DATA SCIENCE WORKSHOP
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AGENDA • Kick off • Rockfeather • Data science overview • Alteryx & Azure ML • Today's case
• Workshop part I & II • Lunch • Workshop part III & IV • Demo | AutoML
• Machine Learning Canvas • Workshop part V 2
WE LIVE IN A DATA DRIVEN WORLD
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“NETFLIX DOESN’T REALLY HAVE OR DO ANYTHING THAT WE CAN’T OR DON’T ALREADY DO OURSELVES.” – BLOCKBUSTER CEO JIM KEYES, 2008
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BLOCKBUSTER VS NETFLIX REVENUE
“NETFLIX DOESN’T REALLY HAVE OR DO ANYTHING THAT WE CAN’T OR DON’T ALREADY DO OURSELVES.”
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WINNERS ADOPT DATA
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DATA-DRIVEN COMPANIES ARE 58% MORE LIKELY TO BEAT REVENUE GOALS THAN THEIR NON DATA-DRIVEN PEERS – FORRESTER, 2020
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ROCKFEATHER
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EMBRACING TECHNOLOGY EMPOWERING PEOPLE
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• Dashboarding
• Churn Prevention
• App Development
• Management Reporting
• Predictive Maintenance
• Robotic Process Automation
• Results-Orientated KPIs
• Sick Leave Reduction
• Cloud Based Solutions
Tool Selection, Training, Coaching & Support 11
EXPERTISE
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BRANCHES
Financial Services
Construction
Manufacturing
Non-Profit
Retail & FMCG
Telecom & Media
Utilities
Automotive
TECHNOLOGY
OUR PRIDE
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PREVENTING CHURN @ ADO DEN HAAG
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PREVENTING DOWNTIME @ VAN TILBURG & BASTIANEN
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PREVENTING CHURN @ ROYAL BRILL PUBLISHERS
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DATA SCIENCE OVERVIEW
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Artificial Intelligence
Machine Learning
Data Science Deep Learning
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ARTIFICIAL INTELLIGENCE IN A NUTSHELL
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WHY GET STARTED WITH AI?
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Digital processes
Data as output
Information
Analytics
Machine learning
New Services
WEBINAR GARTNER MAGIC QUADRANT 2020 FOR
DATA SCIENCE AND MACHINE LEARNING PLATFORMS
WHAT IS THE MAGIC QUADRANT?
Challengers
Leaders
Niche Players
Visionaries
Ability to Execute Focus on Today
Completeness of Vision Focus on Tomorrow 24
DATA SCIENCE ROLES
1. Identify Business Case
2. Data Collection (ETL)
3. Data Science / Machine Learning
4. Data Visualisation
5. Production
Analytical Leader
Data Engineer / BI Developer
Citizen Expert Data Scientist Data Scientist
Data Engineer / BI Developer
Software Developer
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MAGIC QUADRANT OVERVIEW
Citizen Data Scientist
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Expert Data Scientist
ALTERYX Strengths
Cautions
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Market perception and execution
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Pricing
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No code approach
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Streaming IoT
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2018
2019
2020
MICROSOFT Strengths
Cautions
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Mature functionality for multiple skill levels & analytical personas
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Cloud based products
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Compute power & control
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Coherence
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2018
2019
2020
MACHINE LEARNING BASICS
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CONFUSION MATRIX
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MACHINE LEARNING CANVAS
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Machine Learning Canvas Value Propositions
Data Sources
Data Collecting
Data Engineering
What are we trying to do for the end-
Which raw data sources can we use
How do we get new data to learn from
Input representations extracted from
user(s) of the predictive system? What
(internal and external)?
(inputs and outputs)?
raw data sources.
objectives are we serving?
Live Evaluation & Monitoring Methods and metrics to evaluate the system after deployment, and to quantify value creation.
ML Task Input, output to predict, type of problem
Local Evaluation
Decisions
Methods and metrics to evaluate the
How are predictions used to make
system before deployment.
decisions that provide the proposed value to the end-user?
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NEXT STEPS
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YOU MIGHT ALSO LIKE…. 1. Read our Blogs about data science (rockfeather.com) 2. Check our interview with AI Professor Eric Postma at Tilburg University 3. Attend our Python data science test drive in Q1 2021
4. Blog tip: Louis Dorard, an overview of ML development platforms
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Alexander Mik
Laurens Cruz
Alexander.Mik@rockfeather.com
Laurens.Cruz@rockfeather.com
+316 11399054
+316 19667058
https://www.linkedin.com/in/alexander-mik/
https://www.linkedin.com/in/laurenscruz/