Download to read offline
![Summary – Page 1
The fundamentals include Probability and Statistics, Liner Algebra, Computer Science,
Communication and sensing plus Software and Systems Engineering abilities. [6]
Internet and its architecture built by Tim Berners Lee and team is something to be
understood. Roy Fielding’s seminal dissertation describing REpresentational State Transfer
(REST) is a must read. [7]
Data Mining and Vis ali ation has been po e f l tools fo decision s ppo t as ell as a i ingData Mining and Visualization has been powerful tools for decision support as well as arriving
at correlations. While Weka focuses on Data Mining, for visualization we can use R with R
Studio, Qt Data Visualization or Python itself. [7]
The emergence of consumer electronics especially mobile phones triggered the popularity ofg p y p gg p p y
ARM based architectures and System on Chip which lead to reduction of hardware costs.
ARM architecture and the approach of lifecycle is unique considering the instruction set is
now world wide being used in many millions of devices. [7]
Computer Vision driven by OpenCV was the first to mature with Optical Character recognitionComputer Vision driven by OpenCV was the first to mature with Optical Character recognition
considering the ease at which the vision elements could be transformed in matrices and
used. Automated tests and Hardware in Loop advancements in simulations provide
significant ability to automate development and test process leading to speeding time to
market. [8]a et [8]
Cryptography and Security engineering emerged with the digital solutions and internet. Open
approaches like OpenSSL, OpenLDAP and Driven by standards on Information Security
and IEC 62351 and ISA-99 and with new laws on data protection, cyber security becomes
an ingredient of all digital solutions [8]an ingredient of all digital solutions. [8]](https://image.slidesharecdn.com/2018-learningapproach-digitaltrends-180201030618/75/2018-learning-approach-digitaltrends-3-2048.jpg)
![Summary – Page 2
Data Analytics is one that has moved into the realm of science. Heavily discussed in detail in
McKinsey report-Age of Analytics, this area has significant influences on value proposition.
Unless you require autonomy like in robotics data science could provide the solution The toolsUnless you require autonomy like in robotics, data science could provide the solution. The tools
for data science include R Rstudio, Python and supported tools. PYMC a library based on Python
can be used for Bayesian Belief Networks (BBN). Natural Language Processing being supported
by libraries like NLTK. [9]
The considerations of AI start from Machine Learning and progresses into latest applications ofThe considerations of AI start from Machine Learning and progresses into latest applications of
Deep Learning considering open tools like TensorFlow from Google. Any discussion on learning
AI would need to start with Machine Learning course from Andrew Ng and then progress to
deep learning. [10]
Mi S i hit t l t l b d l ti d i t i i th hMicro Services an architectural style based on evolutionary design to services is the approach
suggested and followed by organizations including Amazon to stay agile and support faster
deployment. [11]
Open Communication Stacks like OpenStack, Basic TCP/IP Stack and latest IoT protocol stacksp p p
have all together started supporting an ecosystem to speed and innovation in communication
development. Additionally supported by mininet like network emulators that support Software
Defined Networking, the possibility of emergence of Software Defined Everything is on the way.
[12]](https://image.slidesharecdn.com/2018-learningapproach-digitaltrends-180201030618/75/2018-learning-approach-digitaltrends-4-2048.jpg)
![Summary – Page 3
There are multiple learning paths for instance a few below:
Probability& Statistics -> Internet -> Data Mining -> Visualization -> AnalyticsProbability& Statistics Internet Data Mining Visualization Analytics
Communications & Sensing-> Internet->Arm SoC-> Hw in Loop->Open Communications> IoT
Computer Science -> Internet->Micro Services-> Open Communications> Cloud Computing
Linear Algebra, Computer Science -> Internet->Machine Learning-> Deep Learning
A Software or Systems Engineering background is necessary, as well is a basic understanding of
Cyber Security.
h h l i l h ld b b d h f b bili l iThe technologies to learn should be based on strengths. If you are strong on probability, analytics
is a sure shot, where as if you are good at communication protocols, then advancement in
protocols and communication including SDN and IoT is the way to go. If you already have an
idea of neural networks and looking forward to autonomy, deep learning is for sure a way to go.
At the same time parallel building knowledge and applying it in some solution would be the way to
go considering the availability of open source frameworks to start with. Mastery of all these
technologies to the detail would be difficult without great team work!
In order to tie these all together, we need to select the right use cases/ business cases, designIn order to tie these all together, we need to select the right use cases/ business cases, design
them in minimal, analyze the returns, the capabilities and select what best serves the purpose.
