This document provides tips for how to start thinking like a data scientist. It recommends getting priorities and motivations straight by assessing current skills and knowledge to determine the best path. It also advises learning basics like data analysis, introductory statistics, and coding very well before specializing. Finally, it suggests focusing on solving problems by looking for them constantly and starting practical applications early rather than just planning to do so later.

1. How to Start
Thinking Like a Data
Scientist

2. We have all read the headlines – data
scientist is the sexiest job, there’s not
enough of them and the salaries are very
high. The role has been sold so well that
the number of data science courses and
college programs is growing like crazy.
After my previous blog post I have
received messages from people asking
how to become a data

3. scientist – which courses are the best,
what steps to take, what is the fastest
way to land a data science job?
I tried to really think it through and I
reflected on my personal experience –
how did I get here? How did I become a
data scientist? Am I a data scientist? My
experience has been very mixed – I have
started out as a securities analyst in an
investment house using mainly Excel.

4. I then slowly shifted towards business
intelligence in the banking industry and
multiple consultancy projects, eventually
doing the actual “data science” – building
predictive models, working with Big Data,
crunching tons of numbers and writing
code to do data analysis and machine
learning – what in the earlier days was
called “data mining”.

5. When the data science hype has started I
tried to understand how is it different
from what I have been doing so far.
Should I learn new skills and become the
data scientist instead of someone
working “in analytics”?
here are the four rules that have helped
me get into and are still helping me
survive in the data science.

6. Get your priorities and motivations
straight.
Be very realistic about what skills you
have right now and where you want to
arrive – there are so many different types
of roles in data science, it’s important to
understand and assess you current
knowledge base. Let’s say you’re working
in HR and want to change careers – learn
about HR analytics! If you’re a lawyer

7. Learn the basics very well.
Although the specifics of the each data
science field are very different, the basics
are the same. There are three areas
where you should develop a strong
foundation – basic data analysis,
introductory statistics and coding.
Data analysis. You should understand
and practice (a lot!) the basic data
analysis techniques –

8. what is a table, how to join two tables, what
are the main techniques to analyse data
organized in such way, how to build
summary views on your dataset and draw
initial conclusions from it, what is the
exploratory data analysis, which visualizations
help you understand and learn from data.
Also, get a very good grasp of introductory
statistics – what is mean, median, when to
use one over the other

9. Also, get a very good grasp of
introductory statistics – what is mean,
median, when to use one over the other,
what is a standard deviation and when it
doesn’t make
any sense to use it, why averages “lie” but
are still the most used aggregated value
everywhere, etc… When I say
“introductory” I really mean
“introductory”. Unless

10. Data science is about solving problems :
find and solve one.One thing I have
learned over the years is that one of the
fundamental requirements for a data
scientist is to be always asking questions
and looking for problems.
 Now I don’t advise to do it 24/7 as you
will definitely go insane, but be prepared
to be the problem solver and look for the
problems non-stop.

11. Start doing instead of planning what you
will do “when”. This is applicable to any
learning behaviour but it’s especially true
in data science. Be sure you start “doing”
from the very first day you start learning.
It’s very easy to put off the actual
learning by just reading “about” data
science.

12. how it “should” be done, copy-pasting
data analysis code from the book and
running it on very simple datasets which
you will never ever get in the real world.
How will it be useful for Managers in
India:
I’ll leverage the Maturity Index to illustrate
how our customers

13. leverage data and analytics for optimizing
key business processes, uncovering new
monetization opportunities, and creating a
more co
And I’ll walk through a case study in which
I use the eight-step process for analysis
and planning,
to great success smpelling customer
engagement.

14. Thank you