This document provides an overview of data science from an implementation perspective. It discusses the different mindsets of scientists, engineers, and mathematicians. Scientists are comfortable with uncertainty while engineers prefer determinism. Models are used to represent abstractions while prototypes are made for implementation. Both deterministic and probabilistic approaches are discussed. Accuracy metrics like precision and recall are important for evaluating models. The CRISP-DM process involves business goals, data preparation, modeling, and production implementation. Effective communication between product management, data science, engineering, and QA is important for success.

1. Data Science
An Implementation Perspective
http://tech.lalitbhatt.net
Director Engineering – Entytle Inc
(http://www.entytle.com)

2. The Joke of the town is
 Engineers think that equations approximate the real
world.
 Scientists think that the real world approximates
equations.
 Mathematicians are unable to make the connection.
Science is very comfortable living with uncertainty and not
knowing. Engineers hate that. - Ranjeet Tate
Does not mean anyone is less or more important than
other. Everyone has it’s place in grand scheme.
Scientist and Engineer

3.  Models to represent the abstraction
 Prototype Implementation
 Solution for production
Science looks for truth.
What engineers build, they consider it truth.
Science and Engineering

4.  It’s a mind set – Schrödinger Cat / Earthquakes
 Deterministic: Single solution for appropriate inputs
so that it can be actionable with no interpretation.
 Probabilistic: Distribution of possible outcomes and
how likely they are going to occur. Still actionable
but requires interpretation
Deterministic and Probabilistic

5. Example:
 Customer A can bring $10000 revenue
 Customer B can bring $1000 revenue
Which one to chase if for A the probability is 0.001
and for B probability is 0.5?
Deterministic and Probabilistic

6. Finding what?
There are known knowns; there are things we know
that we know. There are known unknowns; that is to
say, there are things that we now know we don't know.
But there are also unknown unknowns -- there are
things we do not know we don't know.
Donald Rumsfield

7. A Process view
 Business Goals
 Data finding and cleaning – De-duplication,
Normalization, Nomenclature, Units, Outliers
 Exploration – Visualization, Patterns, Causality
 Modeling – Build a story
 Testing and Validation
 Production Implementation
 Feedback loop
Domain
Knowledge

8.  Classification and regression
 Cluster analysis
 Pattern mining
 Outlier analysis
Lists not exhaustive but most important takeaway is
Model building.
Mathematical Tools

9.  Don’t try to find every pattern in everything. Mine
the pattern and not force it.
 If it’s available to buy, consider that.
 Never underestimate the variation that one can get
in real data
 More you code, more bug you put.
Approach to implementation

10.  Descriptive: Last year we had 10 mm of rain per
day average.
 Predictive: Next year we will have 9 mm of rain per
day average.
 Prescriptive: Recommendation on what can
increase the rain further.
Types of Output

11.  Training vs Testing
 A/B Test
 Errors
Testing Models/Implementations

12.  Type I and Type II errors
 Accuracy = (110+80)/(110+50+40+80)
 Recall = 110/(110 +40) How many +ve were right
 Precision = 110/(110+50) Predicted yes and came yes
 Diagnostics Odd ratio = (TP/FP)/(FN/TN)
 …
Models accuracy
Predicted Y Predicted N
Actual Y 110 40
Actual N 50 80

13. CRISP-DM
Image: https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining

14. Communication Flow
Product
Management
Data Science
Engineering QA
Let’s appreciate each one is doing his/her job.
Like any relationship, it takes constant work
What customers want?
What data is telling?
What is the model and
how to implement it.
Preferably a prototype
implementation
Input and Output
Is model working
on all type of data
and at scale?
Also what are edge
cases?
Review test results on
all possible real life
cases
Input and
expected
output

15. Thanks
Questions