A talk delivered as part of Knowledge Leadership Forum (KLF), Sastra University on 23/09/2021

1. DATA SCIENCE FOR
MANUFACTURING AND AUTOMATION
Ravishankar Rajagopalan, Ph.D.
Founder - CourseBricks

2. 2020
CourseBricks
A Data Science Service
and a Machine Vision
Product Company
https://CourseBricks.com
ABOUT ME
2000
B.E. (Mechanical)
SASTRA
Thanjavur
2009
Ph.D. Applied
Statistics &
Optimization
The Ohio State
University
2009 - 2019
10+ years Data
Science Experience in
Industry
GE United Health
Group [24]7.ai
Mu Sigma Petronas

3. COURSEBRICKS – SERVICES + PRODUCT
CourseBricks has teams focusing on servicing clients in Data Science as well as focused
on developing a Machine Vision solution named augurai for Quality Assurance
DATA SCIENCE AS A SERVICE AUGURAI DEVELOPMENT
Supporting clients in Computer Vision,
IoT and Text Analytics
Rapid Proof of Concepts (POCs) to assess
feasibility to production deployment
Healthcare
Technology/Startups
Media/Television
3D Printing
Oil & Gas
Augurai is a state-of-the art end-to-end
Machine Vision solution for Quality
Assurance
It is capable of detecting surface defects
in both external and internal surfaces
Machine + Optics + Computer Vision
software is currently in early stage
development

4. CourseBricks brings the exact combination of skills required
to implement a Machine Vision solution
Computer Vision Deep Learning Robotics
Optics IoT Data Analytics
Basic to Advanced Image and
Video Processing
Developing cutting edge algorithms
on images, text and streams
Building machines integrating
hardware and software
components
Expertise in selecting the right
imaging system
Working with streaming Big Data
for insights and prediction
Generate actionable insights from
unstructured and structured data

5. augurai is being incubated at AIC-
RMP
(AIC) Atal Incubation Centre- Rambhau
Mhalgi Prabodhini (RMP) Foundation has
been set up in alignment with Atal
Innovation Mission (AIM), NITI Aayog to
nurture, handhold and support New Age
Entrepreneurs for New Age India.
augurai has been selected for incubation
after a competitive selection process.

6.  Introduction to Data Science
 Applications of Data Science in the Industry
 Manufacturing - Industry 4.0 and the Buzzwords
 Data Science in Manufacturing – Application Areas and Challenges
 Emerging Applications of Data Science in Manufacturing and
Automation
 Data Scientist – Skills Required
 Data Science Interview Preparation
 Data Science Resources

7. Data Science in Day-to-Day Life
Amazon, Netflix, Flipkart
(Recommender Systems)
Digital Advertising
Alexa/Siri
(Speech Recognition)
Uber/Ola Routing
(Optimization)

8. What is Data Science?
Algorithms
Technology
Data
Data Science is the Art and Science of using Algorithms on Data to
generate actionable insights, make predictions and prescribe
actions
Descriptive Predictive Prescriptive
Generate Insights Predict Future Prescribe Optimal Actions

9. Where is Data Science Applied?
Risk Models, Fraud Detection, Algorithmic
Trading
Personalization, Dynamic Pricing,
Recommender Systems
Market Basket Analysis, Price
Optimization, Inventory Management,
Store Location Optimization
Bid Pricing, Customer Segmentation,
Attribution, Fraud Detection
Medical Imaging, Drug Discovery, Disease
Prevention
IoT, Predictive Maintenance, Demand
Forecasting, Inventory Management,
Warranty Analysis
Dynamic Pricing, Demand Forecasting,
Personalized Recommendations, Trip
Planning
Customer Segmentation, Market Mix
Models, Campaign Optimization, Lead
Scoring
BANKING AND FINANCE
eCOMMERCE
RETAIL
ADVERTISING
HEALTHCARE
MANUFACTURING
TRAVEL
MARKETING

10. Manufacturing – The buzzwords
Cyber Physical Systems
Internet of Things

11. Industry 4.0 – A timeline
https://www.spectralengines.com/articles/industry-4-0-and-how-smart-sensors-make-the-difference

12. Manufacturing – Where are they being applied?
Maintenance and Quality are the two areas where Data Science is being mostly
applied

13. Manufacturing – Hurdles for DS Implementation
https://www.pwc.com/gx/en/industrial-manufacturing/pdf/intro-implementing-ai-manufacturing.pdf
 Legacy Systems
 Data in Silos
 Integration with ERP systems
 Lack of Manufacturing + DS Skills
 Cultural Challenges

