François Chung, Ph.D.

Tag: coursera

Cybersecurity specialization

Cybersecurity specialization

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

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

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

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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Bitcoin and cryptocurrency technologies

Bitcoin and cryptocurrency technologies

Coursera training, MOOC (2019). Given online by Princeton University (US), this training explains how Bitcoin and other cryptocurrencies work at a technical level, and provides the conceptual foundations to engineer secure software that interacts with the Bitcoin network. Other important topics covered are how secure Bitcoins are, how anonymous Bitcoin users are, what determines the price of Bitcoins and whether cryptocurrencies can be regulated.

Week 1: Introduction to crypto and cryptocurrencies

Main topics:

  • Cryptographic hash functions;
  • Hash pointers and data structures;
  • Digital signatures;
  • Public keys as identities.

Week 2: How Bitcoin achieves decentralization

Main topics:

  • Centralization vs. decentralization;
  • Distributed consensus;
  • Consensus without identity: the blockchain;
  • Incentives and proof of work.

Week 3: Mechanics of Bitcoin

Main topics:

  • Bitcoin transactions;
  • Bitcoin scripts;
  • Bitcoin blocks;
  • Bitcoin network.

Week 4: How to store and use Bitcoins

Main topics:

  • Online wallets and exchanges;
  • Payment services;
  • Transaction fees;
  • Currency exchange markets.

Week 5: Bitcoin mining

Main topics:

  • Mining hardware;
  • Energy consumption and ecology;
  • Mining pools;
  • Mining incentives and strategies.

Week 6: Bitcoin and anonymity

Main topics:

  • How to de-anonymize Bitcoin;
  • Decentralized mixing;
  • Zerocoin and Zerocash;
  • Tor and Silk Road.

Week 7: Community, politics and regulation

Main topics:

  • Consensus in Bitcoin;
  • Bitcoin core software;
  • Governments notice Bitcoin;
  • Anti money-laundering.

Week 8: Alternative mining puzzles

Main topics:

  • ASIC resistant puzzles;
  • Proof-of-useful-work;
  • Nonoutsourceable puzzles;
  • Virtual mining.

Week 9: Bitcoin as a platform

Main topics:

  • Bitcoin as an append-only log;
  • Bitcoin as smart property;
  • Multi-party lotteries in Bitcoin;
  • Bitcoin as randomness source.

Week 10: Altcoins and the cryptocurrency ecosystem

Main topics:

  • Short history of Altcoins;
  • Interaction between Bitcoin and Altcoins;
  • Lifecycle of an Altcoin;
  • Sidechains.

Week 11: The future of Bitcoin

Main topics:

  • The blockchain as a vehicle for decentralization;
  • Blockchain integration;
  • What can we decentralize?
  • When is decentralization a good idea?

References

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