Schedule (all lectures are 2 hour-long)
=======

 

Lecture                                                    Date & Time
-----------                                                   -------------------
Linux Part 1                                             TUE SEP 20, 2PM-4PM
Linux Part 2                                             MON SEP 26, 09AM-11AM
Linux Part 3                                             MON OCT 03 , 09AM-11AM
GIT Part 1                                               MON OCT 10 , 09AM-11AM
GIT Part 2                                               MON OCT 17, 09AM-11AM
GIT Part 3                                               MON OCT 24 , 09AM-11AM
GIT Part 4                                               MON OCT 31 , 09AM-11AM
Python Part 1                                          MON NOV 07 , 09AM-11AM
Python Part 2                                          MON NOV 14 , 09AM-11AM
Python Part 3                                          MON NOV 21 , 09AM-11AM
Python Part 4                                          MON NOV 28 , 09AM-11AM
Intro to Machine Learning Part 1             MON DEC 05 , 09AM-11AM
Intro to Machine Learning Part 2             MON DEC 12 , 09AM-11AM
Intro to Machine Learning Part 3             MON DEC 19 , 09AM-11AM

 

Syllabus
=======

 

o Linux
Part 1: Linux filesystem, Bash terminal and commands, Commands help / manual, Navigating the filesystem, Parsing files, Piping commands, File permissions, Super user privileges
Part 2: Writing bash shell scripts, Using a dedicated developing environment, Executing scripts / permissions, Bash variables, Strings, arrays, Control flow statements (for loop, if-else statements)
Part 3: Control flow statements [continued] (while loop, case/switch), Special script variables / exit codes, Arguments parsing with getopts, String/path manipulations, Hands on coding example of a full script

 

o Git
Git part 1: Git ecosystem, Git promotion model & typical workflow, Commit definition, Examining history, Hands-on: creating a history of commits on a local repo
Git part 2: Concept of Branches (as a pointer to a commit), Fast-forward merge, Introduction to Github, Remotes, Cloning, pushing and pulling, Tracking remote branches, Hands-on: creating a repo on Github and simulating team collaboration
Git part 3: Three-way merge, Tags, Merge conflicts, Hands-on: dealing with merge conflicts, Collaboration with fork / pull-request model, Hands-on: fork / PR model
Git part 4 Rebase, Cherry-picking, Finding a bug with bisection

o Python
Part 1: Introduction to Jupyter notebooks, Markdown, Variables and datatypes, Mutable vs Immutable datatypes, Strings and string manipulation, Lists, Dictionaries and tuples, Control structures: for loop, if-else statements, Constant interactive hands-on in between each concept
Part 2: Functions, None keyword, Function docstring, Type hinting, Development Environment (Visual Studio Code), Modules and imports, Debugging, Exceptions and asserts statements
Part 3: Object Oriented Programming Basics, Method attributes, Conda: creating, activating and configuring environments, Refactoring code, Creating modules and importing them in Jupyter notebooks
Part4: Numerical analysis and plotting with numpy: array creation, slicing and filtering, Plotting with matplotlib: line plot, scatter plot, bar plot, overlays, histograms, subplots, heatmaps

 

o Intro to Machine Learning (with Python)
Part 1: General usefulness of ML in science, Example of ML applied to simple problem of predicting projectile range, Choice of features and labels, Assessing model performance, MSE, Basics of optimization, parameter estimation, model fitting, Model generalizability, training/testing, Hands-on example with sklearn using linear (OLS) model, Bias-variance trade-off, Cross-validation, Hands-on example with sklearn using polynomial features and Pipeline objects
Part 2: Preventing overfitting with regularization, Hyper-parameter tuning, Nested cross-validation, Classification and classification metrics, SVM, Unbalanced classes, Dealing with regularization and class imbalance with SVM, Dealing with missing data / data imputation, Assessing model stability, Hyper parameter tuning with grid search
Part 3: Unsupervised techniques, Dimensionality reduction with PCA, Clustering with K-means, Assessing clustering performance

 

A step-by-step full example project should be implemented by the student at the end of the course (project content TBA).