14. Manufacturing – India is way behind the curve
India has the most potential to apply Data Science in Manufacturing

15. Ola’s Two Wheeler Factory

16. Manufacturing – A Broader View
Automotive
Logistics
Cement Oil & Gas
3D Printing
Pharma
Textile Food Processing
Chemical

17. Real Time Monitoring of 3D Printing

18. Digital Twin

19. Textile Defect Detection

20. Drone based Warehouse Inventory
Tracking

21. Stockpile Inventory Tracking using Drones

22. Machine Vision for Inspection

23. Drone for Enclosed Space Inspection

24. Predictive Maintenance

25. Data Scientists need to acquire Zillion Skills!!
Technology
Programming/Databases/Big
Data/Visualization
Science
Statistics/Machine
Learning/Deep Learning etc
Soft Skills
Story Telling/Influencing
Domain Knowledge
Finance/Healthcare/Manufacturing

26. Core Tech and Science Skills for Data Scientists
TECHNOLOGY + SCIENCE
Programming
R/Python/Java/
Scala/Julia
Big Data
Databases (SQL)
Hadoop Spark
Visualization
Tableau
R/Python
Fundamental
Probability/ Statistics
Machine Learning
Unstructured Data
Deep Learning
Natural Language Processing
Image Processing
Video analytics
Speech Recognition
Prescriptive
Optimization
Simulation

27. Data Science Lifecycle
Deploy the model for integration
with a product
Model Deployment
Extract and curate data from
various sources
Data Extraction
Validate the model with real world
data
Model Validation
Extract and curate data from various
sources (visualization driven)
Exploratory Data Analysis
Build Machine Learning/Deep Learning
Models
Model Building
Create enriched features from existing
columns
Feature Engineering
Monitor the model performance to
know when to retrain the model
Model Performance Monitoring
Work with the business to identify the pain
point and identify an appropriate solution
Problem Definition

28. Data Science is a Team Work
Architecture
Project Management
QA
Engineering (Product Development)
Data Science
Data Engineering
Business Stakeholders
Product Management

29. Learn Soft Skills to navigate Data Science Career
01 Science
02 Technology
03 Business/Domain Knowledge
Understand data from a business context
04 Problem Solving
Ability to think on the feat and come up with solutions
05 Critical Thinking
Ability to ask the right questions
06 Story Telling
Visualization and Presentation
07 Team Work
Ability to work as part of cross functional teams
08 Emotional Intelligence
Ability to handle stressful situations
09 Stakeholder Management
Influencing skills when dealing with upper management
10 Networking
Networking with professionals inside/outside the organization

30. Data Science Career Ladder
Technical skills are sufficient to get started as a Data Scientist but Soft Skills takes a
precedence as one grows towards an Expert Data Scientist
Beginner Data Scientist
Ability to use Data
Science tools to extract,
explore and build basic
ML Models with
supervision
Intermediate Data
Scientist
Ability to use ML
Algorithms to solve
specific business
problems (without
supervision)
Advanced Data Scientist
Ability to choose
appropriate models,
modify them as
required to suit the
business problem and
defend the choice
Expert Data Scientist
Invent new algorithms
as required and be a
thought leader in
driving company
level initiatives

31. Data Science Interviews
Probability & Statistics
SQL
Data Structures and
Algorithms
Machine Learning Deep Learning
Problem Solving
Data Science interviews focus on six major areas.

32. Data Science Interviews – Do’s and Dont’s
Do’s Dont’s
Company Research
Research about the
company, team and their work in
Data Science
Interviewer Profile
Get to know the interviewer
profile in advance (LinkedIn)
Ask Questions
Express interest in the position
by asking relevant questions
Failure to Focus on Fundamentals
Learning advanced topics
without learning the
fundamentals
Too focused on Packages
Not knowing what goes behind
the commands Python
Giving up on a problem
Several problems are open
ended and have multiple
answers

33. How do you learn and Practice Data Science?
Learn Practice Apply
Coursera
 Udacity
 Edx
 NPTEL
 Data Camp
 Khan Academy
 Udemy
 PluralSight
Kaggle
Driven Data
Crowd AI
TianChi
Analytics Vidhya
Data Science Society
Hackathons
Internships
Contribution
to Open Source

34. Practice Data Structures/Algorithms/SQL?
Programming Algorithms SQL
HackerRank
 LeetCode
 HackerEarth
 TopCoder
 CoderByte
 HackerTrail
 CodeChef
 InterviewBit
 TestDome
•Learn
Mode Analytics
CodeAcademy
LearnSQL.com
SQLZoo
SQLBolt
•Practice
HackerRank
SQLFiddle
LearnSQL.com

35. QUESTIONS ?

36. CONTACT US
https://CourseBricks.com + 91 96633 97694
ravishankar@coursebricks.com