Learning Progress 2020

Learning is an eternal path. I wanted to share my own adventure primarily for the following practical reasons: to help me remember what I have supposedly been working on technology-wise and to help me practice a technique that is known as spaced repetition. This is really just a summary to motivate myself to keep on track, to practice consistent self-reflection and to be reminded as often as possible to stay a bit more humble, as it becomes obvious that I don’t know much in the end. In the process, I hope you may find some inspiration too.

December

30

• University of London
• Agile Software Projects
  • Converting user stories to use case diagrams.
• Vim: Run multiple Ex Commands as a batch.
• Fundamentals of TDD: Red-Green-Refactor by Example.

29

• University of London
• Agile Software Projects
  • Converting user stories to use case diagrams.
• Vim: Running commands in the shell.
• Fundamentals of TDD: Overview.

28

• University of London
• Agile Software Projects
  • Use case diagrams.
• Vim: Tab-complete Ex commands; insert word under the cursor in command mode; recall commands from history.
• Mastering the shell: configure the shell.

27

• University of London
• Agile Software Projects
  • Market research.
• Programming With Data
  • Python: Error handling; plotting in Matplotlib.
• Vim: Running normal commands across a range.
• Mastering the shell: Shell scripting.

26

• University of London
• Programming With Data
  • Python: Cleaning data.
• Vim: Duplicating and moving lines with :t and :m.
• Mastering the shell: Pipes; inputs; outputs.
• betaveros on Advent of Code.

25

• Vim: Command-line mode; differences with Vi, Ex commands.
• Mastering the shell: slice and dice with grep and sed.

23

• University of London
• Agile Software Projects
  • Market research.
• Programming With Data
  • Python: Cleaning data.
• Vim: Editing tabular data with visual block mode.
• tmux: man page, commands, keybindings.
• Mastering the shell: man page, commands, keybindings.

22

• University of London
• Agile Software Projects
  • Market research.
• Programming With Data
  • Python: Defensive programming with error handling.
• Vim: More on Visual mode.
• tmux: Advanced workflows.

21

• University of London
• Agile Software Projects
  • Market research; testing in context, types of tests; measuring aims and objectives.
• Vim: Inserting unusual characters by character code and digraph; overwriting existing text in Replace mode.
• tmux: Vim integration.
• Setting up cloud_sql_proxy inside Docker.

20

• University of London
• Agile Software Projects
  • Market research, user survey.
• Programming With Data
  • Python: Scraping data.
• Vim: Make corrections instantly from Insert mode; paste from a register without leaving insert mode.
• tmux: Configuration; layout & navigation.
• How to set up live streaming on YouTube with OBS.

19

• University of London
• Agile Software Projects
  • Requirements gathering/elicitation.
• Programming With Data
  • Python: Scraping data.
• Basics of command handlers with Discord bot.
• Configuring basic Docker images.
• Vim: Operator-pending mode.
• tmux: Managing sessions.

18

• University of London
• Programming With Data
  • Python: Scraping data.

17

• University of London
• Agile Software Projects
  • [Project] Researching state of worldwide broadband access.
• Programming With Data
  • Python: Scraping data.

16

• University of London
• Agile Software Projects
  • [Project] Researching state of worldwide broadband access.
• Programming With Data
  • Python: Integrating Jupytext with pre-commit hook.

15

• University of London
• Agile Software Projects
  • [Project] Researching state of worldwide broadband access.
• Programming With Data
  • Python: VCS with Jupyter Notebooks using Jupytext.

14

• University of London
• Agile Software Projects
  • Creating a user survey.
• Programming With Data
  • Python, pandas: concatenate; merge; compare.
  • Python: Data visualisation principles, seaborn.
  • Git.
• Vim: use counts to do simple arithmetic and repeat commands.

13

• University of London
• Agile Software Projects
  • Introduction to testing, evaluation methods, unit tests, software proposal.
• Programming With Data
  • Python, pandas: dummy variables; exploding values; merge; join; concatenate.
  • Python: unittest module.
• Vim: Comparing buffers with vimdiff; updating config on the fly; compose repeatable changes.
• tmux: Managing sessions.
• i3: Review how containers are stored and managed with trees.

12

• University of London
• Graphics Programming
  • p5.js: Interview with Memo Atken (computer vision).
• Vim: Find and replace by hand; using the “dot” command; chunking undos.
• tmux: Keybindings.

10

• University of London
• Agile Software Projects
  • Agile in practice, lean approach, Kanban basics, Scrum and sprints, software proposal.
• Graphics Programming
  • p5.js: Optical flow; face detection.
• Programming With Data
  • Python, pandas: Pivot tables; cross tabulation.
• Figma: Scroll between elements in prototypes.
• Vim: Repeating and reversing operations.
• tmux: Keybindings.

9

• University of London
• Agile Software Projects
  • Software proposal.
• Graphics Programming
  • p5.js: Background subtraction; frame differencing.
• Vim: Project-wide search & replace.

8

• University of London
• Agile Software Projects
  • Defining Agile, software proposal.
• Graphics Programming
  • p5.js: Computer vision and its applications; brightness tracking; colour tracking.

7

• University of London
• Agile Software Projects
  • Predictive models; prototyping.
• Programming With Data
  • Python, pandas: Reshaping and pivot tables.

6

• University of London
• Agile Software Projects
  • A/B testing; predictive models.
• Graphics Programming
  • p5.js: Instagram filter.

5

• University of London
• Agile Software Projects
  • Heuristic evaluation; walk-throughs; analytics and A/B testing.
• Graphics Programming
  • p5.js: Instagram filter.
• Python: quick review of packing and unpacking.

3

• Python: quick review of re module.
• Presenting and exporting prototypes in Figma.

2

• Read about YAML specifications.

1

• Figma: Building a navigation menu with components; constraints; layout grids; auto layout.

November

30

• University of London
• Agile Software Projects
  • Design patterns; open source resources; tools for interaction design.
• Graphics Programming
  • p5.js: Instagram filter.
• Figma: layout grids; scrollable content on mobile; building a navigation menu with components.

29

• University of London
• Agile Software Projects
  • Introduction to user-centred design.
• Graphics Programming
  • p5.js: Instagram filter.

28

• University of London
• Agile Software Projects
  • Introduction to user-centred design; agileUX; design patterns.
• Graphics Programming
  • p5.js: Instagram filter.
• Programming With Data
  • Python, pandas: Reshaping and pivot tables.

27

• University of London
• Graphics Programming
  • p5.js: filters (edge detection, sharpen, in GIMP).
• Programming With Data
  • Python, pandas: Reshaping and pivot tables.
• Figma: review shapes and tools; Bézier tool; masks; text; layers; components; variants.

26

• University of London
• Agile Software Projects
  • Market analysis.
• Graphics Programming
  • p5.js: Digital image processing; filters (invert, grayscale, threshold, blur); convolution.
• Figma: vectors; variants; constraints; shapes; gradients; auto layout; styles; guides; components; masks.
• Very brief intro to TypeScript.

25

• University of London
• Graphics Programming
  • p5.js: working with images; pixels; averaging images.
• Mocking in Python.

24

• University of London
• Agile Software Projects
  • Requirements gathering, market analysis.
• Graphics Programming
  • p5.js: working with images; pixels; averaging images.
• Review how to undo actions in Git.

23

• University of London
• Agile Software Projects
  • Requirements gathering, market analysis.
• Graphics Programming
  • p5.js: working with images; pixels; averaging images.

22

• University of London
• Agile Software Projects
  • Introduction to user centred design; interaction design in practice.
• Graphics Programming
  • p5.js: working with images; pixels.

