Data science/ML/AI

Data Science Portfolios, Speeding Up Python, KANs, and Other May Must-Reads Python One Billion Row Challenge — From 10 Minutes to 4 Seconds With a longstanding reputation for slowness, you’d think that Python wouldn’t stand a chance at doing well in the popular “one billion row” challenge. Dario Radečić’s viral post aims to show that with some flexibility and outside-the-box thinking, you can still squeeze impressive time savings out of your code. N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting Anyone who enjoys a thorough look into a model’s inner workings should bookmark Jonte Dancker’s excellent explainer on N-BEATS, the “first pure deep learning approach that outperformed well-established statistical approaches” for time-series forecasting tasks. Build a Data Science Portfolio Website with ChatGPT: Complete Tutorial In a competitive job market, data scientists can’t afford to be coy about their achievements and expertise. A portfolio website can be a powerful way to showcase both, and Natassha Selvaraj’s patient guide demonstrates how you can build one from scratch with the help of generative-AI tools. A Complete Guide to BERT with Code Why not take a step back from the latest buzzy model to learn about those precursors that made today’s innovations possible? Bradney Smith invites us to go all the way back to 2018 (or several decades ago, in AI time) to gain a deep understanding of the groundbreaking BERT (Bidirectional Encoder Representations from Transformers) model. Why LLMs Are Not Good for Coding — Part II Back in the present day, we keep hearing about the imminent obsolescence of programmers as LLMs continue to improve. Andrea Valenzuela’s latest article serves as a helpful “not so fast!” interjection, as she focuses on their inherent limitations when it comes to staying up-to-date with the latest libraries and code functionalities. PCA & K-Means for Traffic Data in Python What better way to round out our monthly selection than with a hands-on tutorial on a core data science workflow? In her debut TDS post, Beth Ou Yang walks us through a real-world example—traffic data from Taiwan, in this case—of using principle component analysis (PCA) and K-means clustering.

Mastering Probability and Combinatorics "Mastering the Essentials: Probability and Combinatorics Explained" Rating ⭐️: 4.0 out 5 Students 👨‍🎓 : 1,129 Duration ⏰ : 1hr 24min of on-demand video Created by 👨‍🏫: Akhil Vydyula 🔗 Course Link #probability ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

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Ray Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads. Creator: ray-project Stars ⭐️: 33.3k Forked By: 5.6k https://github.com/ray-project/ray #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Join @datascience_bds for more cool repositories. *This channel belongs to @bigdataspecialist group

Data Science Core Concepts 2023 Data Science Core Concepts Rating ⭐️: 4.8 out 5 Students 👨‍🎓 : 1551 Duration ⏰ : 1hr 49min of on-demand video Created by 👨‍🏫: Python Only Geeks 🔗 Course Link #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

Your Ultimate guide to Permutations Have you ever marveled at how many ways you can arrange a set of items when the order truly matters? In this article, I will explain permutations, exploring how they help determine the number of possible arrangements in a set. If you find my articles interesting, don’t forget to clap and follow 👍🏼, these articles take times and effort to do! Permutations “A permutation is a mathematical technique that determines the number of possible arrangements in a set when the order of the arrangements matters. Common mathematical problems involve choosing only several items from a set of items in a certain order. “[1] Types of permutations 1 / Permutations Without Repetition : used when each item in the set can only appear once in each arrangement. 🔗 Read More

Ocean Data in Canada Learn what ocean data are, how they're being used, and the ways in which you can access open ocean data. Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 1368 Duration ⏰ : 49min of on-demand video Created by 👨‍🏫: Katherine Luber, Jacob Thompson, Shayla Fitzsimmons 🔗 Course Link #datascience ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

9 types of data visualization In this article, I will guide you through the wonderful world of data visualization and expand your knowledge about the way you can display your data and how to tell your data story to your specific audience. Let’s start with data visualization in its most basic form; the (static) chart. Charts are used to display large amounts of data in a condensed and easy-to-understand manner. They are graphical representations of data which makes it easy and fast to digest by the brain. Moreover, charts make it apparent to find hidden information and insights that are otherwise hard to find from a table with data. There are a lot of types of charts, each with its own function. The most commonly known charts are the bar chart, the line chart, and the pie chart. Charts form the basis for all types of data visualizations I will discuss in this blog. 🔗 Read More

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storytelling with data by Cole Nussbaumer Knaflic 📄 284 pages 🔗 Read Online #datascience #datavisualization ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈

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Probability for Data Science Covers the probability concepts essential for data science Rating ⭐️: 4.7 out 5 Students 👨‍🎓 : 2917 Duration ⏰ : 1hr 56min of on-demand video Created by 👨‍🏫: Anand Seetharam 🔗 Course Link #datascience #probability ➖➖➖➖➖➖➖➖➖➖➖➖➖➖ 👉Join @datascience_bds for more👈