Eryk Lewinson

Data Scientist, Author of "Python For Finance Cookbook"

[Eryk Lewinson @erykml1]

I have recently published another tutorial in @gitconnected. This time, I looked into portfolio optimization using  PyPortfolioOpt 📈 https://t.co/NG8NVamnoY

[Eryk Lewinson @erykml1]

In my latest article, I show how to use SciPy for portfolio optimization 📈 We can easily find a portfolio that historically minimizes the volatility or maximizes the Sharpe ratio. https://t.co/9xS7aPv0CA

[Eryk Lewinson @erykml1]

I just published 7 Best Machine Learning Workflow and Pipeline Orchestration Tools https://t.co/SGJy5DJXnc

[Towards Data Science @TDataScience]

From pair-programming and mentoring to carving out personal-development time, @erykml1 presents practical tips to help you build healthy habits for continuous learning as a data scientist. https://t.co/hdqB2hGZOw

[Towards Data Science @TDataScience]

What are the best strategies to avoid stagnation as a data scientist? @erykml1 draws on his own experiences and offers pragmatic tips you can incorporate into your daily routines and workflows. https://t.co/hdqB2hGZOw

[Eryk Lewinson @erykml1]

I just published in @gitconnected Evaluating Trading Strategies with Python 101 https://t.co/fSZwgsBqrk

[Eryk Lewinson @erykml1]

After some time, delving into finance feels refreshing! Excited to share my latest piece on the basics of technical analysis using Python 📈💻 Check out the article published on @gitconnected here: https://t.co/HZYYQRSTpt

[Eryk Lewinson @erykml1]

I just published an article in @TDataScience! This time, I am exploring 15 hidden gems in pandas that will help you to level up your data wrangling game! https://t.co/1jEPkyYEy0

[Towards Data Science @TDataScience]

Keep Track of Your Backtests with DVC’s Experiment Tracking - Part 4 of the tutorial on how to use DVC for experiment tracking, this time, with time series forecasting by @erykml1 https://t.co/XX1aYFYtlb

[Eryk Lewinson @erykml1]

The repo also contains the slide deck, and I will share the presentation itself once it becomes available.

In case you have any feedback or questions, feel free to share – I'm curious to hear your thoughts!

3/3🧵

[Eryk Lewinson @erykml1]

It was a real pleasure to present at @PyData Global 2023 today!

During my presentation, I talked about using #git and @DVCorg for reproducible ML experimentation—all from within VS Code! 1/3🧵

[Eryk Lewinson @erykml1]

For the toy example, I used something I've always wanted to play around with - a Pokémon dataset! If you're curious about a reproducible approach to classifying legendary Pokémon, you can check out the project's codebase on GitHub - https://t.co/5p31BdNnqj 2/3 🧵

[Eryk Lewinson @erykml1]

@allaei explains convex optimisation with a Cobb-Douglas utility function in a clear and fun way. All in the context of a fruit salad! Check out his article in @TDataScience :) https://t.co/45H7pPrEsu

[Eryk Lewinson @erykml1]

@allaei explains convex optimization with a Cobb-Douglas utility function in a clear and fun way. All in the context of a fruit salad! Check out his article in @TDataScience :) https://t.co/45H7pPrEsu

[Towards Data Science @TDataScience]

Learn how to properly evaluate the performance of time series models through backtesting — @erykml1's new tutorial patiently walks us through the process. https://t.co/7ZRPuWJ1EY

[Eryk Lewinson @erykml1]

I just published a new article about time series in @TDataScience: Putting Your Forecasting Model to the Test - A Guide to Backtesting https://t.co/JY638Mowcs

[Towards Data Science @TDataScience]

.@erykml1 explores the nuances of simple moving averages and exponentially weighted moving averages https://t.co/BteSY5ZGC3

[Eryk Lewinson @erykml1]

A really nice tutorial by @MichalOleszak on organizing a machine learning monorepo with pants (a build system). If you want to get started with a monorepo approach, I highly recommend his article in @TDataScience! https://t.co/GnKB9tlDdJ

[Eryk Lewinson @erykml1]

How to Build an LLM from Scratch by @ShawhinT in @TDataScience https://t.co/9E4U4BbuWw

[Towards Data Science @TDataScience]

Metrics Layer: A Single Source of Truth for All KPI Definitions by @erykml1 https://t.co/RgwLfhonwE