François Chung, Ph.D.

Tag: coursera

Storytelling and influencing

Storytelling and influencing

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Coursera training, MOOC (2023). The ability to effectively communicate and persuade others is a key leadership skill. Traditional common-sense models of communication and persuasion often fail to capture the complex nature of ‘influencing’. This online training from Macquarie University (AU) aims to develop our capacity to communicate appropriately in different situational and cultural contexts, making us influential leaders.

Week 1: The necessary art of persuasion

Main topics:

  • Persuasive communication;
  • Assessing human behavior;
  • Decision making;
  • Questioning for unconscious values.

Week 2: Telling your story

Main topics:

  • Information processing and recalling stories;
  • Storytellers;
  • The importance of storytelling;
  • Structuring stories.

Week 3: Connecting with people

Main topics:

  • The art and science of building rapport;
  • Relationships only happen with rapport;
  • The matching exercise;
  • Mehrabian's communication model;
  • Making your meeting matter.

Week 4: Talk the talk

Main topics:

  • Group influence and impression management;
  • The three questions to influence;
  • The need to lead;
  • The questions that get real answers.

Week 5: Painful truth

Main topics:

  • Looking for win-win;
  • The nature of objections;
  • Convincing quickly.

Week 6: Winning over hearts and minds

Main topics:

  • Pitching as a persuasion process;
  • Perfect pitch preparation;
  • The secret structure;
  • The reason people ask questions.

References

Cybersecurity specialization

Cybersecurity specialization

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Coursera training, MOOC (2022). This specialization from The University of Maryland (US) covers the fundamental concepts underlying the construction of secure systems, including the hardware, the software and the human-computer interface, with the use of cryptography to secure interactions. These concepts are illustrated with examples drawn from modern practice, and augmented with hands-on exercises involving relevant tools and techniques.

Course 1: Usable security

Main topics:

  • Human-Computer Interaction (HCI);
  • Design methodology and prototyping;
  • A/B testing, quantitative and qualitative evaluation;
  • Secure interaction design;
  • Biometrics, two-factor authentication (2FA);
  • Privacy settings, data inference.

Course 2: Software security

Main topics:

  • Low-level security: attacks and exploits;
  • Defending against low-level exploits:
  • Web security: attacks and defenses;
  • Designing and building secure software;
  • Static program analysis;
  • Penetration and fuzz testing.

Course 3: Cryptography

Main topics:

  • Computational secrecy and modern cryptography;
  • Private-key encryption;
  • Message authentication codes;
  • Number theory;
  • Key exchange and public-key encryption;
  • Digital signatures.

Course 4: Hardware security

Main topics:

  • Digital system design: basics and vulnerabilities;
  • Designing intellectual property protection;
  • Physical attacks and modular exponentiation;
  • Side-channel attacks and countermeasures;
  • Hardware trojan detection;
  • Trusted integrated circuit;
  • Good practice and emerging technologies.

References

Training

Usable security (course certificate)
Software security (course certificate)
Cryptography (course certificate)
Hardware security (course certificate)

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Google Cloud: big data and machine learning

Google Cloud: big data and machine learning

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Coursera training, MOOC (2020). This online training introduces the big data and machine learning (ML) capabilities of Google Cloud Platform (CGP). Through a combination of presentations, demos and hands-on labs, the training gives an overview of Google Cloud and a detailed view of the data processing and ML solutions, such as BigQuery, Cloud SQL, Dataproc, Pub/Sub, Dataflow and Data Studio.

Week 1: Big data and ML fundamentals

Main topics:

  • Exploring a BigQuery public dataset;
  • Choosing the right solution approach;
  • Recommending products using Cloud SQL and Spark;
  • Predicting visitor purchases using BigQuery ML.

Week 2: Modern data pipeline challenges

Main topics:

  • Real-time IoT dashboards;
  • Creating a streaming data pipeline;
  • ML on unstructured datasets;
  • Classifying images with pre-built ML models.

References

Data science specialization

Data science specialization

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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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Covid-19: epidemiology and contact tracing

Covid-19: epidemiology and contact tracing

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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

Training

COVID-19 Contact Tracing (course certificate)

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