François Chung, Ph.D.
Azure: fundamentals, machine learning and Power BI

Azure: fundamentals, machine learning and Power BI

Microsoft Docs training, MOOC (2020). These 3 online courses present Microsoft Azure and Power BI. The training teaches the basic cloud concepts, along with hands-on exercises, and provides an overview of Azure services, such as Azure Machine Learning (ML), which is a cloud platform for training, deploying, managing and monitoring ML models. Furthermore, the training explains how to use Power BI and build business intelligence reports.

Course 1: Azure fundamentals

Main topics:

  • Principles of cloud computing;
  • Azure architecture and service guarantees;
  • Compute, data storage and networking;
  • Security, responsibility and trust;
  • Infrastructure standards with Azure Policy;
  • Azure resources with Azure Resource Manager.

Course 2: Azure machine learning

Main topics:

  • Working with data;
  • Orchestrate ML with pipelines;
  • Deploy ML models;
  • Automate model selection;
  • Tune hyperparameters;
  • Monitor models and data drift.

Course 3: Power BI

Main topics:

  • Get started building with Power BI;
  • Get data with Power BI Desktop;
  • Model and explore data;
  • Use visuals, publish and share.

References

Training

Microsoft Docs (badges and trophies)

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Microsoft Docs (Azure fundamentals)
Microsoft Docs (Azure machine learning)
Microsoft Docs (Power BI)

Strategic thinking

Strategic thinking

LinkedIn Learning training, MOOC (2020). Working hard is important, but what's really essential is making sure to be working on the right things. Strategic thinking helps managers and leaders guide the direction of their teams and find solutions to key business problems. Furthermore, the training presents frameworks and strategies to make strategic thinking a daily habit, so that we can make the best use of our time, energy and effort at work.

Course 1: Strategic thinking

Main topics:

  • Strategic thinking: the big picture;
  • Developing your strategic thinking;
  • Making implementation happen.

Course 2: How to make strategic thinking a habit

Main topics:

  • Setting yourself up for strategic thinking;
  • Strategic thinking tactics;
  • The most important strategic questions.

References

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LinkedIn Learning (Strategic thinking)
LinkedIn Learning (How to make strategic thinking a habit)

Trends debate: technology and privacy

Trends debate: technology and privacy

Fujitsu project @Brussels, Belgium (2020). In the midst of the Covid-19 pandemic, and the development of contact tracing applications to help track and stop the spread of the coronavirus, Trends organized an online debate around technology and privacy entitled: “Does saying yes to technology mean saying no to ethics and privacy?”. The debate has been published as an article in Trends, which is a Flemish financial-economic magazine presenting analyses of current economic, political and social news.

Participants in this debate, who are experts in technology, ethics, privacy or legislation, are representing the following organizations:

  • Atos;
  • Everest Law;
  • Fujitsu;
  • Icapps;
  • Ministry of Privacy;
  • Nutanix;
  • Privaco;
  • SiriusLegal;
  • Universiteit Gent.

The main topics of discussion around this debate are as follows:

  • Should a contact tracing application be considered as a Pandora's box whose consequences cannot be estimated?
  • Is GDPR sufficient as a security mechanism? Or do we need to develop a clearer framework to regulate the potential misuse of technology?
  • To what extent does fragmentation, at political, geographic or economic level, hinder the efficiency of contact tracing applications requiring a global reach to be efficient?
  • Who decides what can be allowed with the possibilities offered by the technology? And how can we enforce that?
  • What is the correct mechanism to determine when a technology is needed? And when can a market be considered as ready?

As a Digital Business Analyst representing Fujitsu for this debate, I shared Fujitsu’s vision and values regarding technology and privacy in the context of the global Covid-19 pandemic, with topics such as working from home (technologies and benefits), extending the legal framework, including GDPR, to regulate Artificial Intelligence and building a human centric future with ethical technology. The debate has been published as an article in Trends on 13th August 2020.

References

Publication

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Quantum computing and physics

Quantum computing and physics

Udemy training, MOOC (2020). This online training presents quantum computing as the next wave of the software industry. Quantum computers are exponentially faster than classical computers of today. Problems that were considered too difficult for computers to solve, such as simulation of protein folding in biological systems and cracking RSA encryption, are now possible through quantum computers. The training is primarily about analyzing the behavior of quantum circuits using math and quantum physics.

Section 1: Introduction

Main topics:

  • Why learn about quantum computing?
  • How is quantum computing different?

Section 2: Quantum cryptography

Main topics:

  • Experiments with photon polarization;
  • No-cloning theorem;
  • Encoding with XOR;
  • Encryption with single-use shared-secrets;
  • Encoding data in photon polarization.

Section 3: Foundation

Main topics:

  • Probability;
  • Complex numbers;
  • Matrix algebra;
  • Matrix multiplication;
  • Logic circuits.

