Data-X, A framework for digital transformation. Designed to fill a holistic implementation gap for projects that use AI, ML, Blockchain, and Data.

1. Ikhlaq Sidhu
Chief Scientist & Founding Director, Sutardja Center for Entrepreneurship & Technology
IEOR Emerging Area Professor Award, UC Berkeley
About Me:
Data-X: A Framework for Rapid Impact
in Digital Transformation
Data X

2. Ikhlaq Sidhu
Chief Scientist & Founding Director
Sutardja Center for Entrepreneurship & Technology
Industrial Engineering & Operations Research
IEOR Emerging Area Professor Award
UC Berkeley
Ikhlaq Sidhu, UC Berkeley
q Chief Scientist and Founder Sutardja Center
q Professor in IEOR at UC Berkeley
q Created many Berkeley programs
q Developed Data-X
q Advisor to many firms and executives
q Granted over 60 US Patents
q Invented technologies used at Skype, HP, US
Robotics, IBM, and licensed to many others …
q Awarded 3Com’s “Inventor of the Year”
q HP – Laser Printer Design
q Venture Advisor at Onset Ventures, X-Fund
q Numerous Advisory Boards and non-profits
All degrees: Electrical Engineering and
Computer Science (EECS), BS to Ph.D.

3. One of my newest courses at Berkeley:
IEOR 135 Applied Data Science with
Venture Applications
Based on the Data-X Project Framework

4. • Detection of fake news
• Prediction of long-term energy prices
to solve Wall Street problem
• Prediction applications stock market,
sports betting, and more
• AI for crime detection, traffic guidance,
medical diagnostics, etc.
• A version of Zillow that is recalculated
with the effects of AirBnB income
and many more…
IEOR 135 Applied Data Science with Venture Applications
Sample Data-X Projects

5. We are in a new phase of evolution due to data, AI, crypto-
systems, blockchain, algorithms -> Data-X
Drivers of Data-X

6. It is a significant problem to our national agenda if students can’t
participate, build, and harness these types of technologies
New technologies on the horizon * World is changing * Next Industrial Revolution
National and Global SecurityNational Competitiveness
The result of skill and behavior mismatch:

7. Our model has
adapted: Business
training is not the
only key element
I’ve seen many
technical projects with
smart people go off
track
Why we can’t deliver:
• Theoretical understanding without a practical
understanding of implementation
• Narrow focus: silos of disconnected expertise not
leading to any useful work product or innovation
• Over-design: way too complex
• Not even sure what to create. Wanting
implementation specs that no one has.
• Expensive cost over-runs on development, sometimes
even trying to create something that already exists
• Disconnected from technical reality
• People not on the same page (misaligned), cannot
work with each other, team breakdown.

8. Data-X
Framework
Innovation
Leadership
Culture of Innovation:
Behaviors and Mindsets
Story
Adaptation
Ecosystem,
Stakeholders
Operational
& Financial
System
Architecture
Open Source
Tools
Components
Minimal
Implementation
Working
Model
Innovation in
Algorithms
At Berkeley, we have
results:
People in our
programs can build
amazing, working
projects in 3 months
with a relatively little
background in ML, AI,
and other data
technologies.
Applicable to all categories of digital transformation
Students/ technical staff
Leaders/
Entrepreneurs
A Solution for Rapid Implementation

9. DATA-X
PROJECT
EXAMPLES
Deep Dave
David Lin
Sharon Ng
Vanessa Salas
Alexandre Vincent
Airfare Data Scraper
14
Final Product
Safest Path Suggestion
• GREEN: SAFEST PATH
• RED: SHORTEST PATH
Downtown Berkeley to Cal Memorial Stadium
Watch live demo here: https://stayfe.herokuapp.com/
CartilageX:
Automated anomaly
detection in knee MRIs
Iriondo C, Jain D, Muhamedrahimov R, Papanikolaou V, Trotskovsky K, Sun L
Commercialization of RecycleAI
1
Image taken of waste
object and input into
model
2
Model classifies
waste object
Our
Project
3
Object sorted to its
appropriate destination
- Bin Sorter
- Robots
- Conveyor Belts
Prediction of Bitcoin
Prices
Aashray Yadav
Nicolas Sarquis
Bhavya Vashisht
Sai Kannan Sampath
Mubarak Abdul Kader
UC Berkeley | Data-X
Berkeley
Innovation
Index My Dinh
Jessica Gu
Aaron Lu
Dayou Wang
Yan Zeng
Yujun Zou

10. What happens is we don’t teach courses in this manner?
1. Deep technical students learn many disconnected theories and skills,
but they cannot deliver implementations
2. And they work in teams which cannot deliver innovation
within companies, government, and research instiutions

11. Open Source
CS Tools
Math
Quantitative
Data-X
Project: Real
Applications
with Code
Samples
What is in this course
Holistic Perspective: Industry, Social Applications, Customer Driven

