2018 learning approach-digitaltrends

Learning Approach towards Digital trends
Abhilash Gopalakrishnanp
1 Feb 2018
* Note: These material 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

AI
CC
IoT
The apples seen on 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

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]

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]

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]

Roots ‐ Foundations
Probability and Statistics 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
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

First steps
I t t D 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

Next Steps
Cyber Security
Books:
Computer Vision
Book:Books:
Security Engineering by Ross Anderson
Courses:
Book:
Mastering Open CV with practical
computer vision projects
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/

Next Steps
Start with Data Analytics 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

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

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

Next Steps
Open Embedded Systems
Book:
Open Communications
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:
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

Putting it all together
Design Thinking
Book: Change by 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

2018 learning approach-digitaltrends

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