https://lamastex.github.io/360-in-525/
Ask for: 60 MSEK.
I'll make a more detailed plan in time (several PhD students have
blocked their times to take at least parts of the course sets). It is
just a snippet from my email to current students in my data science
courses.
A 360-in-525 CIM Course Set in Data Sciences for Spring 2018
There will be several full-day (360 minutes-long, with usual breaks)
workshop-style 1hp to 3hp courses funded by the Centre for
Interdisciplinary Mathematics in Spring 2018 to remedy some of the
problems above. Feel free to take any subset of modules if you can
satisfy the prerequisite modules. Physical presence is mandatory for
the hp in all of these modules and cannot be substituted for YouTubing
via HangOutsOnAir. There is no TA support for these modules and the
assessment may be Oxford style where you need to book a one-on-one
meeting with me for about a hour or two at the end of the modules you
have taken. Minimal auto-graded assignments to help you know your
limits of absorbing knowledge under your current time commitments will
be in place similar to the assessments for 'Introduction to Data
Science'.
CIM course 1. one-full-day workshop titled 'Introduction to Apache Spark for Data Scientists' (1 hp) on April 20 2018. Most of you can skip this
(I will not introduce RDDs and jump right into Datasets and DataFrames but much much slower as several students will be Faculty in humanities. Also our main textbook 'Apache Spark - the Definitive Guide' will be in print by then). There is a very very small probability that this date may change.
CIM course 2. two-full-days workshops titled 'Social Media and Big Data' (2 hp) on April 26-27 2018. Prerequisites: CIM course 1 or 'Introduction to data Science'. the first day will be an introduction to the domain by Professor Simon Lindgren,a digital sociologist from Umea: http://www.simonlindgren.com/ and the second day will be by Raaz (effectively, a rebuild-up of the full twitter experimental designs in real-time towards the 'Where Am I Operator' and a bit more on digesting gdelt global news streams). This is jointly sponsored by UU's CEMFOR: http://www.rasismforskning.uu.se/.
(Recommend this for Mariama and Li or others like Amendra if they want to have more fun - there are lots of jobs in digital humanities, including journalism, marketing, security, etc. Other data scientists will generally run into data fusion problems from social media feeds even when working on other products/problems.)
CIM course 3. two-full-day workshops titled 'Geospatial Analytics and Big Data' (2 hp) on May 3-4 2018. Prerequisites: CIM course 1 or 'Introduction to data Science'. The first day will be done by domain experts from UU's Department of Cultural Geography: http://katalog.uu.se/empinfo/?id=N2-980 with tutorials on non-distributed geospatial analytics and the second day will be
CIM course 4. three-full-day workshops titled 'Mathematical, Statistical and Computational Foundations for Data Scientists' (3 hp) on May 11, 18 and 25 2018. Prerequisites: current proficiency in high-school level mathematics (pre-calculus 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. Topics will include: Sets, Maps, Functions, Modular Arithmetic, Axiomatic Probability, Conditional probability, Pseudo-random constructive understanding of random variables and structures including graphs, Statistics, Likelihood Principle, Bayes Rule, Decisions (parametric and non-parametric) including tests and estimators, Markov chains and their pseudorandom constructions, etc (full details around new year). We will use sageMath/python locally and collaborate in COCALC in lab/lectures.
CIM course 5. two-full-day workshops titled 'Population Genetics and Big Data' (2 hp) on May 31 and June 1 2018. Prerequisites: (CIM course 1 or 'Introduction to data Science') and (CIM course 4 or equivalent). The first day will be the main theories in current population genetics and genomics. The second day will use ADAM and possibly Hail over Apache Spark.
CIM course 0. Mathematical Statistical learning Theory Series: An L1 View of Minimum Distance Estimators in 360 minutes for mathematical researchers (1 hp) on TBA (if at least two Maths PhD students enrol at least 3 days before the proposed offering in doodle).
This course will introduce a PhD student in mathematics or mathematical statistics to one of the fundamental problems at the very core of various probabilistic theories of decision-making. We will mainly focus on the relation between the combinatorial geometric complexity of the sigma algebras of a simple measurable space and the rates of convergence of empirical measures in one of the simplest posable decision problems - nonparametric density estimation of an unkown density in L1. This course was given first in CMAP, Ecole Polytechnique, Palaiseau, France for PhD students. Perhaps Felipe, Tilo and Dan may be into this (Geometers and Combinatorial probabilists should also find this very insightful for their own research as one of the basic theorems involves the combined use of over a dozen unique classical and some less well-known inequalities!).