21

• University of London
• Agile Software Projects
  • Research questions.
• Graphics Programming
  • p5.js: 3D and WebGL; colour harmony; working with images; pixels.
• Programming With Data
  • Python: data cleaning and preparation; SQL injections.

20

• University of London
• Graphics Programming
  • p5.js: 3D and WebGL.
• Programming With Data
  • Python: data cleaning and preparation.

19

• University of London
• Agile Software Projects
  • Research questions.

17

• Web scraping and crawling in Python.

16

• University of London
• Agile Software Projects
  • Research questions.
• Graphics Programming
  • p5.js: 3D and WebGL.

15

• University of London
• Agile Software Projects
  • User stories, choosing a tech stack.
• Programming With Data
  • Python: data cleaning and preparation.
• Graphics Programming
  • matter.js: Graphics in video games; graphics in films and art; introduction to 3D graphics with materials and lights; camera and perspective; texture.

14

• University of London
• Programming With Data
  • Python: data cleaning and preparation.
• Graphics Programming
  • matter.js: Additive syntesis; fractals; Sierpinski carpet.
• Review cron/crontab with Corey Schafer.

13

• University of London
• Agile Software Projects
  • Department for Education Realising the potential of technology in education: A strategy for education providers and the technology industry.
• Graphics Programming
  • matter.js: Oscillation.
• Review the grep command and the find command with Corey Schafer.

12

• University of London
• Agile Software Projects
  • Department for Education Realising the potential of technology in education: A strategy for education providers and the technology industry.
• Graphics Programming
  • matter.js: Trigonometry.
• Programming With Data
  • Python: data cleaning and preparation.

11

• University of London
• Agile Software Projects
  • User stories.
• Graphics Programming
  • matter.js: Perlin noise; 2D noise; 3D noise.
• Programming With Data
  • Python: Errors and exceptions; exploring the problem space of cleaning data.

10

• University of London
• Agile Software Projects
  • Design and prototyping.
• Graphics Programming
  • matter.js: Perlin noise; 2D noise; 3D noise.
• Programming With Data
  • Python: Working with missing data in pandas, assert, errors and exceptions.

9

• University of London
• Graphics Programming
  • matter.js: Perlin noise; 2D noise; 3D noise.

8

• University of London
• Agile Software Projects
  • Market analysis.
• Graphics Programming
  • matter.js: Working with time and progress bars; generative art.
• Programming With Data
  • Python: Cleaning and restructuring data.

7

• University of London
• Graphics Programming
  • matter.js: Working with time and progress bars.

6

• University of London
• Agile Software Projects
  • Design, prototyping, and construction.
• Graphics Programming
  • matter.js: Working with time and progress bars.

5

• University of London
• Graphics Programming
  • matter.js: Working with time and progress bars.

4

• University of London
• Agile Software Projects
  • Design, prototyping, and construction.
• Graphics Programming
  • matter.js: Constraints.
• Programming With Data
  • Python: Retrieving data from databases using query languages; getting to grips with the basics, working with missing data in Pandas, asserts and exceptions.

3

• University of London
• Agile Software Projects
  • User-centred design in a nutshell; design, prototyping, and construction.
• Graphics Programming
  • matter.js: Constraints.
• Programming With Data
  • SQL and connection handling in Python.

3

• University of London
• Agile Software Projects
  • User-centred design in a nutshell; design, prototyping, and construction.
• Graphics Programming
  • matter.js: Constraints.
• Programming With Data
  • SQL and connection handling in Python.

2

• University of London
• Agile Software Projects
  • User-centred design in a nutshell.
• Graphics Programming
  • matter.js: Static objects and composites.
• Programming With Data
  • Introduction to SQL.

1

• University of London
• Agile Software Projects
  • Competitor analysis; alternative models to SWOT/STEEPLE.
• Graphics Programming
  • matter.js: Static objects and composites.
• Programming With Data
  • Introduction to HTML, DOM, web scraping, HTTP, file formats on the web; regular expressions; introduction to databases.

October

31

• University of London
• Graphics Programming
  • Introduction to matter.js; constraints.

30

• University of London
• Agile Software Projects
  • Data analysis, interpretation & presentation; planning for research; analysing the competition; SWOT analysis.
• Graphics Programming
  • Introduction to matter.js.
• Programming With Data
  • Python: Working with CSV; data processing libraries; working with JSON; retrieving data from the web; walrus operator (assignment expression).

29

• University of London
• Agile Software Projects
  • Data analysis, interpretation & presentation.
• Graphics Programming
  • p5.js: particle systems.
• Programming With Data
  • Python: data loading, storage and file formats with pandas.

28

• University of London
• Agile Software Projects
  • Data analysis, interpretation & presentation.
• Graphics Programming
  • p5.js: particle systems.
• Programming With Data
  • Python: working with different file formats in pandas.

27

• University of London
• Agile Software Projects
  • Introduction to research methods; data analysis, interpretation & presentation.
• Graphics Programming
  • Basics of physics engine: designing simple asteroid shooting game.

26

• University of London
• Agile Software Projects
  • Introduction to research methods.
• Programming With Data
  • Python: working with CSV with pandas.

25

• University of London
• Agile Software Projects
  • Introduction to research methods.
• Graphics Programming
  • Basics of physics engine: collision detection.
• Programming With Data
  • Python: accessing text corpora and lexical resources with NLTK, matplotlib heatmaps; working with data.
• Review: @property in Python.

24

• University of London
• Agile Software Projects
  • Introduction to research methods.
• Graphics Programming
  • Basics of physics engine: collision detection.

23

• University of London
• Agile Software Projects
  • Introduction to research methods.
• Graphics Programming
  • Basics of physics engine: collision detection.

22

• University of London
• Agile Software Projects
  • Capturing interaction with use cases; modelling requirements; user-centred design; quantitative and qualitative research.

20

• University of London
• Graphics Programming
  • Basics of physics engine: forces; gravity & friction.

19

• University of London
• Graphics Programming
  • 2D transformations, basics of physics engine.
• Agile Software Projects
  • Requirements gathering; creating personas; scenarios; design fiction.

18

• University of London
• Programming With Data
  • Python: inverse document frequency, accessing text corpora and lexical resources with NLTK.
• Graphics Programming
  • Vector arithmetic.
• Agile Software Projects
  • Requirements and specification; requirements gathering.
• Pluralsight: So You Want to Be an Entrepreneur?; Unit Testing with Python.

17

• University of London
• Programming With Data
  • Regular expressions; data handling with dictionaries.
• Graphics Programming
  • 2D transformations.
• Agile Software Projects
  • Requirements and specification; requirements gathering.
• Pluralsight: Prioritizing Tasks and Managing Time for Greater Productivity; So You Want to Be an Entrepreneur?.

16

• University of London
• Programming With Data
  • Python: natural language processing with NLTK.
• Pluralsight: Docker and Kubernetes: The Big Picture; The Future of Technology Careers; Learning Technology in the Information Age; Prioritizing Tasks and Managing Time for Greater Productivity.

15

• University of London
• Programming With Data
  • Python: currying (partial argument application); generators; itertools module; errors and exception handling; working with files.
• Graphics Programming
  • Review 2D transformations.
• Agile Software Projects
  • Project management; tracking progress, Gantt charts; managing resources and time; version control systems.

14

• University of London
• Programming With Data
  • Python: data structures and sequences.
• Agile Software Projects
  • Design principles; user experience.
• Graphics Programming
  • Object Oriented Programming in Javascript (OOP); vectors.