Here we can apply Design thinking as a framework for analysis and selection, thus tying the
pieces to make a bigger impact. Industry Standards is another most important ingredient. [13]](https://image.slidesharecdn.com/2018-learningapproach-digitaltrends-180201030618/75/2018-learning-approach-digitaltrends-5-2048.jpg)
Uploaded byAbhilash Gopalakrishnan
2018 learning approach-digitaltrends
The document provides an overview of various digital technologies including AI, IoT, cloud computing, data analytics, and more. It discusses the "apples" or fundamental technologies in these areas like AR, VR, AI, IoT, and cloud computing. It then outlines several learning paths one could take to understand these technologies, beginning with foundations in areas like probability, statistics, computer science, and communications. It provides recommendations for books and courses to learn about each technology from roots to more advanced concepts. Finally, it discusses bringing all the pieces together using design thinking.
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2018 learning approach-digitaltrends
- 1. Learning Approach towards Digital trends Abhilash Gopalakrishnanp 1 Feb 2018 * Note: Thesematerial compiled from public information credits to McKinsey, edX, Coursera and different knowledge sources contain personal opinions. None of these represent the opinions of my employer
- 2. AI CC IoT The apples seenon the tree include: AR - Augmented RealityVR DA AR - Augmented Reality VR- Virtual Reality AI-Artificial Intelligence IoT-Internet of Things CC Cloud Computing AR CS CC-Cloud Computing DA-Data Analytics CS- Cyber Security SDN-Software Defined Networks Software Defined Everything SDN Voice Recognition Micro Services Model Based Engineering Distributed Source Control Open Communication Stacks How do we get to the apple and eat as well ? Sir Issac newton identified the force behind the apple falling down as gravitation. Many such strides led to Bayesian Belief Network Natural Language Processing Deep Learning Recognition Open Embedded Systems Distributed Databases Big Data Analytics a g do as g a tat o a y suc st des ed to advancement of humanity. Today as humans we are no more mere observers, instead have power to create new from our understanding of nature. Internet or Data Mining Computer Vision ARM Architecture & System on Chip Cryptography & Security Engineering Automated Test & Hardware in Loop Every science had been through three distinct-stages i.e., classification , correlation and Effect- Cause- Effect. Or otherwise we call a certain interest science once it has reached the third phaseInternet or World Wide Web reached the third phase. The correlations done here is a humble attempt to compile and analyse these technologies and support accelerate their learningtheir learning. If you don’t know what you don’t know, That is a great beginning – Socrates
- 3. Summary – Page 1 The fundamentalsinclude Probability and Statistics, Liner Algebra, Computer Science, Communication and sensing plus Software and Systems Engineering abilities. [6] Internet and its architecture built by Tim Berners Lee and team is something to be understood. Roy Fielding’s seminal dissertation describing REpresentational State Transfer (REST) is a must read. [7] Data Mining and Vis ali ation has been po e f l tools fo decision s ppo t as ell as a i ingData Mining and Visualization has been powerful tools for decision support as well as arriving at correlations. While Weka focuses on Data Mining, for visualization we can use R with R Studio, Qt Data Visualization or Python itself. [7] The emergence of consumer electronics especially mobile phones triggered the popularity ofg p y p gg p p y ARM based architectures and System on Chip which lead to reduction of hardware costs. ARM architecture and the approach of lifecycle is unique considering the instruction set is now world wide being used in many millions of devices. [7] Computer Vision driven by OpenCV was the first to mature with Optical Character recognitionComputer Vision driven by OpenCV was the first to mature with Optical Character recognition considering the ease at which the vision elements could be transformed in matrices and used. Automated tests and Hardware in Loop advancements in simulations provide significant ability to automate development and test process leading to speeding time to market. [8]a et [8] Cryptography and Security engineering emerged with the digital solutions and internet. Open approaches like OpenSSL, OpenLDAP and Driven by standards on Information Security and IEC 62351 and ISA-99 and with new laws on data protection, cyber security becomes an ingredient of all digital solutions [8]an ingredient of all digital solutions. [8]
