IoT Protocols

 

https://cloud.google.com/iot/docs/concepts/protocols

https://docs.oracle.com/en/cloud/paas/iot-cloud/develop/iot-connectivity-protocols.html

https://azure.microsoft.com/en-in/overview/internet-of-things-iot/iot-technology-protocols/

https://www.avsystem.com/blog/iot-protocols-and-standards/

2020

https://www.nabto.com/guide-iot-protocols-standards/

A Comprehensive Review on IoT Protocols’ Features in Smart Grid Communication

by Lilia TightizOrcID andHyosik Yang *OrcID

Department of Computer Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea

*

Author to whom correspondence should be addressed.

Energies 2020, 13(11), 2762; https://doi.org/10.3390/en13112762

https://www.mdpi.com/1996-1073/13/11/2762

Internet of Things: Architectures, Protocols, and Applications

Pallavi Sethi1 and Smruti R. Sarangi 1

Journal of Electrical and Computer Engineering / 2017 

Volume 2017 |Article ID 9324035 | https://doi.org/10.1155/2017/9324035

https://www.hindawi.com/journals/jece/2017/9324035/

A Survey of Protocols and Standards for Internet of Things

Tara Salman, Raj Jain

Department of Computer Science and Engineering

Washington University in St. Louis

https://arxiv.org/ftp/arxiv/papers/1903/1903.11549.pdf

Agile Software Development Methodology

 

https://www.agilealliance.org/agile101/

https://www.infoworld.com/article/3237508/what-is-agile-methodology-modern-software-development-explained.html

https://agilemanifesto.org/

Kent Beck

Mike Beedle

Arie van Bennekum

Alistair Cockburn

Ward Cunningham

Martin Fowler

James Grenning

Jim Highsmith

Andrew Hunt

Ron Jeffries

Jon Kern

Brian Marick

Robert C. Martin

Steve Mellor

Ken Schwaber

Jeff Sutherland

Dave Thomas

Embracing Agile: How to master the process that’s transforming management 

by Darrell K. Rigby, Jeff Sutherland, and Hirotaka Takeuchi

From the HBR Magazine (May 2016)

https://hbr.org/2016/05/embracing-agile

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https://www.youtube.com/watch?v=WjwEh15M5Rw

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Data Science Research - Journals, Papers and Areas

 Data Science - Areas of Research

Top ten areas - MIT Press - 2020

https://hdsr.mitpress.mit.edu/pub/d9j96ne4/release/2

2020

https://www.analyticsinsight.net/top-10-research-challenge-areas-pursue-data-science/

Data Science - Journals

Journal of Management Analytics

Publish open access in this journal

Focuses on the theory of data analytics and its application in traditional business disciplines, such as accounting, finance, and supply chain management.

https://www.tandfonline.com/toc/tjma20/current

2021

Data Science Methodologies: Current Challenges and Future Approaches

I˜nigo Martineza,, Elisabeth Viles, Igor G Olaizolaa

Preprint submitted to Big Data Research - Elsevier

June 15, 2021

https://arxiv.org/pdf/2106.07287

Research questions:

• RQ1: What methodologies can be found on the literature to manage data science projects?

• RQ2: Are these available methodologies prepared to meet the demands of current challenges?

7. Development Workflows for Data Scientists 

Development Workflows for Data Scientists by Github and O’Reilly Media

8. Big Data Ideation, Assessment and Implementation

Big data ideation, assessment and implementation by Martin Vanauer

10. Agile Delivery Framework

Larson and Chang proposed a framework based on the synthesis of agile principles with Business Intelligence (BI), fast analytics and data science. There are two layers of strategic tasks: (A) the top layer includes BI delivery and (B) the bottom layer includes fast analytics and data science.

In this article the conceptual framework is presented for designing integral methodologies for the management of data science projects. The framework proposes  three foundation stones: project, team and data & information management.

The disciplinary research landscape of data science reflected in data science journals

Lingzi Hong , William Moen , Xinchen Yu , Jiangping Chen 

Information Discovery and Delivery (2020)

https://www.emerald.com/insight/content/doi/10.1108/IDD-06-2020-0071/full/html 

The research questions for the study are:

RQ1. What is the population of journals that focus on topics of data science?

RQ2. What disciplinary landscape of data science is reveal

Important - Table - Top keywords of disciplines

Data Science - Techniques

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2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square?

2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis

3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:14:55 kNN Example 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes

5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example

6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo

6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation

7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo

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_____________________________

Deep Learning - Introduction and Bibliography

What is Deep Learning?

Deep learning is a form of machine learning for nonlinear high dimensional data reduction and prediction.

Using  Bayesian probabilistic perspective in deep learning provides a number of advantages. Specifically statistical interpretation and properties, more efficient algorithms for optimisation and
hyper-parameter tuning, and an explanation of predictive performance. 

Traditional high dimensional statistical techniques; principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are shallow learners.

Their deep learning counterparts exploit multiple layers of of data reduction which leads to performance gains. Stochastic gradient descent (SGD) training and optimisation and Dropout (DO) provides model and variable selection. Bayesian regularization is central to finding networks and provides a framework for optimal bias-variance trade-off to achieve good out-of sample performance.

To illustrate the use of bayesian perspective,  an analysis of first time international bookings on Airbnb. is presented in the paper.


https://arxiv.org/pdf/1706.00473.pdf



Deep Learning Introduction
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How to get started with Deep Learning for Data Science?



-1. Learn Python and R ;)

0. Andrew Ng and Coursera

- https://lnkd.in/eUe9YZE

1. Siraj Raval: YouTube channel. Specifically this playlists:

- The Math of Intelligence: https://lnkd.in/eYPJbsW

- Intro to Deep Learning: https://lnkd.in/e4Sg9qy

2. François Chollet's book: Deep Learning with Python (and R soon):

- https://lnkd.in/gfV2ery
- https://lnkd.in/e6_YGqx

3. IBM Cognitive Class:

- https://lnkd.in/eNKPSnJ
- https://lnkd.in/eBVRf-R

4. Medium blogs:

- https://lnkd.in/eaUx5aN
- https://lnkd.in/eGaQwts

5. DataCamp:

- https://lnkd.in/eWVz7e5
- https://lnkd.in/ezXBq6M

Info collected from a Linkedin Post

https://www.linkedin.com/feed/update/urn:li:activity:6363784952114401280

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Updated 21 July 2021,  2 February 2018
5 June 2017