13

• University of London
• Programming With Data
  • Python: conditional logic; lists, loops & iterations; functions libraries and dependencies.
• Agile Software Projects
  • User experience; accessibility, usability.
• Graphics Programming
  • p5.js: 2D transformations.

12

• University of London
• Programming With Data
  • Introduction to development environments in Python; Jupyter Notebook.
• Agile Software Projects
  • Basics of interaction design; user experience.
• Graphics Programming
  • p5.js: 2D transformations.

11

• Book Python for Data Analysis: data aggregation; groupby’s apply method; quantile and bucket analysis; pivot tables and cross-tabulation; time series.

10

• Book JavaScript Definitive Guide: in operator; instanceof operator; logical expressions (&&, ||, !); assignment expressions; evaluation expressions with eval(); conditional operator (?:); first-defined operator (??); typeof operator; delete operator; await operator.
• Book Python for Data Analysis: pandas’ groupby.

9

• Python Fire package.

8

• Book Python for Data Analysis: introduction to matplotlib; plotting with pandas and seaborn; data aggregation and group operations.
• Book JavaScript Definitive Guide: relational expressions.

7

• Book Python for Data Analysis: figures and subplots.
• Book JavaScript Definitive Guide: unary arithmetic operators; bitwise operators.

6

• Book JavaScript Definitive Guide: expressions and operators.
• Book Python for Data Analysis: pivoting data; plotting and visualization.

5

• C: structures and unions.
• Book Python for Data Analysis: string manipulation; data wrangling (join, combine, and reshape); reshaping and pivoting.
• Book JavaScript Definitive Guide: expressions and operators.

4

• Book Python for Data Analysis: introduction to pandas; correlation & covariance; data loading, storage, and file formats; binary data formats; interacting with web APIs; interacting with databases; data cleaning and preparation; data transformation; string manipulation.
• Book JavaScript Definitive Guide: type conversions; variable & constant scope and declaration; destructuring assignment; expressions and operators.

3

• S.O.L.I.D. principles, Digital Ocean.
• Book Python for Data Analysis: introduction to pandas.

2

• Book JavaScript Definitive Guide: template literals; pattern matching; null & undefined; symbols; global object; conversions and equality.

1

• Book JavaScript Definitive Guide: lexical structure; types, values, and variables.
• Book Python for Data Analysis: introduction to pandas.

September

30

• Book Python for Data Analysis: file input and output with arrays; linear algebra; introduction to pandas.
• Book JavaScript Definitive Guide: lexical structure.

29

• Book Python for Data Analysis: NumPy basics arrays and vectorized computation; array-oriented programming with arrays.
• Book JavaScript Definitive Guide: Introduction to JavaScript; lexical structure.

28

• Foundations of Data Science: K-Means Clustering: using the Pandas library to read, sort and filter data; plotting and labelling data; interpreting data; organizing the work.
• Book Python for Data Analysis: built-in data structures; currying; files and the operating system; bytes and Unicode with files; NumPy basics arrays and vectorized computation.

27

• Foundations of Data Science: K-Means Clustering: Working with multidimensional data in Python; using the Pandas library to read, sort and filter data.
• Book Python for Data Analysis: Review basics of Python; built-in data structures.

26

• Foundations of Data Science: K-Means Clustering: Clustering with K-Means; supervised vs unsupervised learning; mean, variance & standard deviation; Jupyter Notebooks; multidimensional data (matrices); covariance; normalization.
• Book Python for Data Analysis: Review basics of Python.

25

• BFG Repo-Cleaner, i18n JavaScript library.

24

• NumPy Fundamentals.

22

• University of London
• Fundamentals of Computer Science
  • Review: proof by contradiction; counting; Chomsky Normal Form; finite state machines; regular expressions; Turing machines; pigeonhole principle; permutations; combinations.

21

• University of London
• Fundamentals of Computer Science
  • Review: Propositional logic; tautology; De Morgan’s law; quantifiers; direct proof; proof by contradiction.

20

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and playlist library.

19

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and playlist library.

18

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and playlist library.

17

• University of London
• Fundamentals of Computer Science
  • Review: Negating formulae; Turing machines; combinatorics.
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and loading files.

16

• University of London
• Fundamentals of Computer Science
  • Review: Negating formulae; Turing machines.
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and loading files.

15

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Working with XML and loading files.

14

• University of London
• Algorithms and Data Structures II
  • Review: trees; binary search trees; heaps; graphs; recurrence relations.
• Theming with CKAN.

13

• University of London
• Algorithms and Data Structures II
  • Review: linked lists; stacks; queues; trees; depth-first traversal; binary search trees; heaps; heapsort; graphs; minimum spanning trees; Dijkstra’s algorithm.
• Getting started with Nvim + LSP.
• Configuring Passwordless Server Login Using SSH.

12

• University of London
• Algorithms and Data Structures II
  • Review: Comparison and non-comparison sorting algorithms (selection sort, quicksort, mergesort, counting sort, radix sort, bucket sort); hashing; collisions in hash tables; linked lists.
• Using a backslash to bypass Bash aliases on the command-line.

11

• University of London
• Algorithms and Data Structures II
  • Review: Comparison and non-comparison sorting algorithms.

10

• University of London
• Algorithms and Data Structures II
  • Review: Recurrence equations; master theorem.
• Vim: review dot command.

9

• University of London
• Algorithms and Data Structures II
  • Review: Structure of recursive algorithms; time complexity of recursive algorithms; recurrence equations.
• Vim: Review Fugitive.vim - working with the git index - Vimcasts.

8

• University of London
• Algorithms and Data Structures II
  • Review: Growth of functions; worst and best cases; asymptotic analysis.
• Vim: Review Using :argdo to change multiple files - Vimcasts.

7

• University of London
• Algorithms and Data Structures II
  • Review: Augmented BST; time & space complexity.

6

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Add features to speed component, adjusting playback.
• Hosting a website from home.
• Node’s Streams.
• Event loop in JavaScript.

5

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Refactor code into separate components; customize layout and buttons.
• Career Planning micro-module.

3

• University of London
• Algorithms and Data Structures II
  • Review: Building heaps in place; augmented binary search trees.

2

• University of London
• Algorithms and Data Structures II
  • Review: Sorting algorithms; hash table collisions with linear probing; binary search tree.

1

• University of London
• Algorithms and Data Structures II
  • Review: Minimum spanning trees; Dijkstra’s algorithm.
• Review: basics of Node.js.
• Docker to Serverless.

August

31

• University of London
• Fundamentals of Computer Science
  • Review: Context-free grammar and languages.

30

• University of London
• Algorithms and Data Structures II
  • Review: Prim’s and Kruskal’s algorithms.
• Object-Oriented Programming
  • C++ (JUCE framework): Customize layout and buttons.
• Production Logs: How To Get The Most Out Of Them - Semicolon&Sons
• Review Python: OOP paradigm; polymorphism; classes; class properties.

29

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Customize layout and buttons.

28

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Customize layout and buttons.

27

• University of London
• Algorithms and Data Structures II
  • Dijkstra’s algorithm; minimum spanning trees.

26

• University of London
• Fundamentals of Computer Science
  • Review: Context-free grammar and languages.

25

• University of London
• Algorithms and Data Structures II
  • Minimum spanning trees (Kruskal’s algorithm); Dijkstra’s algorithm.
• Fundamentals of Computer Science
  • Review: GNFA; regular expressions; nonregular languages; pumping lemma.

24

• University of London
• Fundamentals of Computer Science
  • Review: Regular expressions.

23

• University of London
• Object-Oriented Programming
  • C++ (JUCE framework): Customize labels and components.

20

• University of London
• Algorithms and Data Structures II
  • Minimum spanning trees (Prim’s algorithm, Kruskal’s algorithm).