Section 4: Math model for quantum physics

Main topics:

  • Modeling physics with math;
  • Substractive probabilities through complex numbers;
  • Modeling superposition through matrices.

Section 5: Quantum physics of spin states

Main topics:

  • Matrix representation of quantum state;
  • State vector;
  • Experiments with spin.

Section 6: Modeling quantum spin states with math

Main topics:

  • Analysis of experiments;
  • Dirac bra-ket notation;
  • Random behavior.

Section 7: Reversible and irreversible state transformations

Main topics:

  • Irreversible transformations measurement;
  • Reversible state transformations.

Section 8: Multi-qubit systems

Main topic:

  • Multi-qubit systems.

Section 9: Quantum entanglement

Main topic:

  • Quantum entanglement.

Section 10: Quantum computing model

Main topics:

  • Quantum circuits;
  • Reversible gates;
  • CNOT and CCNOT gates;
  • Universal and Fredkin gates;
  • Superposition and entanglement on quantum gates.

Section 11: Quantum programming with Microsoft Q#

Main topics:

  • Q# simulator hardware architecture;
  • Measuring superposition states;
  • Effect of superposition on quantum gates;
  • Toffoli gate;
  • Programming quantum computers.

Section 12: IBM quantum experience

Main topic:

  • IBM quantum experience.

Section 13: Conclusion

Main topic:

  • Speedup revisited.

References

Training

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DataNews 2020 (NL) (magazine article, Dutch version)

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Data science specialization

Data science specialization

Coursera training, MOOC (2020). This specialization covers the concepts and tools needed throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. Topics covered include using R to clean, analyze, and visualize data, navigating the entire data science pipeline from data acquisition to publication, using GitHub to manage data science projects, and performing regression analysis, least squares and inference using regression models.

Course 1: Data scientist’s toolbox

Main topics:

  • Data science fundamentals;
  • R and Rstudio;
  • Version control and GitHub;
  • R Markdown, scientific thinking and big data.

Course 2: R programming

Main topics:

  • Background and getting started;
  • Programming with R;
  • Loop functions and debugging;
  • Simulation and code profiling.

Course 3: Getting and cleaning data

Main topics:

  • Finding data and reading different file types;
  • Data storage systems;
  • Organizing, merging and managing data;
  • Text and data manipulation in R.

Course 4: Exploratory data analysis

Main topics:

  • Analytic graphics and base plotting in R;
  • Lattice and ggplot2;
  • Data dimension reduction;
  • Cluster analysis techniques.

Course 5: Reproducible research

Main topics:

  • Concepts, ideas and structure;
  • Markdown and knitr;
  • Reproducible research checklist;
  • Evidence-based data analysis.

Course 6: Statistical inference

Main topics:

  • Probability and expected values;
  • Variability, distribution and asymptote;
  • Intervals, testing and p-value;
  • Power, bootstrapping and permutation tests.

Course 7: Regression models

Main topics:

  • Least squares and linear regression;
  • Linear and multivariate regression;
  • Residuals and diagnostics;
  • Logistic and Poisson regression.

Course 8: Practical machine learning

Main topics:

  • Prediction, errors and cross validation;
  • Caret package;
  • Decision trees and random forests;
  • Regularized regression and combining predictors.

Course 9: Developing data products

Main topics:

  • Shiny, GoogleVis and Plotly;
  • R Markdown and Leaflet;
  • R Pakages and Swirl.

References

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Spark fundamentals (Cognitive Class training)
Hadoop fundamentals (Cognitive Class training)
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Blockchain essentials

Blockchain essentials

Cognitive Class training, MOOC (2020). This online training presents blockchain for business and explores use cases that demonstrate how this technology is radically improving supply chains and banking, as well as creating new opportunities for innovation. Blockchain technology provides a shared ledger to save time when recording transactions between parties, remove costs associated with intermediaries and reduce risks of fraud and tampering.

Module 1: What is blockchain?

Main topics:

  • Business networks;
  • Ledgers, transactions and contracts;
  • The problem with existing networks;
  • Different types of blockchain;
  • Requirements of a blockchain for business.

Module 2: Examples of blockchain networks

Main topics:

  • Improving global trade;
  • Supply chain transparency;
  • Global payments;
  • Decentralized and trusted identity;
  • Key players for blockchain adoption.

Module 3: IBM and blockchain

Main topics:

  • IBM's blockchain strategy;
  • IBM Blockchain Platform;
  • Hyperledger project;
  • Hyperledger Fabric.

References

Training

Blockchain essentials (course certificate)
IBM Blockchain Essentials V2 (certification badge)

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AWS: foundations and machine learning

AWS: foundations and machine learning

AWS training, MOOC (2020). These online courses provide an overall understanding of AWS cloud, with an overview of cloud concepts, services, security, architecture, pricing and support. Specific courses to AWS partners teach best practices to address customer business priorities around costs, agility, compliance, innovation and growth. Machine Learning (ML) is also covered, with a deep dive into the same ML curriculum used to train Amazon’s developers and data scientists.

