert may be required from time to time, but you can usually perform the skill independently. Advanced 80 You can perform the actions associated with this skill without assistance. You are certainly recognized within your immediate organization as "a person to ask" when difficult questions arise regarding this skill. Expert 100 You are known as an expert in this area. You can provide guidance, troubleshoot and answer questions related to this area of expertise and the field where the skill is used. * Rating scale is based on a proficiency rating scale used by NIH. Respondent instructions: You will be asked about your proficiency for a variety of skills. Please use the following scale when indicating your level of proficiency for each skill.
  7. 7. 7 Sample
  8. 8. 8 Proficiency varies across skills Top 10 Data Science Skills 1. Communication 2. Managing structured data 3. Data mining and visualization tools 4. Science / Scientific method 5. Math 6. Project management 7. Data management 8. Statistics and statistical modeling 9. Product design and development 10. Business development
  9. 9. 9 Job Roles in Data Science *Researcher (e.g., researcher, scientist, statistician); Business Management (e.g., leader, business person, entrepreneur); Creative (e.g., jack of all trades, artist, hacker); Developer (e.g., developer, engineer)
  10. 10. 10 Proficiency in 25 skills varies by job role • Different types of data scientists possess different skills • Biz Management – strong in business skills • Developer – strong in technology/programming skills • Researcher – strong in math/ statistics skills • Creatives – average in all skills
  11. 11. 11 Structure of Data Science Skills * Factor analysis is based on proficiency ratings of 621 data professionals. Reliability (Cronbach’s alpha for each of the three Skills areas (based on items that loaded on the respective factors) were: .87 (Business); .92 (Tech / Prog); .92 (Math / Stats) Factor Analysis of Data Skills • Data reduction technique • Examines the statistical relationships (e.g., correlations) among a large set of variables and tries to explain these correlations using a smaller number of variables (factors) • Elements (or factor loadings) of the factor pattern matrix represent the strength of relationship between the variables and each of the underlying factors • Tells us two things: 1. number of underlying factors that describe the initial set of variables 2. which variables are best represented by each factor
  12. 12. 12 Structure of Data Science Skills * Factor analysis is based on proficiency ratings of 621 data professionals. Reliability (Cronbach’s alpha for each of the three Skills areas (based on items that loaded on the respective factors) were: .87 (Business); .92 (Tech / Prog); .92 (Math / Stats) Plot the factor loadings for the 25 data skills into a 3-dimensional space Three Distinct Skill Sets • Business • Technology / Programming • Math / Statistics
  13. 13. 13 The Structure of Data Science Skills
  14. 14. 14 Proficiency in general skill areas varies by job role
  15. 15. 15 Business Skills: Proficiency varies by job role *Researcher (e.g., researcher, scientist, statistician) n = 133; Business Management (e.g., leader, business person, entrepreneur) n = 86; Creative (e.g., jack of all trades, artist, hacker) n = 30; Developer (e.g., developer, engineer) n = 54
  16. 16. 16 Technology and Math/Statistics Skills: Proficiency varies by job role *Researcher (e.g., researcher, scientist, statistician) n = 133; Business Management (e.g., leader, business person, entrepreneur) n = 86; Creative (e.g., jack of all trades, artist, hacker) n = 30; Developer (e.g., developer, engineer) n = 54
  17. 17. 17 Top Data Science Skills by Job Role
  18. 18. 18 Satisfaction with Work Outcome *Researcher (e.g., researcher, scientist, statistician); Business Management (e.g., leader, business person, entrepreneur); Creative (e.g., jack of all trades, artist, hacker); Developer (e.g., developer, engineer)
  19. 19. 19 In Search of the Data Scientist Unicorn
  20. 20. 20 Data Science as a Team Sport Impact of Business Expert
  21. 21. 21 Data Science as a Team Sport Impact of Technology / Programming Expert
  22. 22. 22 Data Science as a Team Sport Impact of Math & Modeling / Statistics Expert
  23. 23. 23 Getting Insight from Data: The Scientific Method 1. Formulate Questions 2. Generate hypothesis/ hunch 3. Gather / Generate data 4. Analyze data / Test hypothesis 5. Take action / Communicate results • Start with a problem statement. • What are your hunches / hypotheses? • Be sure your hypotheses are testable. • You can use experimental or observational approach to analyzing data. • Integrate your data silos to ask bigger questions; connect the dots and get a 360 degree view of your customers. • Employ Predictive analytics / Inferential statistics to test hypotheses • Employ machine learning to quickly surface insights • Implement your findings • Use Prescriptive analytics to guide course of action
  24. 24. 24 Scientific Method and Data Science Skills
  25. 25. 25 What skills are linked to project success?
  26. 26. 26 Importance of Data Science Skills by Job Role
  27. 27. 27 Education and Data Science Skills
  28. 28. 28 Lack of Gender Diversity
  29. 29. 29 Lack of Gender Diversity – Other Science Roles
  30. 30. 30 Job Roles in Data Science by Gender
  31. 31. 31 Highest Level of Education Attained
  32. 32. 32 Gender Comparison of Proficiency across Skills
  33. 33. 33 Advice for Data Scientists • Be specific when talking about “data scientists” • There are different types – defined by what they do and the skills they possess • Work with other data professionals who have complementary skills. Teamwork is key to successful data science projects. • Learn to use data mining and visualization tools • R, Python, SPSS, SAS, graphics, mapping, web-based data visualization • Be an advocate for women in the field of data science

×