19

• University of London
• Fundamentals of Computer Science
  • Review: Context-free languages; regular languages.

18

• University of London
• Algorithms and Data Structures II
  • Priority queues, implementing heaps in C++, introduction to graphs.

17

• University of London
• Fundamentals of Computer Science
  • Review: Finite automata; regular expressions.

16

• University of London
• Object-Oriented Programming
  • C++: Refactoring a project with the JUCE framework.

15

• University of London
• Object-Oriented Programming
  • C++: Reviewing how to set up a project with the JUCE framework.

13

• University of London
• Algorithms and Data Structures II
  • Heaps, heapsort and priority queues.

12

• University of London
• Algorithms and Data Structures II
  • Review: Heaps.
  • Heapsort & its time complexity.

11

• University of London
• Algorithms and Data Structures II
  • Review: Recurrence relations; master theorem.

10

• University of London
• Algorithms and Data Structures II
  • Review: Merge sort; divide & conquer; recurrence relations; master theorem.
• Fundamentals of Computer Science
  • Review: Searching: sequential search; binary search; comparison trees; Fibonacci search.
  • Review: Sorting: selection sort; insertion sort; bubble sort; quick sort; heap sort; merge sort; shell sort; radix sort.

9

• University of London
• Algorithms and Data Structures II
  • Operations in heaps.
• Object-Oriented Programming
  • C++: work with a table component (JUCE framework).

8

• University of London
• Fundamentals of Computer Science
  • Review: Merge sort; the algorithm of happiness (Lloyd Shapley); review time complexity of algorithms & Big O notation; recursion.

6

• University of London
• Fundamentals of Computer Science
  • Recursive algorithms; recursion & iteration.

5

• University of London
• Object-Oriented Programming
  • C++: implement a moveable play head with a timer (JUCE framework); threads; add a playlist component with a table; implement a play button.

4

• University of London
• Object-Oriented Programming
  • C++: implement a moveable play head (JUCE framework).

3

• University of London
• Algorithms and Data Structures II
  • Binary search trees; heaps.

2

• University of London
• Fundamentals of Computer Science
  • Greedy algorithms, halting problem, binary search, heap sort, recursion, quick sort, merge sort.
• How to Avoid Dumb Code Mistakes Part II

1

• University of London
• Object-Oriented Programming
  • C++: implement a waveform display (JUCE framework).
• 21 JavaScript tricks with Aaron Jack.
• Using VS Code debugger.

July

31

• University of London
• Object-Oriented Programming
  • C++: Member initializer lists; implement a waveform display (JUCE framework).

30

• University of London
• Algorithms and Data Structures II
  • Binary search trees.

29

• University of London
• Fundamentals of Computer Science
  • Review: Of simple algorithms for sorting.

28

• University of London
• Object-Oriented Programming
  • C++: Virtual functions; polymorphism; creating a new GUI component class for audio player control; implementing drag and drop.

27

• University of London
• Algorithms and Data Structures II
  • Binary search trees.

26

• University of London
• Fundamentals of Computer Science
  • Review: Chomsky Normal Form.
  • Turing machines, review of simple algorithms for sorting.
• How To Make Less Dumb Mistakes When Programming.

25

• University of London
• Fundamentals of Computer Science
  • Context-free grammar & languages; parse trees; ambiguity in grammars & languages; Chomsky Normal Form.

24

• Creating a Chrome extension.

23

• University of London
• Object-Oriented Programming
  • C++: pointers & references; audio playback with the JUCE framework.

22

• University of London
• Algorithms and Data Structures II
  • Trees and their implementation; binary search trees.

21

• University of London
• Fundamentals of Computer Science
  • Regular languages; context-free grammar; Chomsky Normal Form.

20

• University of London
• Fundamentals of Computer Science
  • Grammar, regular languages.

19

• University of London
• Algorithms and Data Structures II
  • Stacks; queues.
• Object-Oriented Programming
  • C++: Inheritance and virtual functions; audio play back & basic synthesis; sound synthesis in JUCE; play back of files; module coupling; module cohesion; pointers & references.

18

• University of London
• Fundamentals of Computer Science
  • Review: Propositional logic; formal proofs; permutations & combinations; automata theory; regular expressions; pumping lemma.
  • Context-free grammar.

17

• University of London
• Fundamentals of Computer Science
  • Context-free grammar.

16

• University of London
• Algorithms and Data Structures II
  • Introduction to data structures; linked lists; stacks.

15

• University of London
• Object-Oriented Programming
  • C++: JUCE application and macros; event listener pattern; implement a slider listener; pure virtual functions.

14

• University of London
• Object-Oriented Programming
  • C++: Getting started with JUCE framework; adding a GUI widget; inheritance; arrays; for loops.

13

• University of London
• Object-Oriented Programming
  • C++: Getting started with JUCE framework.
• Helm package manager.

12

• University of London
• Object-Oriented Programming
  • C++: Arrays; implementing an Exponential Moving Average (EMA) algorithm; using std::chrono; maps.

11

• University of London
• Object-Oriented Programming
  • C++: this keyword; stack & scope lifetime; arrow operator; optimizing usage of std::vector; auto keyword; casting; logging.

10

• University of London
• Object-Oriented Programming
  • C++: How the compiler works; how linking works; variables; functions; loops; pointers; references; classes; structs; static vs local static; enums; constructors; destructors; inheritance; virtual functions; interfaces; visibility; arrays; strings; string literals; const keyword; mutable keyword; ternary operators; instantiate objects; new keyword; implicit conversion; explicit keyword; operator overloading.

9

• University of London
• Object-Oriented Programming
  • Control flow, setting up projects with Makefiles.

8

• University of London
• Object-Oriented Programming
  • Integrating new classes into skeleton project; functions and user input.

7

• University of London
• Algorithms and Data Structures II
  • Review: Hashing algorithms.

6

• University of London
• Algorithms and Data Structures II
  • Review: Hashing algorithms.

5

• University of London
• Algorithms and Data Structures II
  • Review: Hashing algorithms.
• Continuous Integration Testing: Basics + What to Test.

4

• University of London
• Algorithms and Data Structures II
  • Growth of functions; Asymptotic notation; time complexity.

3

• Publishing a package to PyPi through continuous integration.

1

• University of London
• Algorithms and Data Structures II
  • Growth of functions; Asymptotic notation; time complexity.

June

30

• University of London
• Algorithms and Data Structures II
  • Growth of functions; Asymptotic notation; time complexity.

29

• University of London
• Algorithms and Data Structures II
  • Growth of functions; Asymptotic notation; time complexity.

28

• University of London
• Algorithms and Data Structures II
  • Growth of functions; Asymptotic notation; frequency count method; time complexity.

27

• University of London
• Fundamentals of Computer Science
  • Review: Propositional logic; proof by contrapositive; combinations & permutations; finite automata; regular expressions.

25

• University of London
• Object-Oriented Programming
  • Refactoring C++ application.
• How to Publish an Open-Source Python Package to PyPI.

24

• University of London
• Object-Oriented Programming
  • Refactoring C++ application.

23

• University of London
• Algorithms and Data Structures II
  • Hashing algorithm.
• Fundamentals of Computer Science
  • Non-regular languages & pumping lemma.

22

• University of London
• Fundamentals of Computer Science
  • Regular expressions & finite automata, regular & non-regular languages; pumping lemma.

21

• University of London
• Fundamentals of Computer Science
  • Regular expressions.
• Object-Oriented Programming
  • Matching algorithm: implementing print and remove functions in wallet; static and non-static functions.
• Setting up Python language server with coc.nvim.
• Tip: Opening file in Neovim from CLI with nvim **, then <TAB> to fuzzy search.

