Data science team, a practice to setup

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Data science team from roles to data pipeline

Datos y análisis

Data Science Team
A practice to set up
Omid Mogharian
V0.2.1 - 06.02.2017

… it’s a mistake to treat data science teams like any old product group ... To build
teams that create great data products, you have to find people with the skills and
the curiosity to ask the big questions. You have build cross-disciplinary groups with
people who are comfortable creating together…
DJ Patil, U.S. Chief Data Scientist at White House
Office of Science and Technology Policy

Roles
Machine Learning
Engineer
Data
Engineer/Architect
Data Analyst
Math &
Statistics
Interpretation/
Visualisation
Modeling &
ML
Math &
Statistics
Machine
Learning
Developing
Developing
Infrastructure
Design
Operation
Data Scientist
Core Team Skills

Relations
Data Science Team
Operations/
System
Administration
Sales
PO/
Customer
relation
BI
App
Development
To make it real

Method
CRISP-DM
The Data Science Process
Communications with Customer

How to? lambdaBig data and Fast data

Big Data Pipeline
Job repository
Scheduler/
Runner
Incremental
Runner
Message QueueData Pipe Agent
A practice for continuous analyse
Severing
layer

Application Environments
Source
Simulator
Stage
Production
Sample Data
Source
Connector
Big Data
Continuous Analyse Application*
Continuous Analyse Application*
To bring accuracy
* Whole software with
several components
which are explained in
previous slide

References
● https://dzone.com/articles/lambda-architecture-with-apache-spark
● https://en.wikipedia.org/wiki/Lambda_architecture
● https://www.mapr.com/developercentral/lambda-architecture
● http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html
● http://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html
● http://www.datascienceassn.org/sites/default/files/Building%20Data%20Scienc
e%20Teams.pdf

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Building a healthy data ecosystem around Kafka and Hadoop: Lessons learned at...

Just ask Watson Seminar

Just ask Watson Seminar

Just ask Watson Seminar

1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx

1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx

1.-DE-LECTURE-1-INTRO-TO-DATA-ENGG.pptx

Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote

Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote

Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote

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Gse uk-cedrinemadera-2018-shared

Gse uk-cedrinemadera-2018-shared

Meetup Data-science OVH

Meetup Data-science OVH

Meetup Data-science OVH

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Data Engineering Proposal for Homerunner.pptx

Data Engineering Proposal for Homerunner.pptx

Is Your Staff Big Data Ready? 5 Things to Know About What It Will Take to Suc...

Is Your Staff Big Data Ready? 5 Things to Know About What It Will Take to Suc...

Is Your Staff Big Data Ready? 5 Things to Know About What It Will Take to Suc...

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BI & Analytics with Ms Power BI.pptx

BI & Analytics with Ms Power BI.pptx

Data Science as a Service: Intersection of Cloud Computing and Data Science

Data Science as a Service: Intersection of Cloud Computing and Data Science

Data Science as a Service: Intersection of Cloud Computing and Data Science

Data Science as a Service: Intersection of Cloud Computing and Data Science

Data Science as a Service: Intersection of Cloud Computing and Data Science

Data Science as a Service: Intersection of Cloud Computing and Data Science

Visionet Business Intelligence Solutions - Is your Business Intelligence real...

Visionet Business Intelligence Solutions - Is your Business Intelligence real...

Visionet Business Intelligence Solutions - Is your Business Intelligence real...

Qiagram

Big Data Meetup: Analytical Systems Evolution

Big Data Meetup: Analytical Systems Evolution

Big Data Meetup: Analytical Systems Evolution

Tips and Tricks to be an Effective Data Scientist

Tips and Tricks to be an Effective Data Scientist

Tips and Tricks to be an Effective Data Scientist

Analyst Webinar: The Role of a Logical Architecture in Modern Data and Analytics

Analyst Webinar: The Role of a Logical Architecture in Modern Data and Analytics

Analyst Webinar: The Role of a Logical Architecture in Modern Data and Analytics

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Privacy in Computer Vision

Privacy in Computer Vision

Privacy in Computer Vision

While the advances of AI and machine learning can benefit immensely people and societies, more and more concerns regarding privacy violations and data breaches are addressed. In this talk, first Privacy-Preserving Machine Learning including its aspects and potentials will be explored. Afterward, what Distributed Ledger Technologies (DLTs) can offer for a practical environment to enable private computation is discussed.

