Growing Data Scientists by Amparo Alonso Betanzos

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Technology

GROWING DATA SCIENTISTS
Amparo Alonso Betanzos
University of Corunna, SPAIN

• A
140–190.000 Big Data experts
1.5 M managers with knowledge of Big Data Analytics
92.000 €/year
25-40.000 €/year
67.000 €/year
AVERAGE INCOME

Knowledge Engineering
Intelligent Monitoring
Computer Science
Computer Engineering
Software Engineering
Graduate programs
Research lines

New scalable and efficient
computational methods
for machine learning
in high dimensional datasets

• Maths, Statistics and Machine Learning
• Statistical tests, Distributions, MLE, etc
• Relational Algebra
• Statistical Programming packages (R)
• Scripting Languages (Python)
• Database query languages, SQL and NoSQL
• Computer Science Fundamentals and Software Engineering
• Supervised Learning: Decision trees, Random Forests, K-nearest neighboors, ensemble methods,
• Unsupervised Learning, clustering
• Optimization
• Data preprocessing techniques
• MapReduce concepts, Hadoop, Hive/Pig, Spark,….
• Expertise, Knowledge Domain
• Implementation

• Start working with the candidate in a small
Project
• Empower them in learning technical
capacities
Communication capacities.
Writing and speaking abilities

New techniques
New tools
New data
sources

As a Service
Embedded
Hub with teams, rotation
Data Democratization

Quby is a leading company offering data driven home services technology across European markets, known for creating the in-home display and smart thermostat Toon. We enable our partners to take on a leading role in the home services domain, by offering data driven home services. Our services enable users to control and monitor their homes using both an in-home display and app. As a data driven company, we use AI and machine learning, backed by Apache Spark, to generate actionable insights for all our end users. Via our IoT devices we have access to Europe’s largest energy dataset, petabytes in scale and growing exponentially. This unique dataset enables us to introduce new data driven services, with a particular focus on homes with smart meter installations. In this talk, Ellissa will describe how machine learning is implemented on the Quby platform and will show multiple use cases backed by high-resolution IoT data. We’ll take a look at super resolution techniques for time series data, where using detailed high-resolution energy data is used to show personalized energy insights for users where only limited low-resolution energy data is available. We’ll show how ML algorithms offer the possibility for non-intrusive monitoring of elderly patients. Ellissa will share the experiences from the Data Science and Data Engineering teams at Quby with bringing these data science algorithms from R&D to production using Databricks and the lessons learned in offering these services to hundreds of thousands of users on a daily basis.

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