mLamda proposes addressing the growing global shortage of data science skills by developing an educational platform to train existing employees within companies. There is a projected gap of over 200,000 data science jobs by 2018. mLamda's platform would provide literacy, fluency and mastery levels of data science training tailored for different learner types. This could generate $11 million annually by capturing just 1% of the total addressable market for companies to invest in employee education. To validate and build the initial business model and prototype, mLamda requests $100,000 over three one-week sprints of work.

1. MLAMBA
GoKart 600

2. mLamda

3. Demand for data scientists is definitely
outstripping supply.
—Andrew Jennings, Chief Analytics Officer, FICO

4. Data science is an interdisciplinary field about
scientific processes and systems to extract
knowledge or insights from data in various
forms.
—Wikipedia

5. Data
Science
Domain Expertise
Mathematics
Computer
Science
Data
Processing
Statistical
Research
Machine
Learning

6. REPORTING OPTIMIZATION OPTIMIZATIONINSIGHT GENERATION INSIGHT COMMUNICATION
WEATHER PREDICTION TALENT ACQUISITIONBIG DATA PREDICTIVE MODELING
FRAUD DETECTION INVESTMENT IDENTIFICATIONANALYTICS IMPLEMENTATION
DATA SCIENCE PROGRAMMING OPTIMIZATIONR, SAS, PYTHON PROGRAMING
RISK ASSESSMENTDATA VISUALIZATION REVENUE MODELINGCLOUD COMPUTING
RECOMMENDATION SYSTEM DATA CLEANINGSTATISTICAL RESEARCH ALGORITHM CREATION
DATA INFRASTRUCTURECONSUMER RESEARCH LEAD ACQUISITION SOCIAL LISTENING

7. DATA RESEARCHERS DATA ENGINEERS
BUSINESS DATA ANALYSTS
DATA ANALYSTS
DIRECTOR OF ANALYTICS DATA CLEANERSTATISTICIANS
DATA DEVELOPERS
DATA CREATIVES
DATA RESEARCHERS
DATA SCIENTISTS

8. “The sexy job in the next 10 years will be
statisticians. People think I’m joking, but
who would’ve guessed that computer
engineers would’ve been the sexiest job
of the 1990s?
—Hal Varian, Chief Economist at Google

9. Big data is not inherently organized or well-
generated. It’s certainly not neatly structured in
a database. It’s messy. It’s everywhere. It’s a
morass of details of what we buy, where we
drive, what we surf online, what we “like”.
—David Williams, CEO, Deloitte Financial Advisory Services LLP

10. Problem Statement

11. By 2018 There will be a Data Science
workforce gap of 200,000+

12. If your company is looking to hire data
scientist right now, good luck.
—Datanami, March 2016

13. mLamda will work with and enable
companies to educate and further
develop their employees to meet the
data science gap.

14. 43% of companies report that lacking appropriate
analytical skills is a key challenge

15. Of those organizations, 20% have changed their
approach to attracting and retaining analytics talent.

16. Demand

17. As a result of the scarcity of data scientists, 63
percent of the companies who want to grow their
data science proficiency are providing formal or
on-the-job training in-house.

18. “One big plus of developing analytical skills
among current employees, is that they
already know the business.”

19. Progressive companies are also doing
more to train existing managers to
become more analytical and new data
scientists to better understand their
business,

20. Turning analytical insights
into business actions is the
top analytics challenge

21. The “training” landscape for data science
learning is inconsistent, disparate, and
does not differentiate amongst different
“student types”.

22. Literacy Fluency Mastery

23. Literacy Fluency Mastery
Understanding the
possible, of data
science.

24. Fluency Mastery
Ability to apply
skills to context.

25. Mastery
Complete understanding
of operation and
execution of data
science, so that others
can be taught.

26. Literacy Fluency Mastery
Teach

27. Domain knowledge is more
valuable in recruiting and
educating data scientists than
engineering and quantitative skills.
— Mike Ross, Sr. Director of Data science at Capital One.

28. We believe the best way to train the
rising number of junior data scientists
is through the transfer of domain
knowledge and a progressive
education platform.

29. Assumptions

30. It’s easier to educate a willing
and existing workforce than
to hire new.
Demystifying data science
by bringing it out of the
black box and making it
available to all.

31. We need to educate the world that the
practice of data science is more accessible to
people who want to learn.

32. Infusing Data Science and educating employees
IS the playbook for not falling behind in the future.
We assuage Marketplace and Executive FOMO on
the data science and machine learning surge.

33. Opportunity

34. Training
• Companies with 500+ employees: Average spend
of $1,200 per employee per year

35. Tuition Reimbursement
• The average maximum reimbursement amount
is $4,591 per employee per year

36. Combined Average Education Spend
• 500+ $5,791 per employee per year

37. Total Addressable Market (New Jobs)
Assumptions
• 200,000+ jobs gap by 2018
• Companies with 500+ employees spend an average of
$5,791 annually on employee training & tuition
reimbursement
• We capture 1% of the total addressable market
Calculations
• 1% of 200000 = 2000
• 2000 * $5791 = $11,582,000 annually
• ½ of that $11M = $5,791,000 (if organizations only
spend half of the average amount on employee
education)
Annual Revenue
• $11M
• Achievable by end of year 2
• Potentially undervaluing the
market; could be more.

38. Ask

39. The cost for GoKart to create an industry-changing
business that will solidify the future of Data Science
education while addressing a major global skills gap is:

40. Three 1-Week Sprints
400 Hours
$100,000
• Marketing research and opportunity validation
• Build and validate the financial model
• Curriculum architecture and design roadmap
• Prototype build
• Prototype testing with potential users
• Prototype testing with potential buyers
• Technology roadmap and costing
The cost for GoKart to create an industry-changing
business that will solidify the future of Data Science
education while addressing a major global skills gap is:

41. Plus the $150 we did not spend today ;)

43. Thank you