Brief Overview of a 360in525 Minutes Course Set
For more details see Overview of a 360in525 Minutes Course Set in Data Sciences, Spring 2018
360in5254: Mathematical, Statistical and Computational Foundations for Data Scientists
Threefullday workshops (3 hp) on May 11, 18 and 25 2018. Prerequisites: current proficiency in highschool level mathematics (precalculus, geometry and algebra with some programming experience beyond Excel). Target Audience: any MSc or PhD student at UU who wants to understand the mathematical statistical foundations in the data scientist’s computational toolbox. The approach will use formal mathematical communication of concepts starting from sets and logic, but with concomitant development of computer programming skills to algorithmically construct and implement the concepts. Topics will include: Sets, Maps, Functions, Modular Arithmetic, Axiomatic Probability, Conditional probability, Pseudorandom constructive understanding of random variables and structures including graphs, Statistics, Likelihood Principle, Bayes Rule, Decisions (parametric and nonparametric) including tests and estimators, Markov chains and their pseudorandom constructions, etc. We will use SageMath locally and collaborate in COCALC during the lab/lectures.
Background and Context:
This is a mathematically more careful (at an advanced undergraduate level) version of UC Berkeley’s most popular freshman course:
 http://data8.org/ with the formula:
 computational thinking + inferential thinking = data science
 as talked about at the end here.
Prepare your laptop:
SOFTWARE: We will be using SageMath/Python ecosystem for the next three Fridays. Follow the download and installation instructions for your Operating System from the following URL:
To test that you have installed correctly do the following:
 On a Mac OS X or Unix/Linux syatem, say you installed sage in a directory inside your home directory called
~/all/software/sage/
, then you can see if the following command launches a Jupyter notebook server successfully:
$ ~/all/software/sage/SageMath/sage n jupyter
 Those with Windows should follow the instructions in the following URL and test that the jupyter notebook server launches successfully:
Course Content
Download the zip file of SageMath ipynb notebooks from:
After downloading the zip file, unzip it inside the directory you launched the sage jupyter notebook server from. You should be able to see all the jupyter .ipynb
notebooks by navigating from your jupyter notebook server.
Individual SageMath Jupyter .ipynb
Notebooks
Use the above archived .zip file directly!
 00. Introduction
 01. BASH crash
 02. Numbers, Strings, Booleans and Sets
 03. Map, Function, Collection, and Probability
 04. Conditional Probability, Random Variables, Loops and Conditionals
 05. Random Variables, Expectations, Data, Statistics, Arrays and Tuples, Iterators and Generators
 06. Statistics and List Comprehensions with New Zealand Earthquakes
 07. Modular Arithmetic, Linear Congruential Generators, and PseudoRandom Numbers
 08. PseudoRandom Numbers, Simulating from Some Discrete and Continuous Random Variables
 09. Estimation, Likelihood, Maximum Likelihood Estimators and Symbolic Expressions
 10. Markov Chains
 11. Limits, Convergence, and Estimation
 12. Nonparametric Estimation and Testing
 15151630 hours, May 25 2018: Kollokvium: Local asymptotic equivalence of pure quantum states ensembles and quantum gaussian white noise
YouTube Archive of lab/lectures

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