Simple tips about "what is data science", "how to get started as a data scientist" and "what to keep in mind in a data science project".

1. 10 tips
from a young
data scientist
Nuno Carneiro (nc@nunocarneiro.com)
www.linkedin.com/in/nunocarneiro
1
17/04/2018

2. 1. Data is here to stay
2. There is abundant learning material online
3. There are many types of data scientists
4. It usually looks like: Understand - Data prep - Analyze - Deploy/ Recommend
5. Data prep is 80% of the work
6. Start with the end
7. Projects with quick feedback time are easier
8. Be thorough with your analysis
9. Build insights and provide recommendations
10. Do data science for good
+ Extra: Description of a case study
Agenda

3. About me

4. 10 tips
from a young data scientist

5. 1. Data is here to stay
The amount of available data is growing exponentially. Making use of it will
be crucial for any successful company.

6. 2. There is abundant learning material online
Learn Python:
● Code Academy
Learn Machine Learning:
● Coursera ML Course
● Coursera Deep Learning Course
● Caltech ML course
Learn Python + data science:
● Dataquest
Work on real projects/ compete with
others:
● Kaggle
● Numerai
● DrivenData
For anything else: Google + Stackoverflow

7. 3. There are many types of data scientists
● Classification
● Recommender Systems
● Time-series analysis
● Regression
● Forecasting
● NLP
● ...
Data Science fields
● Business analyst
● Data engineer
● Quant
● Consultant
● ...
Backgrounds:
● Statistics
● Business
● Software Engineering
● ….
Data Science profiles
Data scientists perform very different tasks according to the problem they are trying
to solve. Being a data scientist can mean travelling the world as a consultant or being in
a research office the whole day.

8. 4. It usually looks like this:
Understand Data prep. Analyze
Recommend
or
Deploy
When you are presenting recommendations or deploying a model, decision makers will
be looking for an intuitive explanation to your conclusions. It is very important to help
them make sense of it by explaining your methodology.

9. 5. Data preparation is 80% of the work
Data preparation is a non-linear process that takes the most time in any
data science project. Don’t underestimate the effort it takes.
Extract data Transfer files
Combine
files
Understand
variables
Create new
variables
Treat errors Define target
Document
data
treatment
Select usable
data
Generate
datasets for
analysis

10. 6. Start with the end
The first step in any data science project is to understand how the outcome will be used.
● What are you trying to learn?/ predict?
● How is the outcome going to be used?
● Which performance measures will be used as success criteria?
● How will the outcome have an impact on the business/ on people’s lives? For example,
you can optimize the prevention of churning customers, but this can drain the business
from all its customers.
For example, in classification exercises, Target definition is one the first and the most important steps. It
requires a good understanding of all the questions mentioned above.
When you start any data science project, the first task should be to
understand the business and how the project outcome will be used.

11. 7. Projects with quick feedback time are easier
Target
definition
Prediction
ActionFeedback
If your project has fast feedback cycles, it will be easier to get an
advantage out of machine learning.
● Example of long feedback cycle: credit default prediction
(30 year mortgages…);
● Example of quick feedback cycle: daily sales forecasting.
Besides, while under development, your project should also get
feedback from external stakeholders as fast as possible:
● Iterate fast;
● Interact with your client (external or internal) at every
step of the way.

12. 8. Be thorough with your analysis
A small mistake in the code can easily lead to wrong
conclusions.
Most often, a small mistake in thinking, like a wrongly
defined target or inclusion of future information, will be the
main source of errors.
If you are doing something that few people understand, your most important
currency is trust. Be very thorough with your analysis to prevent mistakes which
could make you lose that trust.

13. 9. Build insights and provide recommendations
Many data scientists get lost in analysis and fail to draw conclusions from their work.
It is very rare to find a data scientist who combines business understanding with analytical
skills and domain over the data science tools (coding, ML, etc.).
So, what?
“So, what?” - Always ask yourself what is the conclusion of your work. If you do statistical
tests, plot data in charts, or analyze prediction backtest results, you should always aim at
two things: 1) Building insights; 2) Providing recommendations.

14. 10. Do data science for good
Use your skills for Good. Even if you have good intentions, watch out for
unintended bad side effects of your work.
PS: Check out the platform mentioned on Tip 2: DrivenData. Their slogan speaks for itself: “Data science competitions to save the world.”
Be careful when creating tools which will not only
predict but also influence future events.
Sometimes, small decisions we take when coding
can have a big impact in other people’s lives.

15. Extra: Case study

16. Case Study: Credit Card Fraud Detection in E-Commerce
http://dx.doi.org/10.1016/j.dss.2017.01.002

17. Thank you
Nuno Carneiro (nc@nunocarneiro.com)
www.linkedin.com/in/nunocarneiro