- 4. Summary – Page 2 Data Analyticsis one that has moved into the realm of science. Heavily discussed in detail in McKinsey report-Age of Analytics, this area has significant influences on value proposition. Unless you require autonomy like in robotics data science could provide the solution The toolsUnless you require autonomy like in robotics, data science could provide the solution. The tools for data science include R Rstudio, Python and supported tools. PYMC a library based on Python can be used for Bayesian Belief Networks (BBN). Natural Language Processing being supported by libraries like NLTK. [9] The considerations of AI start from Machine Learning and progresses into latest applications ofThe considerations of AI start from Machine Learning and progresses into latest applications of Deep Learning considering open tools like TensorFlow from Google. Any discussion on learning AI would need to start with Machine Learning course from Andrew Ng and then progress to deep learning. [10] Mi S i hit t l t l b d l ti d i t i i th hMicro Services an architectural style based on evolutionary design to services is the approach suggested and followed by organizations including Amazon to stay agile and support faster deployment. [11] Open Communication Stacks like OpenStack, Basic TCP/IP Stack and latest IoT protocol stacksp p p have all together started supporting an ecosystem to speed and innovation in communication development. Additionally supported by mininet like network emulators that support Software Defined Networking, the possibility of emergence of Software Defined Everything is on the way. [12]
- 5. Summary – Page 3 There aremultiple learning paths for instance a few below: Probability& Statistics -> Internet -> Data Mining -> Visualization -> AnalyticsProbability& Statistics Internet Data Mining Visualization Analytics Communications & Sensing-> Internet->Arm SoC-> Hw in Loop->Open Communications> IoT Computer Science -> Internet->Micro Services-> Open Communications> Cloud Computing Linear Algebra, Computer Science -> Internet->Machine Learning-> Deep Learning A Software or Systems Engineering background is necessary, as well is a basic understanding of Cyber Security. h h l i l h ld b b d h f b bili l iThe technologies to learn should be based on strengths. If you are strong on probability, analytics is a sure shot, where as if you are good at communication protocols, then advancement in protocols and communication including SDN and IoT is the way to go. If you already have an idea of neural networks and looking forward to autonomy, deep learning is for sure a way to go. At the same time parallel building knowledge and applying it in some solution would be the way to go considering the availability of open source frameworks to start with. Mastery of all these technologies to the detail would be difficult without great team work! In order to tie these all together, we need to select the right use cases/ business cases, designIn order to tie these all together, we need to select the right use cases/ business cases, design them in minimal, analyze the returns, the capabilities and select what best serves the purpose. Here we can apply Design thinking as a framework for analysis and selection, thus tying the pieces to make a bigger impact. Industry Standards is another most important ingredient. [13]
- 6. Roots ‐ Foundations Probability andStatistics Computer ScienceProbability and Statistics Book: https://www.dartmouth.edu/~chance/teaching aids/books articles/probability book/amsboo Computer Science Book: Introduction to Computation and_aids/books_articles/probability_book/amsboo k.mac.pdf Course: https://www.edx.org/course/introduction- Programming Using Python, Revised And Expanded Edition By John V. Guttag Course:p // g/ / probability-science-mitx-6-041x-2 Course: PH525.1x: Data Analysis for Life Sciences 1: Statistics and R h // d / i /d l i https://www.edx.org/course/introduction- computer-science-mitx-6-00-1x-11 https://www.edx.org/xseries/data-analysis- life-sciences Communication & Sensing Book: Communication Networks by Andrew S. Tannenbaum Course: https // ed o g/co se/s stem ie comm nications signals hk st elec1200 1 3https://www.edx.org/course/system-view-communications-signals-hkustx-elec1200-1x-3 https://www.edx.org/course/digital-networks-essentials-imtx-net01x Sensors: http://engineering.nyu.edu/gk12/amps-cbri/pdf/Intro%20to%20Sensors.pdf https://www.edx.org/course/introduction-control-system-design-first-mitx-6-302-0x
- 7. First steps I t tD t Mi iInternet Book: Seminal Dissertation by Roy Thomas Fielding on REST Data Mining Book: Data Mining:: Practical Machine Learning Tools andon REST Courses: Java Web Services Mi ft NET W b S i Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) Microsoft .NET Web Services Python based Services https://blog.miguelgrinberg.com/post/designing-a- restful-api-with-python-and-flask Open Source Software : Weka https://www.cs.waikato.ac.nz/ml/w eka/ (University of Waikato)restful api with python and flask eka/ (University of Waikato) Data Visualization Book :http://www storytellingwithdata com/book ARM architecture and SoC Book :http://www.storytellingwithdata.com/book Courses https://www.edx.org/course/data-visualization-all- Book: ARM System-on-Chip Architecture by Steve Furber F S ft ARM Ki l p g trinityx-t005x https://www.udemy.com/data-visualization/ https://www coursera org/learn/datavisualization Free Software : ARM Kiel Course: Embedded Systems - Shape The World: Microcontrollerhttps://www.coursera.org/learn/datavisualization Open Source Software: Qt Data Visualization, HTML5- JQuery, Tablueu Public Shape The World: Microcontroller Input/output
