This document provides an introduction to data science and IoT. It discusses what data science is, the main academic disciplines that constitute it (including mathematics, artificial intelligence, statistics, and machine learning). It also defines data and the different types and structures of data. The document introduces big data and the 5 V's of big data. It discusses the data science process and some common applications of data science like recommender systems and predictive maintenance. Finally, it provides an overview of IoT platforms and how predictive modeling and analysis can be used for maintenance in IoT applications.

1. El khadir LAMRANI
Introduction to Data Science
& IoT

2. Plan
2
• Introduction to data science
• What is data science ?
• Data ?
• Big data ?
• Algorithms
• Data science process
• Data science application
• Data science for IoT
• IoT Platform
• Predictive maintenance modeling & Analysis

3. Introduction to data science
What is Data science ?
3
• An interdisciplinary field about scientific methods, processes, and systems to
extract knowledge or insights from data in various forms
• Concept to unify linear algebra, statistics, dataanalysis and their related methods
in order to understandand analyze actual phenomena with data
• An empirical approach that relies data to provide an answer to problems.
Mathematics
Artificial Intelligence
Domain expertise
Computer vision
Statistics
Machinelearning
Advancedcomputing
Linguistics
Database& StorageVisualization
Main academic disciplines that constitute data science

4. Introduction to data science
As the beginning was data ..
juin 18 4
What is data ?
“Data is the New Oil”
– World Economic Forum 2011

5. Introduction to data science
As the beginning was data ..
juin 18 5
• Type of Data
• Quantitative
• Qualitative
• Complexity of data
• Structured(relational database)
• Semi-structured(Json, XML, CSV, Logs)
• Unstructured(Images,Texts, Videos, Signal, ..)
• Where are the data ?
• Everywhere
• Open dataset
• API (twitter,google, Wikipedia,..)
• Web (web scrapping)
• IoT (sensors)
• …

6. Introduction to data science
Big data
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5 Vs of Big Data
• Raw Data: Volume
• Data types: Variety
• Change over time: Velocity
• Data Quality: Veracity
• Information for Decision Making: Value

7. Introduction to data science
Big data
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Hadoop Ecosystem -Hortonworks data platform

8. Introduction to data science
Algorithms.. To do what ?
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1. Discover links in the data
2. That’s all

9. Type of problem to resolve
• Classification
• Regression
Learning mode
• Suppervised
Regression/Classification
• Unsuppervised
Data driven (Clustering)
• Reinforcement
Algorithms learn to react to an environment
Introduction to data science
There is a taxonomy of algorithms
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10. Famous machine learning algorithms (Source: SAS Blog, Hui Li)
Introduction to data science
There is a taxonomy of algorithms
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11. Introduction to data science
Data Science Process
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12. • Transaction Databases -> Recommender systems (NetFlix), Fraud Detection
• Wireless Sensor Data ->Smart Home, Real-time Monitoring, Internet of
Things
• Text Data, Social Media Data -> Product Review and Consumer Satisfaction
(Facebook, Twitter, LinkedIn), Chatbots, ..
• Genotype and Phenotype Data -> Patient-CenteredCare, Personalized
Medicine, ..
• Images, vidéos -> Health-care, Facial recognition,
Introduction to data science
Data science Applications
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13. Vast network of devices connected to the Internet
• Remote Monitoring of things.
• Things collect and exchange data.
Data science for IoT
What is IoT ?
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14. Introduction to data science
IoT Platform
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15. Data science for IoT
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IoT Platform
Capteurs Passerelle
IoT
IoT HuB
Azure Stream
analytics Event HuB
Données de
référence
Base de données
Tableauxde
bord
Fichier JSON Fichier JSON
Modèle de
prédiction
Fichier JSON
Prédiction
en temps
réel
Fichier CSV
Kafka KafkaSpark streaming

16. Techniques are designed to help determine the condition of in-
service equipment in order to
• Build a model that predicts yield failure on their manufacturing
process
• Through analysis determine the factors that lead to yield failures in
their process.
Data science for IoT
Maintenance predictive modelling & analysis
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17. • Our system will be able to
• Predict failures
• Classify failures
• Find similar cases
• Analyzing the reasons of failures
• Recommend a solution
Data science for IoT
Maintenance predictive modelling & analysis
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18. Thank you.
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