Data science involves using industrial research techniques on a company's own data to develop advanced algorithms that provide a competitive advantage. Data engineering is a specialized form of software engineering focused on handling and processing data using skills in areas like structured and unstructured data storage, machine learning platforms, and predictive APIs. While data science and business intelligence overlap in using data analysis, statistics, and visualization, data science has a more scientific approach focused on the future rather than the past. Data-focused jobs are in high demand across many industries, especially technology, but some roles may become automated, increasing the value of skills like research and communication. Education options for these fields include academic programs, boot camps, and online classes.

1. …in data
new professional careers

2. Who am I?
• David Rostcheck
• I’m a consulting data
scientist
• Follow my articles on
LinkedIn

3. We will talk about 4 things:
Big Data Data
Science
Data
Engineering
Business
Intelligence

4. BIG DATA

5. What is big data?

6. is data that is
so big
that it
requires
specialized
techniques
to handle

7. like: clusters

8. or cloud computing

9. or graph algorithms

10. Data may
   change   
rapidly
so big data may also be fast data

11. big data requires
specialized tools
to handleMAP/RED
UCE

12. big data tools are in demand
but
keep your perspective

13. Big Data tools can be complex
It is often easier to solve problems at small scale, then scale
up, if possible

14. remember:
not all companies use big data
but
all companies use data

15. DATA SCIENCE

16. What is data science?

17. Data science is
industrial research
on a company’s
own data

18. What is its goal?

19. to produce
advanced algorithms
that deliver a
competitive advantage

20. data scientists often work with unstructured data
… which can be large

21. “The qualifications for the job include the
strength to tunnel through mountains of
information and the vision to discern patterns
where others see none”
- Bloomberg Businessweek

22. Is data science really science?

23. let’s compare…
academic science data science
Teams PhDs, graduate
students
PhDs, technologists
Setting University Company
Publication Formal (academic
publications,
conferences)
Less formal (blogs, white
papers, open source)
Funding Public grants Corporate
Goal Advance human
knowledge
Create competitive
advantage

24. Data science is industrial science
It shares some attributes with academic science,
but has other differences

25. What kind of work do data scientists
do?

26. data scientists create artificially intelligent
systems
these are often called “narrow AI”

27. examples
• Recommender systems
• Self-driving cars
• AI agents
• Smart energy management
• Medical diagnosis
• Machine vision

28. DATA ENGINEERING

29. What is data engineering?

30. data engineering is a specialized kind of
software engineering
with additional skills in
handling and processing data

31. data science vs. data engineering
data science data engineering
Approach Scientific (Exploration) Engineering (Development)
Problems Unbounded Bounded
Path to Solution Iterative, exploratory, nonlinear Mostly linear
Education More is better (PhD’s common) BS and/or self-trained
Presentation Skills Important Not as important
Research
experience
Important Not as important
Programming skills Not as important Important
Data skills Important Important

32. What kind of special training does a data
engineer need?

33. Data storage and processing
– structured: (SQL)
– unstructured (NoSQL)
– Big Data (Hadoop, Apache Spark/Storm/Flink, cloud)
Data visualization
Machine Learning algorithms and platforms (ex. Dato)
Predictive APIs (ex. Watson)

34. Does a data engineer need more math
than a regular software engineer?

35. It really helps.
Linear algebra & calculus are important to
understand machine learning

36. BUSINESS INTELLIGENCE

37. Wait – aren’t data science and
business intelligence really the same
thing?

38. Maybe. Let’s compare…
business intelligence (BI) data science
Data analysis Yes Yes
Statistics Yes Yes
Visualization Yes Yes
Data Sources Usually SQL, often Data
Warehouse
Less structured (logs, cloud data,
SQL, noSQL, text)
Tools Statistics, Visualization Statistics, Machine Learning,
Graph Analysis, NLP
Focus Present and past Future
Approach Analytic Scientific
Goal Better strategic decisions Advanced functionality

39. The two fields are closely related.
In some ways data science is an evolution of
business intelligence.

40. which industries most use data-
focused jobs?

41. right now:
Technology
Education
Finance
Consulting
Health Care
( Technology employs over 50% of data workers)

42. but...
“Technology” companies like
Uber, Amazon, AirBnB
compete in other industries
(transportation,
retail,
hotels)

43. “Software is eating the world”
– Andreessen Horowitz

44. which industries will AI change?

45. Ultimately, all of them.
Incorporating AI is a large business opportunity

46. data jobs are in demand
• “The hot job of the decade… Data scientists
today are akin to Wall Street “quants” of
the 1980s and 1990s”
- Harvard Business Review
• “18.7% projected growth 2010-2020”
- VentureBeat
• “McKinsey projects […] ‘50 percent to
60 percent gap between supply and requisite
demand’”
- Bloomberg Businessweek

47. On the other hand…
Some people believe data jobs themselves will be
automated:
“New Teradata Platform Reduces
Demand For Data Scientists”
- Forbes
“Automating the Data Scientist”
- MIT Technology Review

48. What do we think?
• Yes, advanced tools will automate some data
exploration
• But: research and communication are
fundamental skills and are always in demand
when the world is changing
• Data will continue to explode (Internet of Things)
• We will see more change and faster change

49. education for data jobs
options include:
academic programs,
boot camps,
and online classes
(Coursera ,
Udacity)

50. for data engineering:
– documentation and webinars (self-education)
– focus on data manipulation tools and machine
learning

51. for data science:
– The more academic science and research expertise,
the better
– Focus on projects that solve unknown problems
– Work with more experienced data scientists

52. Questions?
Contact: drostcheck@leopardllc.com, twitter: @davidrostcheck
Articles: http://linkedin.com/in/davidrostcheck