- 8. Next Steps Cyber Security Books: Computer Vision Book:Books: SecurityEngineering by Ross Anderson Courses: Book: Mastering Open CV with practical computer vision projects https://www.edx.org/course/introduction- cybersecurity-uwashingtonx-cyb001x https://www.edx.org/micromasters/ritx- Course: https://www.udemy.com/hands- on-computer-vision-with-opencv- python/https://www.edx.org/micromasters/ritx cybersecurity Automated Test and Hardware in Loop python/ Automated Test and Hardware in Loop Book: ‘Test Driven Development: By Example’ By Kent Beck Test driven development for Embedded C : http://cpputest.github.io/ Hardware-in-the-Loop (HIL) Simulation Course: h // d / /d i i f d 200 5 0https://www.edx.org/course/devops-testing-microsoft-devops200-5x-0 https://www.udemy.com/test-driven-development-for-professionals/
- 9. Next Steps Start with DataAnalytics and AI with this report The age of analytics: Competing in a data-driven world by McKinsey https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the- age-of-analytics-competing-in-a-data-driven-worldg y p g Data Analytics Book: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IpythonBook: Python for Data Analysis: Data Wrangling with Pandas, NumPy, and Ipython Data Analytics - Models and Algorithms for Intelligent Data Analysis Courses: htt // d / i /d t l i lif ihttps://www.edx.org/xseries/data-analysis-life-sciences https://www.edx.org/course/data-science-r-basics-harvardx-ph125-1x-0 https://www.edx.org/course/python-for-data-science Open Source Software : Using Python NLTK for Natural Language Processing andOpen Source Software : Using Python NLTK for Natural Language Processing and PYMC for Bayesian Belief Networks URLS: https://www.kdnuggets.com/2018/01/gregory-piatetsky-data-science- i b t j ht lincubator-january.html https://www.kdnuggets.com/2017/07/machine-learning-big-data-explained.html https://www.kdnuggets.com/2017/09/science-data-science.html
- 10. Next Steps Artificial Intelligence Book :Book: Artificial Intelligence: Pearson New International Edition: A Modern Approach by Stuart Russel & Team Courses: https://courses.edx.org/courses/BerkeleyX/CS188.1x-4/1T2015/course/https://courses.edx.org/courses/BerkeleyX/CS188.1x 4/1T2015/course/ https://www.edx.org/course/artificial-intelligence-ai-columbiax-csmm-101x-4 Micro Masters: https://www.edx.org/micromasters/columbiax-artificial-intelligence A Faster Path - Following Andrew Ng (Baidu), also explains the math behind: htt // d / / hi l i l bi 102 2https://www.edx.org/course/machine-learning-columbiax-csmm-102x-2 https://www.coursera.org/learn/machine-learning https://www.coursera.org/specializations/deep-learningp // g/ p / p g Open Software : TensorFlow
- 11. Next Steps Internet of Things Book:Internet of Things: Principles and Micro Services Book: Book: Internet of Things: Principles and Paradigms Course: h // d / i / i Book: Microservice Architecture: Aligning Principles, Practices, and Culture l https://www.edx.org/micromasters/curtin x-internet-of-things-iot https://www.edx.org/course/iot-sensors- devices curtinx iot2x Material: https://martinfowler.com/articles/microservic es.html devices-curtinx-iot2x https://www.mooc-list.com/tags/sensors https://www.fullstackpython.com/microservic es.html Di t ib t d S M d l B d E i iDi t ib t dDistributed Source Control Book: Model Based Engineering Book: Model-Based Systems Engineering with Distributed Databases Book: Version Control with Git 2e Material: y g g OPM and SysML Courses: https://www coursera org/learn/mbse Version Control with Git 2e Material: https://git- scm.com/book/en/v2 https://www.coursera.org/learn/mbse https://sysengonline.mit.edu/ https://git- scm.com/book/en/v2
- 12. Next Steps Open Embedded Systems Book: OpenCommunications Book: Book: Real-Time Embedded Systems: Open- Source Operating Systems Perspective U i F RTOS R l i K l Book: OpenStack Essentials OpenStack for Architects Using FreeRTOS Realtime Kernel Material: FREERTOS Material: https://www.edx.org/course/introduction- openstack-linuxfoundationx-lfs152x FREERTOS https://www.raspberrypi.org/help/ openstack linuxfoundationx lfs152x Software Defined EverythingSoftware Defined Everything Book: SDN – Software Defined Networks Software Defined Storage for Dummies Cloud Computing: Principles and Paradigm Distributed and Cloud Computing: From Parallel Processing to the Internet of ThingsDistributed and Cloud Computing: From Parallel Processing to the Internet of Things Courses: https://www.edx.org/course/introduction-cloud-computing-ieeex-cloudintro-x-2
- 13. Putting it all together Design Thinking Book: Changeby Design: How Design Thinking Transforms Organizations and Inspires Innovationp Short Presentation: Link in Slideshare Courses: https://emeritus.org/management-certificate-programs/innovation-design- thinking/thinking/ https://www.coursera.org/learn/uva-darden-design-thinking-innovation