Amazon's Online Business_Use Case Introduction to Data Science Data Science Sub Domains Data Science Life Cycle Tools/ Libraries & FrameWorks

1. Dr Nisha Arora
Introduction to Data Science
Image: rxlogix.com

2. Contents
 Amazon’s Online Business: A Use –Case
 Introduction to Data Science
 Data Science Sub-Domain
 AI Vs ML Vs Deep Learning Vs Data Science
 Recent Applications
 Associated Risks
 Data Science Life Cycle
 Data Science Skills
 Tools/Libraries & Frameworks
 Resources

3. What Makes Amazon Rich?
Image: Seeking Alpha

5. What’s Their Strategy?

6. Online recommendations:
 Recommended for you, Nisha
 Frequently bought together
 Your recently viewed items and featured
recommendations – inspired by your browsing
history
 Browsing history
 Related to items you’ve viewed
 Customers who bought this item also bought
 There’s newer version of this item
 Best selling in category

7. Offline recommendations/Email-
Marketing:
 Best selling this week: cannon model
 Best selling this week: kodak model
 “Buy a camera package”
 Best selling this week across the entire
product range: no specific brand
Sending the right message to the right person
at the right time.

8. Image: slideshare
https://stackoverflow.com/questions/2323768/how-does-the-
amazon-recommendation-feature-work

9. Mattress industry caught
napping as Amazon makes
stealth move into its market
https://www.nbcnews.com/business
/consumer/mattress-industry-
caught-napping-amazon-makes-
stealth-move-its-market-n929746
https://www.businessinsider.in/Am
azon-is-now-selling-its-own-
mattresses-and-it-shows-why-the-
industry-is-worried-about-the-e-
commerce-giants-
growth/articleshow/66034755.cms
Image: Business Insider

10. An immigrant Indian applied for a salesman's job at London 's premier downtown
department store. The boss got impressed with his confidence and hired him.
The first day was long and arduous for the young man, but he got through it. And
finally 6:00 PM, came around the boss duly fronted up and asked,
"How many sales did you make today?“
"Sir, Just ONE sale." said the young salesman.
"Only one sale?” blurted the boss. "No! No! You see here, most of my staff make
20 or 30 sales a day.”
By the way "How much was the sale worth?”
“93300534.00 pounds” said the young man.
…….
https://www.linkedin.com/pulse/20141120151721-5270504-indian-salesman-
humor/

11. What is Data Science?

12. Data Science Sub Domains

13. 13
DS Vs AI Vs DM Vs Big Data

14. 14
AI Vs ML Vs Deep Learning
 Artificial Intelligence is
 Human intelligence exhibited by machines
 Machine Learning is
 An approach to achieve Artificial Intelligence
 Deep Learning is
 A technique for implementing Machine Learning

15. 15
AI Vs ML Vs Deep Learning
Artificial Intelligence
Data
Science
Machine Learning
Neural Network
Deep Learning

16. AI Vs ML Vs Deep Learning
Image_Edureka

17. 18
Artificial intelligence (AI) is
 An area of computer science
 That emphasizes the creation of intelligent machines
 That work and react like humans.
 AI is the science and engineering of making intelligent machines
that have the ability to achieve goals like humans do.
- John McCarthy (1955)
What is Artificial Intelligence?

18. 19
 Artificial Intelligence is human intelligence exhibited by
Machines.
 Artificial Intelligence is the ability of machines to seemingly
think for themselves.
 The core parts of AI are:
 Machine Learning
 Knowledge Engineering
 Robotics etc
What is Artificial Intelligence?

19. 20 Image_aiie

20. Image: Medium

21. Image: Medium
https://youtu.be/EuxGlRwS2l8?t=1465
My webinar on Machine Learning

22. 23
Machine Learning is the
 Study of algorithms that
 Improve their performance
 At some task
 With experience
 Artificial Neural Networks (ANN) are set of algorithms
inspired by the working of human brain or nervous system.
 ANN finds patterns in the data, calculate the probability of a
certain outcome occurring, and “learn” through error and
successes using a feedback loop
 Deep Learning involves ANN with more than three layers.
Machine Learning & Deep Learning

23. 24
Natural Language Processing (NLP) deals with computer
understanding of human language. NLP can perform tasks like
 Machine translation
 Sentiment Analysis
 Text classification
 Spam detection
 Chatboats
Natural Language Processing

24. 25
Speech Recognition is the process of converting
 Audio and voice into written text
 And vice versa
 Speech recognition’s applications are
 Phone or video call transcription (speech to text)
 Amazon’s polly (text to speech)
 Apple’s Siri, Amazon’s Alexa, Microsoft’s Cortana,
Google’s Google Assistant
 Create speech from brainwaves for those who have
speech disorder
 Video streaming of dream
Speech Recognition

25. 26
Expert system uses
 A knowledge base about its application domain and
 An inference engine to solve problems
 That would normally require human intelligence
 Applications are
 Financial management
 Corporate planning
 Traffic management
 Credit authorization
 Computer installation design
Expert System
https://medium.com/@NaveenkanthArasu/https-medium-com-naveenkantharasu-
understanding-expert-systems-everything-you-need-to-know-a5ec3abafc2

26. 27
AI Planning is the task of determining
 The course of action for a system to reach it’s goal
 In the most optimal way possible
AI Scheduling is the creation of schedules
 Of activities to resources
 Considering necessary goals and constraints
Planning, scheduling and optimization

27. 28
Robotic Process Automation (RPA) is the
 Science of software robots
 That mimic human actions
 Robotics are used in the global manufacturing sector in
assembly, packaging, customer service
 Collaborative robots—or cobots—are robots that are
designed to physically interact with humans in a shared
workspace
 Kismet _ A robot developed by M.I.T.’s Artificial
Intelligence Lab
Sophia
Vicky-small wonders
Robotics

28. 29
Computer Vision is defined as
 As computers obtaining a high-level understanding
 From digital image or videos
 For example; Apple’s Face ID
 Image recognition
 Hand-writing generation
Computer Vision

29. Data Science Skills
Image: ZHAW

30. Data Science Life Cycle
Image: Pinierest

31. Data is like people –
Interrogate it hard enough and it
will tell you whatever you want
to hear
Value Of Asking The Right Question

32. Data Science Tools/Libraries/ Frameworks

34. Julia
Auto ML

36. References
https://towardsdatascience.com/
http://machinelearningmastery.com/
https://www.analyticsvidhya.com/
http://www.analyticbridge.com/
http://www.datasciencecentral.com/
https://www.kaggle.com/
https://www.kdnuggets.com/
https://www.dataquest.io/blog/

37. References
https://community.rstudio.com/
https://www.r-statistics.com/tag/r-community/
https://rweekly.org/
http://stats.stackexchange.com
http://datascience.stackexchange.com/
https://www.researchgate.net
https://www.quora.com
https://github.com/
https://forums.fast.ai/
https://www.deeplearning.ai/forums/

38. For your queries
http://stats.stackexchange.com/users/79100/nisha-arora
https://www.researchgate.net/profile/Nisha_Arora2/contributions
https://www.quora.com/profile/Nisha-Arora-9
http://learnerworld.tumblr.com/

39. Thank You