20

• University of London
• Object-Oriented Programming
  • Implementing matching algorithm for cryptocurrency trading; testing the matching algorithm; implementing “wallet” functionality.
• Review: Absolute vs relative imports in Python; property-based testing with Hypothesis; Mocking and Patching Pitfalls.

19

• Python with pytest: Mocking; fixtures; monkeypatching.

18

• University of London
• Object-Oriented Programming
  • C++: Appending to vectors and sorting them.

17

• University of London
• Algorithms and Data Structures II
  • Open addressing.
• Object-Oriented Programming
  • C++: Reading user input; exception handling.

16

• University of London
• Algorithms and Data Structures II
  • Hash functions (division method, multiplication method), open addressing (linear probing, quadratic probing, double hashing).

15

• University of London
• Algorithms and Data Structures II
  • Collisions in hash tables (extend & re-hash, linear probing, separate chaining).

14

• University of London
• Algorithms and Data Structures II
  • Hash tables, collisions in hash tables (extend & re-hash, linear probing, separate chaining).

13

• University of London
• Algorithms and Data Structures II
  • Introduction to hashing and hash tables.
• Fundamentals of Computer Science
  • Automata theory (regular operations [union, concatenation]), nondeterminism.

12

• Setting up CKAN and working with its API.

11

• Setting up Apache Airflow and creating basic DAGs.

10

• University of London
• Fundamentals of Computer Science
  • Automata theory (finite automata, designing them).

9

• University of London
• Fundamentals of Computer Science
  • Combinatorial principles; automata theory (finite automata).

8

• Managing various Git and SSH identities.

7

• University of London
• Fundamentals of Computer Science
  • Combinations & permutations.
• TDD: pytest patterns and examples.

6

• University of London
• Fundamentals of Computer Science
  • Combinations & permutations.
• C fundamentals: Strings & function pointers.

4

• University of London
• Algorithms and Data Structures II
  • Bucket sort.
• Fundamentals of Computer Science
  • Permutations; combinations.
• Manage multiple SSH keys at once. Configuration with IPv6.

3

• University of London
• Algorithms and Data Structures II
  • Radix sort; bucket sort.
• TDD: Debugging with pdb using pytest; using Coverage.py; raising errors in parametrized tests.

2

• University of London
• Algorithms and Data Structures II
  • The limits of comparison sorts; lower bounds for comparison sorts; sorting in linear time; counting sort; radix sort.
• Object-Oriented Programming
  • Iterating over vectors and memory.
• TDD: Pytest configuration; test discovery; use with pdb.

1

• University of London
• Object-Oriented Programming
  • Implementing basic statistical functions; operator overloading.
• Scripting with Rofi.

May

31

• University of London
• Fundamentals of Computer Science
  • Combinations & permutations.
• Object-Oriented Programming
  • Modularizing program, constructor functions.
• Vim: Jumps ('", '', ``) & other movements.
• Agile Manifesto Principles.

30

• More on Git grep.
• TDD: Unit Tests (from Clean Code, by Robert C. Martin); Unit Tests in Python (Socratica); review parametrization, fixtures, setup/teardown.

29

• TDD: Agile in Practice; Testing your Python Code with PyTest | Scipy 2019 Tutorial.

28

• University of London
• Fundamentals of Computer Science
  • Counting: subtraction rule; division rule; pigeonhole principle; permutations & combinations.

27

• University of London
• Fundamentals of Computer Science
  • Combinations; basic counting principles (product rule, sum rule).
• TDD: Mocking instances of a class.

26

• University of London
• Algorithms and Data Structures II
  • Review: Of comparison sorting algorithms.
• Fundamentals of Computer Science
  • Permutations, combinations.
• How to save patch from Git stash with git stash show -p > patch.
• TDD: Read Python Testing with pytest (plugins).

25

• University of London
• Algorithms and Data Structures II
  • Analyzing divide-and-conquer algorithms.

24

• University of London
• Algorithms and Data Structures II
  • Merge sort.
• TDD: Read Python Testing with pytest (builtin fixtures [tmpdir, pytestconfig, cache, capsys, monkeypatch, doctest_namespace, recwarn]).

23

• University of London
• Algorithms and Data Structures II
  • Analysis of insertion sort; selection sort; quicksort; performance of quicksort; mergesort.
• TDD: Read Python Testing with pytest (parametrize tests; fixtures; fixture scope; parametrize fixtures).

22

• TDD: Read Python Testing with pytest (markers; fixtures; running subset of tests).

21

• University of London
• Algorithms and Data Structures II
  • Insertion sort; analyzing algorithms.

19

• University of London
• Algorithms and Data Structures II
  • Overview of sorting algorithms and bubble sort.

18

• University of London
• Object-Oriented Programming
  • File parsing functions.
• Fundamentals of Computer Science
  • Combinatorial principles, pigeonhole principle.

17

• University of London
• Object-Oriented Programming
  • Linking header files.

16

• University of London
• Object-Oriented Programming
  • Tokenise output; exception handling.
• Learn about Deno (Node.js replacement).
• TDD: Read Python Testing with pytest (getting started; marking test functions).

14

• University of London
• Algorithms and Data Structures II
  • Recurrence relations; master theorem.
• Object-Oriented Programming
  • Implementing a tokenise function.

13

• University of London
• Algorithms and Data Structures II
  • Recurrence relations; master theorem.

12

• University of London
• Fundamentals of Computer Science
  • Proofs: direct; by contraposition; by contradiction.
  • Propositional logic; predicate logic.

11

• University of London
• Algorithms and Data Structures II
  • Solving recurrences: substitution method; recursion-tree method; master method.
• Fundamentals of Computer Science
  • Inductive proof.

10

• University of London
• Object-Oriented Programming
  • Implementing a tokenise function.

9

• University of London
• Algorithms and Data Structures II
  • Recurrence relations, Strassen’s algorithm.
• Configure FZF, COC.nvim and UltiSnips with Neovim.

7

• University of London
• Algorithms and Data Structures II
  • Master theorem; recurrence relations.

6

• University of London
• Algorithms and Data Structures II
  • Master theorem; recurrence relations.

5

• University of London
• Algorithms and Data Structures II
  • Analysis of recursive algorithms.

4

• University of London
• Object-Oriented Programming
  • Classes and functions.
• Downgrading packages by date with aptitude.

3

• Managing dotfiles with bare Git repository.

2

• University of London
• Object-Oriented Programming
  • Creating classes.

2

• University of London
• Object-Oriented Programming
  • Creating classes.

1

• University of London
• Object-Oriented Programming
  • Creating classes.
• Git: using submodules and Git LFS; move files from one repo to another while preserving history.
• Vim review: text motions; tags; jump list; change list.

---

April

30

• University of London
• Object-Oriented Programming
  • Using header files and making a class with functions.

29

• University of London
• Fundamentals of Computer Science
  • Proof by contradiction.
• Object-Oriented Programming
  • Using header files and making a class with functions.

28

• University of London
• Algorithms and Data Structures II
  • Structure of recursive algorithms; writing recursive algorithms.
• Fundamentals of Computer Science
  • Formal proof.

27

• University of London
• Algorithms and Data Structures II
  • Structure of recursive algorithms.
• Object-Oriented Programming
  • Floating-point numbers.
• Vim: mksession.

26

• University of London
• Fundamentals of Computer Science
  • Predicates and quantifiers; nested quantifiers; First-order logic; introduction to proofs.

