Agile development of data science projects | Part 1

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This document discusses agile development of data science projects. It begins by defining data science as focusing on predicting, prescribing, or explaining something, distinct from business intelligence which focuses on reporting past events. It notes data science encompasses quantitative research, advanced analytics, predictive modeling, and machine learning. It then discusses how reliably data science teams can deliver value, showing a data science readiness level chart ranging from algorithm design to proven systems. The rest of the document discusses collaborating across teams and organizations to move from initial concepts to specific, integrated predictive systems.

Données & analyses

Agile development of data
science projects | Part 1
Anubhav Dhiman | July 18, 2018 | Berlin

What is data science?
Data science focuses on predicting something,
prescribing something, or in some cases explaining
something, making it distinct from Business Intelligence
(BI), which focuses on backward-looking factual
reporting (describing something that happened).
It is also distinct from big data storage and processing
technologies like Hadoop and Spark. These tools are
valuable inputs into the quantitative research process
but are insufficient to realise the full potential of data
science.
Successful organizations coordinate all three areas
(data science, BI, and big data) to achieve maximum
value
Broadly data science encompasses
quantitative research, advanced analytics,
predictive modelling and machine learning.

How reliably and
sustainably can
data science team
deliver value for
organizations?
Source: Domino Data Lab

Delivery
9. System proven in operational environment
8. System complete and qualified
7. Prototype demonstrated in operation environment
6. Algorithm integrated in development
5. Algorithm validated against production data
Discovery
4. Algorithm validated against sample data
3. Experimental proof of concept
2. Data explored and described
1. Algorithm design and development
Data Science
Readiness Levels
Source: Emily Gorcenski

How to make
collaboration
easier across
organization?
Source: Louis Dorard

From :
1. background to
specifics
2. domain
integration to
predictive
engine
Source: Louis Dorard
1
2 3
4 5
7 6
8 9
10

Up Next … Part 2
- Data Science Lifecycle
- Developing and Deploying
AI solutions

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Agile development of data science projects | Part 1

1. Agile development of data science projects | Part 1 Anubhav Dhiman | July 18, 2018 | Berlin

2. What is data science? Data science focuses on predicting something, prescribing something, or in some cases explaining something, making it distinct from Business Intelligence (BI), which focuses on backward-looking factual reporting (describing something that happened). It is also distinct from big data storage and processing technologies like Hadoop and Spark. These tools are valuable inputs into the quantitative research process but are insufficient to realise the full potential of data science. Successful organizations coordinate all three areas (data science, BI, and big data) to achieve maximum value Broadly data science encompasses quantitative research, advanced analytics, predictive modelling and machine learning.

3. How reliably and sustainably can data science team deliver value for organizations? Source: Domino Data Lab

4. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development Data Science Readiness Levels Source: Emily Gorcenski

5. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development Can we solve problem as stated? Data Scientists, Data Engineers1 4 1

6. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development What does a MVP look like? +Designers, Product Managers Data Scientists, Data Engineers 2 1 2 1

7. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development How do we build the MVP? +Designers, Product Managers Data Scientists, Data Engineers +Infra, Backend, Frontend 3 2 1 3 2 1

8. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development How do we ship the MVP? +QA, Legal +Designers, Product Managers Data Scientists, Data Engineers +Infra, Backend, Frontend 4 3 2 1 4 3 2 1

9. Delivery 9. System proven in operational environment 8. System complete and qualified 7. Prototype demonstrated in operation environment 6. Algorithm integrated in development 5. Algorithm validated against production data Discovery 4. Algorithm validated against sample data 3. Experimental proof of concept 2. Data explored and described 1. Algorithm design and development How do we improve MVP? +CR, Analytics +QA, Legal +Designers, Product Managers Data Scientists, Data Engineers +Infra, Backend, Frontend 5 4 3 2 1 5 4 3 2 1

10. How to make collaboration easier across organization? Source: Louis Dorard

11. From : 1. background to specifics 2. domain integration to predictive engine Source: Louis Dorard 1 2 3 4 5 7 6 8 9 10

12. Up Next … Part 2 - Data Science Lifecycle - Developing and Deploying AI solutions

Agile development of data science projects | Part 1

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