12. What is in this class?
Common Open
Source CS Tools:
• Numpy, SciPy
• Pandas
• TensorFlow, Sklearn
• SQL to Pandas
• NLP / NLTK
• Matplotlib
Quantitative
• Prediction: Regression
• ML Classification: Logistic,
SVM.. Trees, Forests,
Bagging, Boosting,..
• Entropy / Information
Topics
• Deep Learning examples,
including CCNs
• Correlations
• Markov Processes
• LTI Systems: Fourier, Filters
where applicable
• Control Models where
applicable
Building Block Code
Samples
• Webscraping
• Stock market live download,
simple trading
• Convolutional Neural
Networks
• Next Word Predictor, Spell
Checking
• Recommendation
• Web Crawler
• Chatbot, E-mail
• Social net interfaces
including twitter
This class will help you combine math and data concepts
The course updates with new tools to stay current. You may learn and use tools not presented in the class project.
Often: Working Code First
Fill In Theory After

13. What is actually in this class?
Common Open
Source CS Tools:
• Numpy, SciPy
• Pandas
• TensorFlow, Sklearn
• SQL to Pandas
• NLP / NLTK
• Matplotlib
Quantitative
• Prediction: Regression
• ML Classification: Logistic,
SVM.. Trees, Forests,
Bagging, Boosting,..
• Entropy / Information
Topics
• Deep Learning examples,
including CCNs
• Correlations
• Markov Processes
• LTI Systems: Fourier, Filters
where applicable
• Control Models where
applicable
Building Block Code
Samples
• Webscraping
• Stock market live download,
simple trading
• Convolutional Neural
Networks
• Next Word Predictor, Spell
Checking
• Recommendation
• Web Crawler
• Chatbot, E-mail
• Social net interfaces
including twitter
Often: Working Code First
Fill In Theory After
• The ML stack use most commonly used in creating ML/AI/Data
applications
• Application and systems viewpoint of data and ML
• Implementation, architecture, and relevant process to build anything
• Statistical, rule based, and hybrid decision systems
• Connection with relevant mathematical foundations (entropy, correlation,
spectral, LTI, basic prediction, classification)
• Practical insight into advanced techniques and tools: (eg. CNNs, NLP,
scraping, recurrent networks, etc.)
• System modeling for data applications

14. Many Course Resources Are Already Available at data-x.blog
For those who want to help students or technical experts learn these skills
We can help in other ways as well

15. OUR APPROACH

16. Make the Tools Use the Tools
(Optimally)
Architect the System Why and how
you build
Most CS Sutardja CenterThis Course
Where we focus:

17. Propose
Low Tech
Solution (1)
Brainstorm
Challenge
and Validate (4)
Demo
or Die
(1)
Execute * Iterate
BMoE Reflections
Agile Sprint (8)
Insightful Story Solution
How the Data-X Course Works:
Team: typically 5 students, with available advisor network

18. The Data-X System View
Web Scrape
Possible Input Code Blocks
Download
Crawl
…
Stream or Poll
Social Net / IoT
Application with Automated
Decisions
Algorithm Options w/ Tables/Matrix
Prediction / Classification
Test, train, split
Keep state
Pandas: Short Term Storage
Long Term Storage: SQL and File
Formats (JSON, CSV, Excel)
Web
Possible Output Code Blocks
Email
Control
Decision
…
Chatbot
Feedback from
External System (World)
Pre-
process
Natural
Language,
State
Features
Blockchain (public ledger or cryptolock)APIs, Services APIs, Services

19. Our model has
adapted: Business
training is not the
only key element
Observation: student projects
and professional projects that
do well require a different
understanding. We created a
model and framework
to provide these:
Data-X Model Layers
1. Tools: using vs making.
Learn to use and understand state of the art tools
and technical approaches
2. Theory:
Understanding the theory and frameworks behind
the tools using first principals
3. Projects:
Story first, second is development agility and
stakeholders acquisition
4. Project Viewpoints:
5 Viewpoints integrated into the teaching model
5. Behaviors and Mindsets:
6 Behaviors and mindsets tuned for innovation

20. Our model has
adapted: Business
training is not the
only key element
Notes: #
* combinations of on-line active
systems, API economy, powerful
open source tools, live systems that
must run and be current all the time,
cloud infrastructure compute and
storage blocks, ..
Data-X Model Layers
5 Project Viewpoints:
a) Customer touchpoints
b) Systems and architecture
c) Risk mitigation,
d) Agile increments,
e) Swim-lanes and team dynamics
6 Necessary Behaviors and Mindsets:
a) The target is moving,
b) Tools are powerful - use them
c) The system is the whole world *
d) There is no greenfield - connect to the existing
structure, means know the existing structure
e) You can’t know it all before you start
f) Develop insight, use technical/theoretical
analogies, first principals, but don’t just plug and
play

21. Project Types
Business or Consumer
Use Case
Social Impact Its Just Cool
(or improve part of a data pipeline
or work towards a research result)

22. End of Section