This Presentation is brief Introduction to Data Analytics and carrier in it. It is with respect to the webinar which took place on 6 th March - Link https://www.youtube.com/watch?v=ltPi1680d1s

1. DATA SCIENCE
INTRODUCTION TO

2. ABOUT ME
I AM ANIRUDDHA KALBANDE
AN ELECTRONICS & TELECOMMUNICATION ENGINEER
YOU CAN REACH ME OUT ON GITHUB.COM/ANIRUDDHA1995/
EMAIL - AKALBANDE6@GMAIL.COM

3. ANALYTICS IN USE
Etihad Airways uses technology to harvest and analyse gigabytes of
data generated by hundreds of sensors working inside its plane .
This allows to monitor planes in real time , reduce fuel cost, manage
plane maintenance, even spot problems before they happen.
Many people use Facebook to update their status, share
photos , and ‘like’ content .The Obama presidential
campaign used all that data on the social network to not just
find votes but to assemble an army of volunteers.
One of the India’s highest - rated TV shows Aggregates and
analyses the millions of messages it receives from viewers on
controversial issues like female fetiches , caste discrimination and
child abuse - and used that data to push for political change.

4. ANALYTICS IN USE
Recommendation systems used by Amazon,
Flipkart, Ebay ,Netflix.
Image recognition by Facebook for Tagging images.
Digital Advertisements
Speech Recognition by Google voice, Siri, Cortana.
Self Driving Cars
Fraud and Risk Detection
Healthcare

5. WHY ARE WE TALKING
ABOUT DATA SCIENCE?

24. CONCLUSION
Data is being created at great velocity
and that too in most unstructured and
unorganised manner .

25. AND ..

34. SKILLS REQUIRED
Problem solving skills
Basic Programming Python
Statistics Distributions (Binomial ,Normal,Chi
Squared Distribution,Exponential Distribution,etc)
Machine learning Concepts
Python Directories such as Numpy ,Pandas,
ScikitLearn , Matplotlib ,Etc)

35. DATA ANALYTICS LIFE CYCLE

36. ANALYTICS
USING DATA TO DISCOVER SOME PATTERN .
GENERATE INSIGHTS AND VISUALS FROM THE PATTERNS .

37. APPROACH TO ANALYTICS DEVELOPMENT
Identify the data source or gather data.
Select the right tools and technology
Understand the business domain
Process the data or data Cleaning
Build mathematical model for analysis
Visualise
Learn , adopt , rebuild your analytics model

38. TOOLS FOR ANALYSIS
SAS ( Statistical Analysis Software)
Python - ( Anaconda Data Science platform )
R - (RStudio)
Matlab
Tableau ,TensorFlow ,IBM Watson ,Cognos
are the other extensions for Machine
Learning and Analysis.

40. FOR MORE UPDATES AND FUTURE SESSIONS
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