25

• University of London
• Fundamentals of Computer Science
  • Logical equivalences.
• Object-Oriented Programming
  • Variables; data types: strings, enum class, vectors, numbers; introduction to classes: constructors.

24

• Datopian
• Parsing files in Python with regular expressions & exporting to CSV.
• University of London
• Object-Oriented Programming
  • Types; floating-point numbers.

23

• Datopian
• Deploying to Heroku; customize CKAN frontend.
• University of London
• Algorithms and Data Structures II
  • Introduction to asymptotic analysis.
• Fundamentals of Computer Science
  • First-order logic; propositional equivalences.

22

• Datopian
• Working with dataflows.
• University of London
• Algorithms and Data Structures II
  • Worst and best case analysis; introduction to asymptotic analysis.

21

• Datopian
• Review: Vim regular expressions.
• University of London
• Algorithms and Data Structures II
  • Growth of functions.
• Fundamentals of Computer Science
  • Propositional logic; tautology.
• Object-Oriented Programming
  • Text-based programs; more on functions.

20

• Datopian
• Review: Vim regular expressions.
• University of London
• Algorithms and Data Structures II
  • Introduction to the analysis of algorithms; growth of functions.
• Object-Oriented Programming
  • Introduction to C++; extraction operator.

19

• Pandas: working with dates and time series data, reading/writing data to different sources - Excel, JSON, SQL.

18

• Pandas: updating rows and columns; add/remove rows and columns; sorting; grouping and aggregating, cleaning data.

17

• Pandas: unpivot data; creating dataframes from CSV files; set, reset, and use indexes; using conditionals to filter rows and columns; updating rows and columns.
• Use and configure spectrwm.

16

• Datopian
• Publishing to DataHub.io with data-cli; learning about CKAN, trying a local instance, getting to know the API and building an extension.

15

• Datopian
• Job stories and Data Packages; updating NodeJS as part of the package manager on Debian.

14

• Datopian
• Job stories; GTD methodology.
• Converting regex patterns in search & replace to uppercase in VS Code (stackoverflow answer).
• Fixing launch error with Yarn (stackoverflow answer).
• How to properly rebase a branch (jeffkreeftmeijer.com).

13

• Datopian
• GitLab: maintaining various GPG/SSH keys for different accounts; working with merge requests.
• Creating functional timesheet with Google Sheets.

12

• Datopian
• Review: GitLab (from GitLab): Git flow; GitHub flow; GitLab flow; merge requests; issue tracking; squashing; committing; testing commits; feature branches.
• Read Style Guides.
• Importing Data: Python Data Playbook.
  • Importing text with NumPy.
  • Importing CSV with csv module and Pandas.
• Read Git team workflows: merge or rebase? from Atlassian.
• Review: A Note About Git Commit Messages from Tim Pope.
• Review: Linux Command Line Pipes and Redirection.

11

• Datopian
• Review: Python: Beyond the Basics on Pluralsight.
• Setting up multiple email addresses with Git.

10

• Datopian
• Review: Python: Beyond the Basics on Pluralsight.
  • Inheritance and subtype polymorphism: object class.
  • Implementing collections: collection construction; container protocol (in, not in, __contains__()); sized protocol (len, __len__()); iterable protocol (iter(), __iter__()); sequence protocol (dunder add, mul, rmul, reversed); set protocol ().
  • Exceptions & errors: specifying exceptions; payloads; defining new exceptions; chaining exception; chaining exceptions; tracebacks; assertions.
  • Defining context managers: protocol (__enter__(), __exit__()); contextlib.
  • Introspection: object types; introspecting objects & scopes; inspect module.
• Setting boot parameters to fix occasional screen tear (Debian boot option used: intel_idle.max_cstate=4 in /etc/default/grub and Option "TearFree" "true" in 20-intel.conf).

9

• Datopian
• Review: Python: Beyond the Basics on Pluralsight.
  • Numeric and scalar types: complex, abs; number base conversions; datetime; time; timedelta; time zones; computational geometry.
  • Iterables & iteration: list, dictionary, set & generator comprehensions; nested comprehensions; map(); filter(); functools.reduce().
  • Inheritance and subtype polymorphism: isinstance(); issubclass(); multiple inheritance; super(); bound proxies.

8

• Datopian
• Review: Python: Beyond the Basics on Pluralsight.
  • Functions: extended argument syntax (*args, **kwargs); forwarding arguments.
  • Closures & decorators: local functions; returning functions; function factories; nonlocal keyword; decorators; functools.wrap(); validating arguments.
  • Properties & class methods: class attributes; @staticmethod; @classmethod; inheritance, @property.
  • Strings and representations: repr; str; format; reprlib; ascii; ord; chr.
  • Numeric and scalar types: int, float; decimal; fraction; complex.

7

• Datopian
• Review: Python: Beyond the Basics on Pluralsight.
  • Organizing larger programs: packages; subpackages; imports; namespaces.
  • Functions: callable objects; calling classes; lambdas; arguments.

6

• Datopian
• Python on Pluralsight.
  • Metaclasses: class allocation and initialisation; keyword arguments; __prepare__; __new__; __init__; inheritance.
  • Class decorators: enforcing class invariant; wrapping properties; chaining class decorators.
  • Abstract base classes basics.
• Best Practices for Code Quality: PEP8; Pylint; Pycodestyle; documenting a project with Sphinx and Apidoc; introduction to type checking.
• Managing Python Packages and Virtual Environments: manage packages with pip; setting up virtual environments; short introduction to tox; virtualenvwrapper; pipenv; poetry.

5

• Datopian
• Python on Pluralsight.
  • Byte-oriented programming overview: Memory-mapped files.
  • Objects internals and custom attributes overview: __dict__; __getattr__(); __hasattr__(); __delattr__(); __setattr__(); attribute storage; __getattribute__.
  • Descriptors overview: properties; implementation.
  • Instance creation overview: allocation with __new__().
• Setting up virtual environments properly in Neovim.

4

• Configuring Markdown Preview in Neovim.
• Using Kali Linux to monitor nearby wireless devices.
• Using Neovim as difftool with Git.

3

• Datopian
• Python on Pluralsight.
  • Byte-oriented programming: memoryview type; memory-mapped files.
• Setting up coc.nvim autocompletion in Neovim.

2

• Datopian
• Python on Pluralsight
  • Advanced flow control: loop-else clauses [nobreak]; try-else clauses; emulating switch statements; dispatching on types (singledispatch decorator).
  • Byte-oriented programming: bitwise operations on integers; byte-wise operations with integers; bytearray type; struct module.
• Setting up Neo Vim plugin in VS Code.

1

• Datopian
• JavaScript review: more on equality.
• Python review: basic data types (numbers, strings); lists; dictionaries; Pandas Tutorial (Part 1): Getting Started with Data Analysis.

---

March

31

• Datopian
• JavaScript review: more on coercion; equality.

30

• Datopian
• JavaScript review: coercion.
• Review: Regular expressions and SVG concepts.

29

• Datopian
• Getting started with CI.
• JavaScript review: primitive types; typeof, NaN, isNaN; type checking.

28

• Datopian
• JavaScript: factory functions; prototype chain; hasOwnProperty; this, new and class keywords.

27

• Datopian
• JavaScript: classes & prototypes; object dot notation.
• Desktop environments: Setting up KDE Plasma 5.18 and GNOME 3.36.

26

• Datopian
• JavaScript: asynchronous features; web API; event loop; promises.
• Desktop environment: XFCE.

25

• Window manager: bspwm.

24

• Datopian
• Python: data wrangling with xlrd Python module; tabular data packages.

23

• Datopian
• JavaScript: closures.
• Extending st terminal with patches.

