Issue 65: The Durbyn Project, DeepSeek in Practice, New Tutorials

A weekly curated update on data science and engineering topics and resources.

This week's agenda:

• Open Source of the Week - the Durbyn project

• New learning resources - Harvard’s R course, Django tutorial, SQL course, Databricks end-to-end project

• Book of the week - DeepSeek in Practice by Andy Peng, Alex Strick van Linschoten, and Duarte O.Carmo.

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Are you interested in learning how to set up automation using GitHub Actions? If so, please check out my course on LinkedIn Learning:

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Open Source of the Week

It’s been a while since we had a Julia project here, and this week’s focus is on Durbyn - a Julia package for time series forecasting applications. This project by Resul Akay is inspired by R’s forecast package and provides implementations of core time-series statistical models.

Project repo: https://github.com/taf-society/Durbyn.jl

Key Features

• Durbyn supports a wide variety of forecasting algorithms, including exponential smoothing (ETS/SES/Holt/Holt-Winters), auto- and manual ARIMA, ARAR/ARARMA, BATS/TBATS

• Formula Interface - it provides a declarative interface for model specification with full support for tables, regressors (features in ML terminology), model comparison, and panel data.

• Performance - the library leverages Julia’s high performance, and it is multi-threaded by default when fitting models across multiple time series

Here is an example for setting up an ARIMA model with regressors:

More details are available in the project documentation.

License: MIT

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New Learning Resources

Here are some new learning resources that I came across this week.

Harvard R Programming Course

Harvard released a new CS course - Intro to R Programming, and it covers the following topics:

• Introduction

• Representing Data

• Transforming Data

• Applying Functions

• Tidying Data

• Visualizing Data

• Testing Programs

• Packaging Programs

AI Researcher Tutorial

The following tutorial by Vuk Rosić focuses on the mathematical foundations of AI, and it covers the following topics:

• Math concepts - derivatives, vectors, gradients, etc

• Probability

• PyTorch and tensors

• Matrix algebra

• Activation functions

• Neural network

• Attention mechanism

• GPT architecture overview

Django Tutorial

The following tutorial by NeuralNine is beginner-friendly to Django, a Python web framework.

SQL Full Course

The following course provides an in-depth introduction to SQL. This 10-hour course is beginner-level, and it starts from the basics of SQL comments all the way to advanced topics such as subqueries, CASE WHEN, stored procedures, loops, cursors, triggers, exception handling, etc.

Databricks End-To-End Project

Hands-on Databricks tutorial by Thomas Hass, focusing on building an end-to-end pipeline. This includes setting data and ML pipelines, a dashboard, and UI.

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Book of the Week

This week’s focus is on a new book - DeepSeek in Practice by Andy P. , Alex Strick van Linschoten , and Duarte O.Carmo. The book provides a hands-on guide to working with the DeepSeek, an open-source LLM model.

The book covers the following topics:

• Introduction to DeepSeek

• Working with DeepSeek via API

• Model fine-tuning

• Running agents with DeepSeek

• Using DeepSeek in production

The book is ideal for AI developers, engineers, and data scientists aiming to leverage open-source LLMs like DeepSeek for their projects. Prior knowledge in Python programming, APIs, and machine learning concepts is required.

The book is available online on the publisher’s website, and a hard copy can be purchased on Amazon.

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Have any questions? Please comment below!

See you next Saturday!

Thanks,

Rami