📄 Big data need physical ideas and methods
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
https://arxiv.org/pdf/1412.6848v1
📌 If a person looks at WHITE paper through BLUE glasses, the paper will become BLUE in the eye of the person. Likewise, in the current study of big data which play the same role as the white paper being looked at, various statistical methods just serve as the blue glasses. That is, results obtained from big data often depend on the statistical methods in use, which may often defy reality. Here I suggest using physical ideas and methods to overcome this problem to the greatest extent. This suggestion is helpful to development and application of big data.
#Data_Analysis , #Statistics and #Probability (physics.data-an)
📝 The many facets of community detection in complex networks
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
📌 ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
Michael T. Schaub, Jean-Charles Delvenne, Martin Rosvall, Renaud Lambiotte
https://arxiv.org/pdf/1611.07769v1
📌 ABSTRACT
Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.
#Social and #Information #Networks (cs.SI); #Data_Analysis, #Statistics and #Probability (physics.data-an); #Physics and #Society (physics.soc-ph
#Coursera Specializations for #Big_Data #Data_Science #Data_Mining #Machine_Learning #Genomic_algorithm
1️⃣ Master Statistics with R
Statistical mastery of data analysis including inference, modeling, and Bayesian approaches.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/statistics?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
2️⃣ Analyze Text, Discover Patterns, Visualize Data
Solve real-world data mining challenges.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/data-mining?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
3️⃣ Launch Your Career in Data Science
A nine-course introduction to data science, developed and taught by leading professors.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/jhu-data-science?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
4️⃣ Unlock Value in Massive Datasets
Learn fundamental big data methods in six straightforward courses.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/big-data?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
5️⃣ Master Algorithmic Programming Techniques
Learn algorithms through programming and advance your software engineering or data science career
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/data-structures-algorithms?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
6️⃣ Become a next generation sequencing data scientist
Master the tools and techniques at the forefront of the sequencing data revolution.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/genomic-data-science?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
1️⃣ Master Statistics with R
Statistical mastery of data analysis including inference, modeling, and Bayesian approaches.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/statistics?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
2️⃣ Analyze Text, Discover Patterns, Visualize Data
Solve real-world data mining challenges.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/data-mining?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
3️⃣ Launch Your Career in Data Science
A nine-course introduction to data science, developed and taught by leading professors.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/jhu-data-science?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
4️⃣ Unlock Value in Massive Datasets
Learn fundamental big data methods in six straightforward courses.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/big-data?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
5️⃣ Master Algorithmic Programming Techniques
Learn algorithms through programming and advance your software engineering or data science career
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/data-structures-algorithms?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
6️⃣ Become a next generation sequencing data scientist
Master the tools and techniques at the forefront of the sequencing data revolution.
(Financial Aid is available for learners who cannot afford the fee.)
🔗 https://www.coursera.org/specializations/genomic-data-science?utm_medium=email&utm_source=marketing&utm_campaign=i63GIP7GEeaXhPPOxq79Gw
20 days left to the submission deadline! We welcome studies on #data for #socialgood and the #wellbeing of the most #vulnerable! Proceedings are going to be published on @FrontAIBigData!
https://t.co/QYfEgdRrhy
https://t.co/QYfEgdRrhy
⚠ A mathematical model from 103 years ago predicted something that was seen for the first time today: a #black_hole.
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
#MachineLearning could never do that: it needs observations to model anything. This is a major weak-point of ML. Let's fix it.
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because #data in those areas is rare. Gathering data in important areas is as important a skill as building fancy neural networks.
Sadly, too often, using extreme inputs to a model is more useful: e.g. by modeling physics of levers on light objects with short levers, we then built very long levers to lift extremely heavy things. Instead, ML is better suited at modeling everyday phenomena with complex models.
https://twitter.com/Reza_Zadeh/status/1053771110410375168?s=19
Twitter
Reza Zadeh
A stark contrast between Machine Learning vs other forms of mathematical modeling is that ML models often don't model extreme corner cases very well, because data in those areas is rare. Gathering data in important areas is as important a skill as building…
Working on #ComplexSystems? #networks & #data? Looking for applying your next methods on #Brain #Life #Disease #SocialSystems #Epidemics #HumanMobility? Aiming at working in a leading Italian research center?