21

• Datopian
• JavaScript: arrow functions; closures.

20

• Datopian
• Chrome developer tools: Node.js profiling; performance monitoring; image performance; page jank; memory leaks.
• JavaScript: thread of execution; functions (higher order, arrow); call stack.

19

• Datopian
• Chrome developer tools: auditing.
• Review: Bash: exit codes and logical operators.

18

• Datopian
• JavaScript: more on scope; callbacks.
• Chrome developer tools: editing; DOM breakpoints; debugging; networking.

17

• Datopian
• JavaScript: objects & arrays; destructuring; scoping.
• General overview of React, Angular & Vue.
• Setting up Awesome window manager.

16

• Datopian
• JavaScript: types & coercion; scope; this & Prototypes.
• Review: Git: rebase & amend; forks & remote repos; destroy & recover.
• Review: JavaScript: events & listeners; event delegation; AJAX; Node.js.

15

• Datopian
• Review: Git: data storage model; areas & staging; stashing; references, commits & branches; merging & rebasing; history & diffs; fixing mistakes; rebase & amend.
• Sass: functions; control flow; data structures (maps); BEM.
• VS Code: code navigation & refactoring; type checking; debugging.

14

• CSS: background and borders; gradients; transforms; transitions; animations; columns; SVG masking.
• Sass: basics; variables; mixins; functions.

13

• CSS: generated content; media queries; working with colors; flexbox; tables; grids.
• Review: Emmet.

12

• CSS: selectors; pseudo-elements; generated content.

11

• CSS: grids; grid-area; grid offsets; grid fallbacks; selectors; levels; specificity.

10

• CSS: flexbox re-ordering; responsive images; CSS grids.
• JavaScript/TypeScript: type checking.

9

• CSS: flexbox grid.
• Linux file permissions & attributes.
• VS Code: setting up formatting & debugging tools.

6

• CSS: floats; flexbox.

4

• University of London
• Algorithms and Data Structures I
  • Review: General overview of past topics.

3

• University of London
• Algorithms and Data Structures I
  • Review: General overview of past topics.

2

• University of London
• Discrete mathematics
  • Review: Mathematical induction; strong induction; recursion; recurrence relations; graph theory; relations; equivalence relations; combinatorics.

1

• University of London
• Discrete mathematics
  • Review: Set theory; functions; propositional logic; predicate logic; rules of inference; Boolean algebra; logic gates; Karnaugh maps; mathematical induction.

---

February

29

• University of London
• Discrete mathematics
  • Review: Equivalence relations; graphs; trees.

28

• University of London
• Algorithms and Data Structures I
  • Review: Growth of functions; Big O notation; analysing algorithms; binary search.
• Discrete mathematics
  • Review: Recurrence relations; inclusion-exclusion principle; relations & matrices; relations with digraphs; equivalence relations.

27

• University of London
• Discrete mathematics
  • Review: Mathematical induction; well ordering principle; strong induction; recursion; structural induction; permutations & combinations; binomial theorem; Pascal’s identity.

26

• University of London
• Algorithms and Data Structures I
  • Review: Bubble sort; insertion sort; RAM model; growth of functions.
• Discrete mathematics
  • Review: Proofs of existence and uniqueness; sets; set operations; set identities; proving set identities; functions; sequences; recurrence relations; summations; matrices; optimization (greedy) algorithms.

25

• University of London
• Algorithms and Data Structures I
  • Review: Bubble sort.
• Discrete mathematics
  • Review: Propositional logic; propositional equivalences; predicates & quantifiers; nested quantifiers; inference with quantifiers; direct proof; proof by contraposition; proof by contradiction; proof by cases.
• Set up XAMPP with a Laravel project.

24

• University of London
• Algorithms and Data Structures I
  • Review: Stacks; linked lists; arrays; Heron’s method algorithm; vectors; queues; dynamic arrays; linear search algorithms.
• Discrete mathematics
  • Review: Graph theory; relations; trees; sets; logic & sequences.

23

• University of London
• Discrete mathematics
  • Review: Dijkstra’s, Prim’s & Kruskal’s algorithms.

22

• University of London
• Algorithms and Data Structures I
  • Review: Arrays; vectors; stacks; queues; linked lists.
• Discrete mathematics
  • Review: Functions; induction; graph theory; trees; relations.
• Creating a light/dark theme toggle with CSS and JavaScript.

21

• University of London
• Algorithms and Data Structures I
  • Review: Worst-case time complexity in search algorithms.
• Discrete mathematics
  • Review: Sets operations; relations; propositional logic; functions; Boolean algebra; recursion.

20

• University of London
• Algorithms and Data Structures I
  • Review: Search algorithms and their JavaScript implementation; time complexity and big-O notation.
• Discrete mathematics
  • Review: Circuits in Boolean algebra; mathematical induction; K-maps.

19

• University of London
• Algorithms and Data Structures I
  • Review: Binary search; recursive algorithms.
• Discrete mathematics
  • Review: Boolean algebra; mathematical induction (proofs by contradiction, direct proofs, false proofs, well ordering principle).

18

• University of London
• Algorithms and Data Structures I
  • Review: Linear data structures.
• Discrete mathematics
  • Review: Boolean identities; definition of Boolean algebra; functional completeness; logic gates; adders; minimization of circuits; Karnaugh maps; Quine–McCluskey method.
• Python review: built-in type gotchas.

17

• University of London
• Algorithms and Data Structures I
  • Review: Binary search algorithm; linked lists; linear search.
• Discrete mathematics
  • Review: Rules of inference; fallacies; rules of inference for quantified statements; Boolean identities.

16

• University of London
• Discrete mathematics
  • Review: Nested quantifiers; negating nested quantifiers; rules of inference.

15

• University of London
• Algorithms and Data Structures I
  • Review: Searching algorithms.

14

• University of London
• Algorithms and Data Structures I
  • Review: Search algorithms (quickSort).
• Discrete mathematics
  • Review: Compound propositions; logical implication; converse, contrapositive & inverse; equivalence; laws of propositional logic (including De Morgan’s laws); precedence of logical operators; definition of predicates; quantifiers; logical operators; nested quantifiers.

13

• University of London
• Discrete mathematics
  • Review: Function composition; bijections; floor & ceiling functions; definition of a proposition; truth tables & truth sets; compound propositions; logical implication; converse, contrapositive & inverse; equivalence; laws of propositional logic; De Morgan’s laws; precedence of logical operators.

12

• University of London
• Algorithms and Data Structures I
  • Review: Dynamic arrays; stacks.
• Discrete mathematics
  • Review: The domain and co-domain; the range; injection (one-to-one); surjection (onto); function composition; bijections; floor & ceiling functions.

11

• University of London
• Discrete mathematics
  • Review: Set operations; membership tables; Venn diagrams; the complement of a set; De Morgan’s laws; set properties; definition of function; the domain and co-domain; the range; injection (one-to-one); surjection (onto).

10

• University of London
• Discrete mathematics
  • Review: Definition of a set; elements of a set; cardinality of a set; listing method; set builder method; subsets of a set; power sets.
• Pluralsight courses: Working with JavaScript Modules; JavaScript Promises and Async Programming.

9

• Pluralsight courses: Advanced Git Tips and Tricks (rewriting history); Increasing Productivity by Beating Procrastination; Rapid ES6 Training; Practical Design Patterns in JavaScript (creational design patterns).

8

• Pluralsight courses: Advanced Git Techniques (submodules, custom commands, hooks, git bisect); Working with Git Branches; Rewriting Git History; Code School: Git Real 2; Advanced Git Tips and Tricks.
• Using all-contributors-bot on GitHub.

