# New DataScientist on board - Where do I start?

**URL:** <https://forum.numer.ai/t/new-datascientist-on-board-where-do-i-start/6762>\
**Category:** Data Science\
**Created:** [October 31, 2023, 12:53pm UTC](https://forum.numer.ai/t/new-datascientist-on-board-where-do-i-start/6762 "2023-10-31T12:53:01Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![zoliveres](https://avatars.discourse-cdn.com/v4/letter/z/ad7895/32.png) [@zoliveres](https://forum.numer.ai/u/zoliveres)\
**Post date:** [November 2, 2023, 8:26am UTC](https://forum.numer.ai/t/new-datascientist-on-board-where-do-i-start/6762/2 "2023-11-02T08:26:56Z")

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I can’t tell you what you’ll consider useful, but here’s what I considered useful, when I started (which was 4 months ago 😛 ):

1. I think you should go trough the [Numerai Example Scripts](https://github.com/numerai/example-scripts), that would guide you trough the whole process of training, submitting, etc.
2. Then Check the Benchmark models of Numerai, this is really fresh information: [Benchmark Models - Numerai Tournament](https://docs.numer.ai/numerai-tournament/benchmark_models)
3. Watch the [Numerai](https://www.youtube.com/@Numerai/playlists) content on YouTube, specifically the Quant Club and the Fireside chats to give you better context.
4. Watch [StudyM8’ts Numerai Series](https://www.youtube.com/watch?v=YeEpOm5JjFk&list=PLlOOibxKpQUlM28lqa4TiUTJ5gjLG5haQ)
5. Check the Grid Search article: [Super Massive LGBM Grid Search](http://forum.numer.ai/t/super-massive-lgbm-grid-search/6463) and in general, just search the forum if you happen to have questions
6. Just hop into Discord, a lot of smart people discussing so many things, I picked up a lot there
7. Marcos López de Prado: Advances in Financial Machine Learning book will give you so many new concept and basic understanding on what Numerai does or even Finance

As per what models are considered effective?  
I can’t really tell, most people are using GBT, there are a few people active on Discord who are using, Genetic Algorithms, Random Forests, Transformers, RNNs, but not much people are conversing about LSTMs.

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