AWS Cloud Practitioner Essentials

Main topics:

  • AWS core services;
  • AWS integrated services;
  • AWS architecture;
  • AWS security;
  • Pricing and support.

AWS Partner Solutions: Business foundations

Main topics:

  • Build your business with AWS;
  • What matters to AWS customers;
  • Security, identity and compliance;
  • Pricing and licensing;
  • Migration and cloud adoption;
  • Opportunity management.

AWS Partner Solutions: Technical foundations

Main topics:

  • AWS solution architects;
  • AWS architectural concepts;
  • Building blocks;
  • AWS Well-Architected Framework;
  • Architecting an AWS solution;
  • Engaging customers and architecting solutions.

AWS Machine Learning: Decision maker

Main topics:

  • Demystifying AI/ML/DL;
  • ML for business challenges;
  • ML terminology;
  • Exploring the ML toolset.

AWS Machine Learning: Data scientist

Main topics:

  • Math for ML;
  • Linear and logistic regression;
  • Elements of data science;
  • Real world ML decisions.

References

Covid-19: epidemiology and contact tracing

Covid-19: epidemiology and contact tracing

Coursera training, MOOC (2020). These 2 online courses from Johns Hopkins University (US) have been attended in the midst of Covid-19 pandemic. The first explains how to identify and measure outbreaks like the Covid-19 epidemic, and how to understand the epidemiology of these infections. The second is about the science of SARS-CoV-2, including the infectious period, the clinical presentation of Covid-19, and why contact tracing can be an effective public health intervention.

Fighting Covid-19 with epidemiology

Main topics:

  • How do we identify and measure outbreaks like Covid-19?
  • How do we investigate and control outbreaks?

Covid-19 contact tracing

Main topics:

  • Basics of Covid-19;
  • Basics of contact tracing for Covid-19;
  • Steps to investigate cases and trace their contacts;
  • Ethics of contact tracing and technological tools;
  • Skills for effective communication.

References

Fundamentals of digital marketing

Fundamentals of digital marketing

Google Digital Garage training, MOOC (2020). This accredited online training, which has been designed by Google in collaboration with industry and education experts, is composed of 26 modules to explore core digital marketing topics. The training is packed full of practical exercises and real-world examples to learn the fundamentals of digital marketing, improve online digital skills, turn knowledge into action and open up new career opportunities.

Part 1: Take a business online

Main topics:

  • The online opportunity;
  • Your first steps in online success;
  • Build your web presence;
  • Plan your online business strategy.

Part 2: Make it easy for people to find a business on the web

Main topics:

  • Get started with search;
  • Get discovered with search;
  • Make search work for you;
  • Be noticed with search ads;
  • Improve your search campaigns.

Part 3: Reach more people locally, on social media or on mobile

Main topics:

  • Get noticed locally;
  • Help people nearby find you online;
  • Get noticed with social media;
  • Deep dive into social media;
  • Discover the possibilities of mobile;
  • Make mobile work for you;
  • Get started with content marketing.

Part 4: Reach more customers with advertising

Main topics:

  • Connect through email;
  • Advertise on other websites;
  • Deep dive into display advertising;
  • Make the most of video.

Part 5: Track and measure web traffic

Main topics:

  • Get started with analytics;
  • Find success with analytics;
  • Turn data into insights.

Part 6: Sell products or services online

Main topics:

  • Build your online shop;
  • Sell more online.

Part 7: Take a business goal

Main topic:

  • Expand internationally.

References

Training

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Enterprise Design Thinking

Enterprise Design Thinking

IBM training, MOOC (2020). This 3-course online training from IBM aims to train design thinkers to address uncertainty, work within constraints, and create human-centered solutions. People with a human-centered mindset are primed to solve problems together, with empathy and humility. With Enterprise Design Thinking, teams can work more efficiently, because they stay aligned and keep people at the center of their work. It’s a proven way to come to better solutions, faster.

Course 1: Enterprise Design Thinking practitioner

Main topics:

  • A focus on user outcomes;
  • Restless reinvention;
  • Diverse empowered teams;
  • Make a plan.

Course 2: Enterprise Design Thinking co-creator

Main topics:

  • Embrace diversity on your team;
  • Stakeholder map;
  • Create a research plan;
  • Turn research into actions;
  • Empathy map;
  • Build detailed prototypes;
  • Prioritization grid;
  • Refine through feedback;
  • Experience-based roadmap.

Course 3: Enterprise Design Thinking - Team Essentials for AI

Main topics:

  • The AI essentials framework;
  • Define your intent;
  • Identify data sources;
  • Recognize what your AI needs to understand;
  • Articulate your AI strategy;
  • Reflect on your AI’s capabilities.

References