7

• Pluralsight courses: What Is TDD and Why It Is Not Unit Testing; Test-driven Development: The Big Picture; Agile Fundamentals; Advanced Git Techniques.

6

• University of London
• Discrete mathematics
  • Review: Permutations with/without repetition; distinguishable objects; binomial coefficients; Pascal’s identity.

5

• University of London
• Discrete mathematics
  • Review: Of counting rules: Inclusion-exclusion of sets; Pascal’s identity.
  • Review: Of probability: sample space & outcomes; probability space; probability trees; conditional probability.
• Introduction to Programming II
  • Showing and hiding DOM elements in p5.js based on events.

4

• University of London
• Discrete mathematics
  • Review: Lexicographic ordering; Hasse diagrams; linear recurrences; sequences; permutations; functions (surjective, bijective, injective); mapping rule; generalized pigeonhole principle; division rule; product rule; product of sets; sum rule.
• Introduction to Programming II
  • Review: Working with DOM elements with p5.js.

3

• University of London
• Algorithms and Data Structures I
  • Review: Queues; stacks.
• Discrete mathematics
  • Permutations, combinations, binomial theorem, Pascal’s identity, divide & conquer recurrences.
• Introduction to Programming II
  • Working with hamburger menus.
• Solve problem 49 on Project Euler: Prime permutations.

1

• University of London
• Algorithms and Data Structures I
  • Review: Heron’s algorithm; discretisation & pseudocode; vectors.
• Discrete mathematics
  • Combinatorics: product rule; addition rule; combining sum & product rules; subtraction rule (principle of inclusion-exclusion); division rule; pigeonhole principle; permutations & combinations.

---

January

31

• University of London
• Discrete mathematics
  • Partial orderings, lexicographic order, equivalence relations, parallel task scheduling, Hasse diagram, sums, asymptotic notation.

30

• University of London
• Algorithms and Data Structures I
  • Computational complexity, NP problems and searching.
• Discrete mathematics
  • Equivalence relations, equivalence classes, partial order, total order, n-ary relations, closures of relations.

29

• University of London
• Algorithms and Data Structures I
  • Complexity classes, P vs EXP vs NP.
• Discrete mathematics
  • Strong & weak partial orders, total order, Hasse diagram, binary relations, surjective and injective relations, equivalence relations, partitions, transitive closure.

28

• University of London
• Discrete mathematics
• Relations and their properties, equivalence relations, equivalence classes.

27

• University of London
• Discrete mathematics
  • Relations, relation properties (symmetry, transitivity, anti-symmetry, reflexivity), Cartesian product, relations using matrices, graph representations of a relation, transitive closure.

26

• University of London
• Algorithms and Data Structures I
• Computational complexity: complexity classes.

25

• University of London
• Discrete mathematics
• Trees: Prim’s algorithm; Kruskal’s algorithm; tree traversal; binary search trees.
• Solve problem 55 on Project Euler: Lychrel numbers.

24

• University of London
• Algorithms and Data Structures I
• Analysis of quickSort.
• Discrete mathematics
• Trees: Tree traversal; universal address system; traversal algorithms (preorder, inorder, postorder); spanning trees; depth-first search; breath-first search.
• Using git bisect and git bisect run.

23

• University of London
• Discrete mathematics
  • Graph theory: Dijkstra’s algorithm in real-world scenarios.
  • Trees: Definitions; spanning trees; minimum spanning trees (Kruskal’s algorithm, Prim’s algorithm); rooted trees; binary search trees; Huffman coding; game trees.
• Introduction to Programming II
  • p5.js: Loading many assets at once, managing waiting time with loading screen.

22

• University of London
• Algorithms and Data Structures I
  • Analysis of quickSort.
• Discrete mathematics
  • Graph theory: Dijkstra’s algorithm; bipartite graphs; matching.
• Solve problem 53 on Project Euler: Combinatoric selections.

21

• University of London
• Discrete mathematics
  • Graph theory: matching in bipartite graphs; graph isomorphism; adjacency matrices; connectivity; shortest-path algorithm.
• Introduction to Programming II
  • JavaScript: pass by value and by reference.

20

• University of London
• Algorithms and Data Structures I
  • Worst-case time complexity of quickSort and mergeSort.
• Discrete mathematics
  • Graph theory: bipartite graphs; adjacency list; adjacency matrix; Dijkstra’s algorithm.

19

• University of London
• Discrete mathematics
• Graph theory: Euler & Hamiltonian paths, shortest-path problems.

18

• University of London
• Algorithms and Data Structures I
  • Substitution cipher with mergeSort.
• Discrete mathematics
  • More on graph theory (common graphs, subgraphs, weighted graphs, adjacency matrices, matching).
• Introduction to Programming II
  • JavaScript modules, Callbacks, Promises, Async Await.

17

• University of London
• Algorithms and Data Structures I
  • Review: mergeSort algorithm.
• Discrete mathematics
  • Communication networks (including Butterfly and Benes), graph theory.

16

• University of London
• Algorithms and Data Structures I
  • mergeSort algorithm.
  • Review: quickSort algorithm and time complexity.
• Discrete mathematics
  • Graph theory and coloring, matching problems, spanning trees (ST) & minimum spanning trees (MST).
• Introduction to Programming II
  • Breakpoint and stepping.

15

• University of London
• Algorithms and Data Structures I
  • Call stack, quick sort algorithm.
• Discrete mathematics
  • Induction, strong induction.
• Python: review namespaces; scopes; decomposition (splits into functions, classes, modules, packages); encapsulation.

14

• University of London
• Algorithms and Data Structures I
  • Decrease & conquer, recursive Euclidean algorithm, recursive searching and sorting, permutations, recursive binary search.
  • Review: Insertion sort; bubble sort.
• Discrete mathematics
  • Isomorphic graphs, bipartite graphs, Hopcroft-Karp algorithm, introduction to proofs.
• Introduction to Programming II
  • JavaScript: Asynchronous function calls; using callbacks; error handling.

13

• University of London
• Algorithms and Data Structures I
  • Abstraction and linked lists, recursion.
• Discrete mathematics
  • Graph theory: union of graphs; graph isomorphism; incidence matrices; graph invariant; graph connectivity; review special types of graphs.

11

• University of London
• Discrete mathematics
  • Review: Graph theory: special types of graphs; simple graphs; bipartite graphs & matchings; complete bipartite graphs.
• Git: Read article Trunk-based development.

10

• University of London
• Discrete mathematics
  • Review: Graph theory: terminology; special types of graphs.
• Review: Walrus operator in Python 3.8.

8

• Git: Committing file patches; adding changes to old commits; recovering dropped hash references.

7

• University of London
• Discrete mathematics
  • Review: Again some propositional logic, predicate logic, Boolean algebra, set theory, functions.

6

• University of London
• Discrete mathematics
  • Review: Propositional logic, predicate logic, Boolean algebra, set theory, functions.

5

• University of London
• Discrete mathematics
  • Review: Predicate logic and Boolean algebra.

4

• University of London
• Discrete mathematics
  • Graph theory: terminology; types of graphs (simple, r-regular, complete graphs); graph models.

3

• University of London
• Algorithms and Data Structures I
  • Binary search, search problems and abstraction.
• Discrete mathematics
  • Introduction to graph theory, degree sequence of a graph.

2

• University of London
• Algorithms and Data Structures I
  • RAM model, time complexity, insertion sort, running time of algorithms, binary search.
• Discrete mathematics
  • Recursion: recursively defined functions, structural induction.

1

• University of London
• Discrete mathematics
  • Recursion: recursively defined functions.

---

2019

2018 and earlier