dap_taltech's Introduction

Nesta TalTech Hackweek 2023 ✨

All things TalTech Hackweek 2023 ft. tutorials, utilities and resources ⛏️

The main purpose of this repo is to store tutorials relevant to the Nesta TalTech hackweek 2023. There are also utilities functions (i.e. data getters) that you can install in your environment that will be relevant across tutorials and (potentially) you project.

🧰 Set up

To follow along the tutorials and to have access to utilities in the repo (i.e. data getters, plotting), you should create a conda environment and install the repo as a library:

conda create -n dap_taltech python=3.9
pip install git+https://github.com/nestauk/dap_taltech.git

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dap_taltech's Issues

get estonian openalex data

Develop presentation for Nesta Data Science Success story [IF TIME]

deal with colab incompatibilities

so far:

requirements.txt has too many libraries - slim down to be tutorial specific
data getters not working locally or remotely.

Create "welcome to hackweek" material

Follow up on participant signs ups

Generate OpenAI API Key for tutorials

Create tutorial material for text analysis

Invite speakers

Create tutorial material for LLMs

Create mini package with data getters

Add innovation mapping LLM tutorial [IF TIME]

Finalise innovation mapping LLM tutorial (if there is time)

Check for content coherence

Practice tutorials

I think it would be good to practice tutorials to make sure things flow/make sense.

Create project deck

Each project pack should contain:

Research question/theme
(where relevant) dataset(s) and instructions to access the data
Links to suggested python libraries/tools
Illustrated example outputs

Make notebooks Colab runnable

We should be able to check if a notebook is running in Colab or not by putting

IS_COLAB = 'google.colab' in str(get_ipython())

in the first cell. If IS_COLAB is True then we can trigger the notebook to !pip install the requirements from the repo.

Create tutorial material for time series forecasting

Develop presentation for optional Introduction to Data Science

Create an optional "data science download" tutorial with high level information on:

how to approach a data science problem
a quick tour of data/methods (largely borrowed from our previous work on how to 'cook up' a data science project)
practical resource list to get you started (python for ds, data for ds, ml libraries, other introductory material)

I don't think we need code in this session, but links to repos/tutorials with code could be helpful.