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update analytics.md #18

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Expand Up @@ -13,45 +13,35 @@ Analytics
Hour type: 00071
Notes: Hours for X SIG
-->
- Sarah Alidoost
- Flavio Hafner (current)
- Johan Hidding
- Dafne van Kuppevelt

We rotate the leadership, and the current lead is indicated with `(current)`.

## What is SIG’s mission?
<!-- help text goes here -->
The mission of this SIG in the broadest sense, is to raise general knowledge on applied analytical solutions in the Netherlands eScience Center, as well as their technical implementations. We aim for a deeper understanding than is generally needed to use a specific method, that is, to go beyond the black-box level of thinking. Mathematics/statistics is capable of drastically reducing the complexity of existing solutions in a wide variety of cases, as well as formulating novel ideas that lie outside our comfort zone.
The mission of this SIG in the broadest sense, is to raise general knowledge on applied analytical and numerical solutions in the Netherlands eScience Center, as well as their technical implementations.
We aim for a deeper understanding than is generally needed to use a specific method, that is, to go beyond the black-box level of thinking. Mathematics/statistics is capable of drastically reducing the complexity of existing solutions in a wide variety of cases, as well as formulating novel ideas that lie outside our comfort zone.


## What is the GitHub repository of the SIG use?
<!-- Please transfer your SIG existing repo to this GitHub organization -->
<https://github.com/NLeSC/Analytics-SIG>

## What is the Office group that the SIG uses?
Analytics@esciencecenter.nl
https://github.com/nlesc-sigs/analytics-sig

## SIG outcomes in the period April 2020 - October 2020 (for existing SIGs)
On May 14th we had a meeting were discussed the outcomes of the survey among the Analytics SIG members.

We had the following presentations:
- May 11: Johan Hidding - "Complex Systems"
- May 25th: Hanno Spreeuw - "Confidence intervals"
- June 8th: Dafne van Kuppevelt - "Overview of network science"
- June 22nd: Wanda de Keersmaecker - "Time series analysis and change detection techniques - Towards tropical forest resilience from remote sensing time series."
- Okt 12th: Pablo Rodriguez Sanchez - "Complex numbers for research software enginees"
The presentations can be found on the [SIG repository](https://github.com/NLeSC/Analytics-SIG).

In the remaining sessions, we worked through chapter 2.6 in the book [A first course in network science](https://www.cambridge.org/highereducation/books/a-first-course-in-network-science/EE22722F27519D8BB1443C7225C57BAF). The chapters were read during self-study, and the answers to the exercises were collected through a survey form and discussed during the meeting.
## What is the Office group that the SIG uses?
analytics@esciencecenter.nl

## Plans for the period until the end of April 2021
## Plans for 2024

- **Network Science course**: We continue working on the book [A first course in network science](https://www.cambridge.org/highereducation/books/a-first-course-in-network-science/EE22722F27519D8BB1443C7225C57BAF)
- **Monthly presentations**: Continuing the monthly meetings with 30min presentations. We aim for monthly interactive meetings where diverse speakers from in and outside the center present a topic. The topic can be either an introduction into a technique or an application where advanced analytical methods played a pivotal role. The planned/upcoming meetings will be posted on the [Analytics SIG github page](https://github.com/NLeSC/Analytics-SIG).
- **Organize small hackaton** We choose a dataset and technique that we want to explore and organize a small (1 day?) hackaton around this
Our interest lies in the following themes:
- statistical computing for social sciences
- causal inference and machine learning
- recent developments in techniques for data sharing through federated learning and differential privacy
- understanding deep learning algorithms and backpropagation
- simulation modeling of the socio-economic aspects of climate change

Some of the topics we want to explore are:
- Network Science
- Data cleaning for AI, e.g. filling missing values
- Numerical computational techniques
For some of these topics, we will collaborate with the Machine Learning SIG.

## What are the expected outputs of the SIG?
- Internal training on a specific topic (mentioned above)
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