Then our Lab can be your next stop! #MSCA 2019
Get in touch for info!
https://ec.europa.eu/research/mariecurieactions/news/2019-msca-call-individual-fellowships-open_en
Then our Lab can be your next stop! #MSCA 2019
Get in touch for info!
https://ec.europa.eu/research/mariecurieactions/news/2019-msca-call-individual-fellowships-open_en
Time Series Analysis From the Ground Up
You can now download the slide deck from our Intro to Time Series tutorial:
https://t.co/6AiGOPZAlh
#Data Science #Timeseries #ARIMA #MachineLearning #Tutorial
You can now download the slide deck from our Intro to Time Series tutorial:
https://t.co/6AiGOPZAlh
#Data Science #Timeseries #ARIMA #MachineLearning #Tutorial
📱 #Mobile_phone #data for informing public health actions across the COVID-19 pandemic life cycle, freely available here:
https://t.co/AFZIf4FNWt
https://t.co/AFZIf4FNWt
Science
Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle
The coronavirus 2019-2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world ( 1 ). Non-pharmaceutical interventions (NPIs) have proven to be critical for delaying and containing the COVID-19 pandemic ( 2 – 6…
💰 We are currently looking for passionate Data Engineers and Data Scientists for our HQ in Berlin, who can improve food-delivery experience for millions of our customers, present in more than 40 countries!
You will develop innovative systems to automate marketing campaigns, design a tailored user experience for each customer and question the existing decision-making processes with ML and advanced analytics solutions.If you're a creative problem solver who is eager to deliver solutions and hungry for a new adventure, an international workplace is waiting for you in the heart of Berlin!
Senior data engineer (Python):
https://lnkd.in/dvzFPZU
Senior data scientist:
https://lnkd.in/dMaCQgC
#dataengineering #datascience #machinelearning #ml #data #deliveryhero
Quick peek at what the team does:
https://lnkd.in/dP2cuNf
You will develop innovative systems to automate marketing campaigns, design a tailored user experience for each customer and question the existing decision-making processes with ML and advanced analytics solutions.If you're a creative problem solver who is eager to deliver solutions and hungry for a new adventure, an international workplace is waiting for you in the heart of Berlin!
Senior data engineer (Python):
https://lnkd.in/dvzFPZU
Senior data scientist:
https://lnkd.in/dMaCQgC
#dataengineering #datascience #machinelearning #ml #data #deliveryhero
Quick peek at what the team does:
https://lnkd.in/dP2cuNf
Delivery Hero
(Senior) Python Data Engineer - Marketing Tech (f/m/d) in Berlin, Germany | Tech at Delivery Hero
Apply for (Senior) Python Data Engineer - Marketing Tech (f/m/d) job with Delivery Hero in Berlin, Germany. Tech at Delivery Hero
💰 Three new #PhD positions in my lab! Broadly focussing on #networks, #dynamics, and #data analysis in #biodiversity and #social systems. (more details soon)
https://t.co/ogcitWzgOk
https://t.co/ogcitWzgOk
💰 Come do a #PhD with me in #cognitive #data #science and #complex #networks at @UniofExeter!
In an EPSRC scholarship by @exetercompsci , we'll investigate how to give structure to #knowledge and its influence in socio-cognitive systems.
Deadline 25/01/21:
https://t.co/BBokMFV3za
In an EPSRC scholarship by @exetercompsci , we'll investigate how to give structure to #knowledge and its influence in socio-cognitive systems.
Deadline 25/01/21:
https://t.co/BBokMFV3za
What do #physicists do when they have lots of massive #data sets? They comb through it looking what properties of systems can be intuited from the data alone!
#statisticalphysics #manybodysystems
https://t.co/W7orMxshDE
#statisticalphysics #manybodysystems
https://t.co/W7orMxshDE
105
<unknown>
#ComplexityPodcast with SFI External Prof Mason Porter about #NetworkScience tools for community detection and how #Topology reveals a hidden order in the flood of #Data:
complexity.simplecast.com/episodes/105
complexity.simplecast.com/episodes/105