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.

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. 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
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
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
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
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
Sample

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
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
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
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
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
The Structure of Data Science Skills

14. 14
Proficiency in general skill areas varies by job role

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
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
Top Data Science Skills by Job Role

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
In Search of the Data Scientist Unicorn

20. 20
Data Science as a Team Sport
Impact of Business Expert

21. 21
Data Science as a Team Sport
Impact of Technology / Programming Expert

22. 22
Data Science as a Team Sport
Impact of Math & Modeling / Statistics Expert

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
Scientific Method and Data Science Skills

25. 25
What skills are linked to project success?

26. 26
Importance of Data Science Skills by Job Role

27. 27
Education and Data Science Skills

28. 28
Lack of Gender Diversity

29. 29
Lack of Gender Diversity – Other Science Roles

30. 30
Job Roles in Data Science by Gender

31. 31
Highest Level of Education Attained

32. 32
Gender Comparison of Proficiency across Skills

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