The talk is on How to become a data scientist. This was at 2ns Annual event of Pune Developer's Community. It focuses on Skill Set required to become data scientist. And also based on who you are what you can be.

1. How to become a Data
Scientist?
Manjunath Sindagi
Pune Developer’s Community Annual Event
20.01.2018

2. Co-Founder @Hyperdata.io
and
Data Science Advisor

3. Focus on:
Apply AI to Business Problems.

4. Agenda
●
● Why Data Scientist?
● Artificial Intelligence
● Being a Data Scientist
● Broad Skill Sets
● Skill Sets based on your Profile
● Typical Team in Data Science
● Conclusion & Final Remarks
● References to start

5. Why data scientist?

6. Why data
scientist?
● Buzzword
● Fancy, Cool
● Demand
● Data Growth
● Passion

7. No. 1 Job in Glassdoor for 2016 and 2017

8. Average Salary is Higher

9. Growth of Data Scientist - IBM

10. Any industry that has digitized data, people are
needed to support the ecosystem and find
insights from the data.

11. Challenges of
Data
4 Vs of Data
➢ Volume
➢ Velocity
➢ Variety
➢ Veracity

12. Data Science and Analytics (DSA) jobs remain
open an average of 45 days, five days longer
than the market average.

13. Big Data and Data Science Skills are
most challenging to recruit for and
potentially can create the greatest
disruption.

14. Some 59% of all DSA job demand
currently is in finance and insurance,
professional services and IT sector.
(according to IBM)

15. Job Trends from Indeed.com

16. Google Trends

17. Artificial Intelligence

18. Artificial
Intelligence
Artificial Intelligence is defined as
the science of making computers
do things that require intelligence
when done by Humans
Making sense out of data .

19. AI And Related Fields

20. Being a Data Scientist

21. Who is Data Scientist?

22. Who is Data
Scientist?
One who has wide breadth of
abilities:
● Academic curiosity
● Storytelling
● Product sense
● Engineering experience
● Cleverness
And above all
● deep domain expertise in
Mathematics, Statistical and
Machine Learning

23. T - Shaped Skill Set

24. What is Data Science?
Data science, also known as data-driven science, is an
interdisciplinary field about scientific methods, processes,
and systems to extract knowledge or insights from data in
various forms, either structured or unstructured, similar to
data mining.
- Wikipedia : https://en.wikipedia.org/wiki/Data_science

25. Data Science
Interdisciplinary Field

26. Being a Data Scientist is Jack of
All, Master of Everything!

27. Broad Skills -
Knowledge Prerequisites

28. Programming
● Strong Programming Skills
● Strong with Python, R or Java
● Fundamentally Strong Data
Structure Knowledge
● Debugging Skills
● Exceptional Problem Solving
Skills

29. Mathematics
● Algebra
● Statistics
● Differentiation
● Calculus

30. Core Areas
Pick One
● Information Retrieval
● Natural Language Processing
● Linguistics
● Machine Learning
● Image Processing
● Video Processing
● Speech Processing
● Then pick Neural Networks and
Deep Learning

31. Tools
and
Technology
If not all at least 3-4
● Excel
● R, Python
● Spark
● Hadoop
● Scala
● AWS
● Solr, Elastic Search
● New ML Libraries - Tensorflow,
Caffe
● Queueing System

32. Top Tools used in 2015-17 : Kdnuggets

33. Application of
Algorithms
● Practical Implementations
● Follow Kaggle, KDNuggets and
solve problems
● Ability to quickly suggest
algorithms to apply and also to
implement the same
● Working with a Mentor will help

34. Data Savvy
● Data Oriented Mindset
● Quickly understand the
problem and give solutions in
short span of time
● Ability to think how data can
add value to business and what
insights can be driven.