The journey to Private AI, where Privacy-Preserving ML meets DLT

The journey to Private AI, where Privacy-Preserving ML meets DLT

The journey to Private AI, where Privacy-Preserving ML meets DLT

Blockchain has received an exceptional amount of attention by the media and large swaths of the population, due to its links to cryptocurrencies such as Bitcoin. Although many people in industry and academia believe that the technology is likely to be the next disruptor, the lack of deep understanding of the technology, its benefits and the reasoning behind the use cases can be a true barrier to reach such future. In this talk, we try to comprehend the usage of such technology via categorising its applications in different layers. We discuss the limitations of blockchain technology and how the next generations of this technology aim to address those. Furthermore, we will picture the potential impacts and possible future field of activities of Distributed Ledger Technologies (including blockchain) through defining major artifacts.

Blockchain, a disappointing tech talk

Blockchain, a disappointing tech talk

Blockchain, a disappointing tech talk

How big is big data?

How big is big data?

How big is big data?

Distributed File System and Why It Matters.

Distributed File System and Why It Matters.

Distributed File System and Why It Matters.

Hadoop essential setup

Hadoop essential setup

Hadoop essential setup

Python: The Dynamic!

Python: The Dynamic!

Python: The Dynamic!

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Privacy in Computer Vision

Privacy in Computer Vision

Privacy in Computer Vision

The journey to Private AI, where Privacy-Preserving ML meets DLT

The journey to Private AI, where Privacy-Preserving ML meets DLT

The journey to Private AI, where Privacy-Preserving ML meets DLT

Blockchain, a disappointing tech talk

Blockchain, a disappointing tech talk

Blockchain, a disappointing tech talk

How big is big data?

How big is big data?

How big is big data?

Distributed File System and Why It Matters.

Distributed File System and Why It Matters.

Distributed File System and Why It Matters.

Hadoop essential setup

Hadoop essential setup

Hadoop essential setup

Python: The Dynamic!

Python: The Dynamic!

Python: The Dynamic!

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Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

Discover Why Less is More in B2B Research

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Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

Digital Transformation Playbook by Graham Ware

As electricity is difficult to store, it is crucial to strictly maintain the balance between production and consumption. The integration of intermittent renewable energies into the production mix has made the management of the balance more complex. However, access to near real-time data and communication with consumers via smart meters suggest demand response. Specifically, sending signals would encourage users to adjust their consumption according to the production of electricity. The algorithms used to select these signals must learn consumer reactions and optimize them while balancing exploration and exploitation. Various sequential or reinforcement learning approaches are being considered.

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2. … it’s a mistake to treat data science teams like any old product group ... To build teams that create great data products, you have to find people with the skills and the curiosity to ask the big questions. You have build cross-disciplinary groups with people who are comfortable creating together… DJ Patil, U.S. Chief Data Scientist at White House Office of Science and Technology Policy

3. Roles Machine Learning Engineer Data Engineer/Architect Data Analyst Math & Statistics Interpretation/ Visualisation Modeling & ML Math & Statistics Machine Learning Developing Developing Infrastructure Design Operation Data Scientist Core Team Skills

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5. Method CRISP-DM The Data Science Process Communications with Customer

6. How to? lambdaBig data and Fast data

7. Big Data Pipeline Job repository Scheduler/ Runner Incremental Runner Message QueueData Pipe Agent A practice for continuous analyse Severing layer

8. Application Environments Source Simulator Stage Production Sample Data Source Connector Big Data Continuous Analyse Application* Continuous Analyse Application* To bring accuracy * Whole software with several components which are explained in previous slide

9. References ● https://dzone.com/articles/lambda-architecture-with-apache-spark ● https://en.wikipedia.org/wiki/Lambda_architecture ● https://www.mapr.com/developercentral/lambda-architecture ● http://www.kdnuggets.com/2015/11/different-data-science-roles-industry.html ● http://www.kdnuggets.com/2016/03/data-science-process-rediscovered.html ● http://www.datascienceassn.org/sites/default/files/Building%20Data%20Scienc e%20Teams.pdf