Facts of Data Generation Evolution Data Science Modern Data Architecture Applications of Data Science

1. Presented By:
Shivumanjesh P

3. Facts on Data Generation..
 Every day 2.5 quintillion bytes of data has been
created
 With so much information at our fingertips, we’re
adding to the data stockpile every time we turn to
our search engines for answers.

4.  Internet - More than 3.7
billion humans use the internet (that’s
a growth rate of 7.5 percent over
2016).
 On average, Google now processes
more than 40,000 searches EVERY
second (3.5 billion searches per day)!

5. Social Media
 Snapchat users share 527,760 photos
 More than 120 professionals join
LinkedIn
 Users watch 4,146,600 YouTube
videos
 456,000 tweets are sent on Twitter
 Instagram users post 46,740 photos
 1.5 billion people are active on
Facebook daily

6. Communication
 We send 16 million text messages
 There are 990,000 Tinder swipes
 15,000 GIFs are sent via Facebook
messenger
 Every minute there are 103,447,520
spam emails sent
 There are 154,200 calls on Skype

7. Services
 The Weather Channel
receives 18,055,556 forecast requests
 Venmo processes $51,892 peer-to-
peer transactions
 Spotify adds 13 new songs on a
average everyday
 Uber riders take 45,788 trips!
 There are 600 new page edits to
Wikipedia

8. Voice Search
 There are 33 million voice-first devices
in circulation
 8 million people use voice control
each month
 Voice search queries in Google for
2016 were up 35 times over 2008

9. Data Science?
 An area that manages, manipulates, extracts,
and interprets knowledge from tremendous
amount of data

10. Evolution of Data Science

11. What makes Data Science different

12. What Data Science is Comprised of

13. Data Science v/s Big Data V/s Data
Analytics

14. Data Science v/s Big Data V/s Data
Analytics Cont..

15. Data Science for the Modern Data
Architecture

16. Some Key Terms in Data Science
 Advanced analytics
 Big data
 Data analysis
 Data analytics
 Data scientist
 Descriptive analytics
 Predictive analytics
 Prescriptive analytics

17. Data Science Process..

18. Common Data Science
techniques One must be aware
of
 Anomaly Detection
 Clustering Analysis
 Association Analysis
 Regression Analysis
 Classification Analysis

19. Steps Involved in Problem
Solving Using Data Science
approach
 Define the problem
 Decide on an approach
 Collect data
 Analyze data
 Interpret results

20. Data Science Solutions for some
common categories of questions.
Questions? Data Science Approach
Which server in my server
farm needs maintenance the
most?
Identifying themes in large
data sets
Is this combination of
purchases different from
what this customer has
ordered in the past?
Identifying anomalies in
large data sets
How likely is this user to
click on my video?
Predicting the likelihood of
something happening
What is the topic of this
online article?
Showing how things are
connected to one another
Is this an image of a cat or a
mouse?
Categorizing individual data
points

21. Applications of Data Science

22. In Retail Industry..

23. In Healthcare domain

24. Medical image analysis

25. Genetics and Genomics

26. Creation of drugs

27. Virtual assistance for patients
and customer support

28. Future of Data Science
And the Refinement Continues……