35. As a data scientist, if you know nothing
else, you need to know how to take
some data, munge it, clean it, filter it ,
mine it, visualize it and then validate.
It’s a very long process

36. Learning
PathWays
● MS/MTech/PhD
● Self Study
● Boot Camps
● Online Courses

37. Skill Set based on Current Profile

38. Skill Sets Focus
● Exceptionally Strong
Programming Skills
● Strong Data Structure
Knowledge
● Master Python, R, Java
● Github Profile
● Then, Work in
Companies to Solve
Problems
Freshers

39. Skill Sets Focus
● Mathematics
● Course - Take Up a
Course Online.
● Pick up a Area - ML, NLP,
Linguistics etc
● Apply and Solve
Problems in Kaggle
Programmers
(> 2 Years Experience)

40. Skill Sets Focus
● Strong AWS Knowledge
● Knowledge of ML/DL
Libraries and Tools
● Photographer’s Mind
● Be a Data Engineer than a
Scientist
● Practice, Practice,
Practice
Programmers
(> 2 Years Experience)

41. Skill Set Focus
Programmers with over 10
Years Experience
● Their curiosity helps to find the
problem on their own and they
solve it themselves.
● Can take a course and Talk to
people with experience in these
areas.
● Inability to admit the lack of
knowledge
● Understand Scale Challenges
with Data

42. Skill Set Focus
Business Analyst/Managers
● Take a Course
○ Understand how things are built
○ Not necessary to know
mathematics or programming
● Understand the steps in ML
○ Data Collection
○ Data Preparation
○ Model Selection
○ Training
○ Evaluation
○ Parameter Tuning
○ Prediction

43. Skill Set Focus
Business Analyst/Managers
● Incremental Systems
● Accuracy Models
● View AI Videos applied to
Business.
● Log the data properly in your
applications.
● Ability to convey problems to
Solutios Architects

44. Skill Set Focus
Database Admins
● Understand different types of Data
○ Text,Images,Numbers,Files etc
● Learn all about storage
mechanisms, advantages,
disadvantages of different
databases
○ NoSQL - Mongo, Cassandra, GraphDB
(Neo4J), CouchDB
○ SQL

45. Skill Set Focus
Database Admins
● Ability to convey what
database is optimal to what
type of data.
● Design and Build Models for
various kinds of data on paper
● Practice Modeling of Data
Extensively.

46. Skill Set Focus
Domain Experts/CxOs
● Same as Business
Analysts/Managers.
● Formulating the Business
around Data
● AI is used to solve business
problem
● AI is used for Automation

47. Typical Team

48. So, is it possible to be Jack of All,
and Master of Everything?

49. Typical Team
● Domain Expert (Business
Analyst/Product Manager)
● Solutions Architect
● Developer - Data
Collection/Preparation
● Data Scientist/NLP
● Data Engineer.
● Application Developer (Expert in
Visualization domain)

50. Data Scientist vs Data Engineers

51. Data Scientist
● Mathematics, Statistics etc.
● Expert in ML, NLP etc in at
least one of these areas..
● Knows to Apply Different ML
Models and Algorithms

52. Data Engineers
● Takes inputs from Data
Scientists once the problem is
solved
● Exceptionally good at
Programming and different
tools
● Solves problems at Scale
● Productionize the solutions.

53. Concluding
Remarks
● Programming
● Engineering Problems
● Data Organization and
Modeling Problems
● Data collection and Preparation
Major Challenges in Data

54. Data Science -
Task
Allocation
Data Acquisition and
Preparation - Major time
consuming task

55. Concluding
Remarks
● Everything is on cloud, let’s use it.
● Unaware of Business Value
● Clueless About Data Science and
Related Technologies
● Solutions Architect and Domain
Experts are critical to know before
you join.
● Vision of the Company
Major Challenges in Companies

56. Final Remarks Work with Mentor
Have a Photographer’s Mind.
Choose your career wisely

57. References to Start

58. Online Course
● Coursera : Andrew NG Machine
Learning Course https://goo.gl/fDTwSE
● Youtube : Prof. Sengupta
https://goo.gl/JGG6th

59. People and
Books
● People to follow.
○ Andrew NG
○ Bernard Marr - AI Journalist.
○ Geoffrey Hinton
○ Roman Trusov
○ Many people :
https://www.quora.com/Who-are-some-
notable-machine-learning-researchers
● Books
○ Programming Collective Intelligence

60. Q & A

61. Thank You :)