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How to think like a
Data Scientist?
Hello!
I am Vikrant Narayan
Data Science enthusiast.
You can find me at vikrant.m.narayan@gmail.com
2
BIG DATA
Extremely large data sets that may be
analysed computationally to reveal patterns,
trends, and associations, especially relating
to human behaviour and interactions.
3
Impacts of Big Data
The need for statistical analysis for constant
growth in any industry, has forced companies
to bank on Big Data.
“ “Without big data analytics,
companies are blind and deaf,
wandering out onto the web like deer
on a freeway.”
- Geoffrey Moore
5
Note to aspiring Data Scientists
● Start with something that interests/
bothers you
● Write down all the relevant
definitions and your protocol for
collecting the data.
.
6
Note to aspiring Data Scientists
● Modify your definition and protocol
as you go along.
● Visualize your data.
● Familiarize with visualizing tools
.
7
Note to aspiring Data Scientists
● Understand the context of collected data
● Understanding variation leads to a better
feel for the overall problem, deeper
insights, and novel ideas for improvement
.
8
Note to aspiring Data Scientists
● Ask, “What else does the data reveal?”
● Evaluate your results’ consistency
● Find out how you can use the data
effectively
9
Vikrant data scientist
Note to aspiring Data Scientists
● Start thinking like a Data Scientist
● Try to implement Data science in
everything you do
.
11
Think like a Data Scientist!
Relevance to managers
● Slowly but steadily, data are forcing their
way into every nook and cranny of every
industry, company, and job.
● Companies without a large and growing
cadre of data-savvy managers are
similarly disadvantaged
13
Relevance to managers
● Managers who aren’t data savvy, who
can’t conduct basic analyses, interpret
more complex ones, and interact with
data scientists are already at a
disadvantage.
14
“There were 5 exabytes of information created
between the dawn of civilization through 2003,
but that much information is now created every
2 days.”.
- Eric Schimdt
15
16
Thank You!

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ch8_multiplexing cs553 st07 slide share ss
 

Vikrant data scientist

  • 1. How to think like a Data Scientist?
  • 2. Hello! I am Vikrant Narayan Data Science enthusiast. You can find me at vikrant.m.narayan@gmail.com 2
  • 3. BIG DATA Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions. 3
  • 4. Impacts of Big Data The need for statistical analysis for constant growth in any industry, has forced companies to bank on Big Data.
  • 5. “ “Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.” - Geoffrey Moore 5
  • 6. Note to aspiring Data Scientists ● Start with something that interests/ bothers you ● Write down all the relevant definitions and your protocol for collecting the data. . 6
  • 7. Note to aspiring Data Scientists ● Modify your definition and protocol as you go along. ● Visualize your data. ● Familiarize with visualizing tools . 7
  • 8. Note to aspiring Data Scientists ● Understand the context of collected data ● Understanding variation leads to a better feel for the overall problem, deeper insights, and novel ideas for improvement . 8
  • 9. Note to aspiring Data Scientists ● Ask, “What else does the data reveal?” ● Evaluate your results’ consistency ● Find out how you can use the data effectively 9
  • 11. Note to aspiring Data Scientists ● Start thinking like a Data Scientist ● Try to implement Data science in everything you do . 11
  • 12. Think like a Data Scientist!
  • 13. Relevance to managers ● Slowly but steadily, data are forcing their way into every nook and cranny of every industry, company, and job. ● Companies without a large and growing cadre of data-savvy managers are similarly disadvantaged 13
  • 14. Relevance to managers ● Managers who aren’t data savvy, who can’t conduct basic analyses, interpret more complex ones, and interact with data scientists are already at a disadvantage. 14
  • 15. “There were 5 exabytes of information created between the dawn of civilization through 2003, but that much information is now created every 2 days.”. - Eric Schimdt 15