Data Science

Google Research has released TimesFM-3, a 330 million parameter time series foundation model that forecasts multiple related series in a single forward pass. Unlike every TimesFM checkpoint through 2.5, it is pretrained natively for multivariate forecasting, accepting multiple targets, past covariates, and past-future covariates with no task-specific fine-tuning. It takes the top average rank among pretrained foundation models on GIFT-Eval, fev-bench, and the TIME leaderboard. The weights, however, ship under a non-commercial, non-production license.

In this tutorial, we explore a LabPlot-inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis...

In this tutorial, we build a complete quantitative backtesting workflow with OctoBot and OctoBot-Script while keeping the environment isolated from Colab’s preinstalled dependencies. We...

Feyn Labs has released SQRL, a family of text-to-SQL models that inspect a database with read-only probes before committing to a query. The flagship SQRL-35B-A3B reports 70.6% execution accuracy on BIRD Dev, edging Claude Opus 4.6, and distills into self-hostable 4B and 9B checkpoints.

In this tutorial, we build a Prefab application that creates interactive dashboards entirely in Python. We design an operations dashboard with reactive state, charts, tables, filters, forms, tabs, and metrics. We generate synthetic pipeline monitoring data and connect it to live UI controls. We then export the app as static HTML and preview it directly inside Google Colab.

We build an end-to-end forecasting workflow with TimeCopilot on a panel of real airline passenger data and a synthetic seasonal series with injected anomalies. We evaluate statistical, foundation, and optional GPU-based models using rolling cross-validation and multiple error metrics. We generate probabilistic forecasts with prediction intervals, visualize future trends, and flag unusual observations. We then explore TimeCopilot's optional LLM agent, which selects a model and explains its predictions.

In this tutorial, we work with NVIDIA's Nemotron-Pretraining-Code-v3 dataset as a large-scale metadata index for code pretraining research. We stream the dataset instead of downloading it, inspect its schema, and build a manageable sample. We analyze languages, file extensions, repository frequency, and directory depth to understand the index structure. We then reconstruct raw GitHub URLs, fetch real source files, and estimate the token scale of the fetched code.

In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for...

In this tutorial, we will generate knowledge graphs from plain text, conversations, and multiple source documents using kg-gen. We start by setting up the...

In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. We start by training...

In this tutorial, we delve into CuPy as a powerful GPU-accelerated alternative to NumPy for high-performance numerical computing in Python. We start by inspecting...

In this tutorial, we explore skfolio, a scikit-learn compatible portfolio optimization library that helps us build, compare, and evaluate different investment strategies in a...

In this tutorial, we implement how to use pandas-ta-classic to build a complete technical analysis and trading strategy workflow. We start by installing the...

In this tutorial, we perform an advanced single-cell RNA-seq analysis workflow using Scanpy on the PBMC-3k benchmark dataset. We start by loading the dataset,...

How momentum optimizes gradient descent by dampening oscillations and accelerating convergence on complex

In this tutorial, we walk through a complete, end-to-end workflow for correcting bias in survey data using the balance library. We simulate a realistic...

Introducing NeuralSet: Meta's Simple, Fast, and Scalable Python Package That Bridges Neuroscience and AI

In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by...

LoRA is widely used for fine-tuning large models because it’s efficient, but it quietly assumes that all updates to a model are similar. In...

In this tutorial, we explore how we use BudouX to bring intelligent, phrase-aware line breaking to languages where whitespace is not naturally present, such...

In this tutorial, we explore Datashader, a powerful, high-performance visualization library for rendering massive datasets that quickly overwhelm traditional plotting tools. We work through...

Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over...

In this tutorial, we build a comprehensive, hands-on understanding of DuckDB-Python by working through its features directly in code on Colab. We start with...

In this tutorial, we explore how to solve differential equations and build neural differential equation models using the Diffrax library. We begin by setting...

In this tutorial, we build a complete pipeline for single-cell RNA sequencing analysis using Scanpy. We start by installing the required libraries and loading...

At first glance, adding more features to a model seems like an obvious way to improve performance. If a model can learn from more...

In this tutorial, we explore tqdm in depth and demonstrate how we build powerful, real-time progress tracking into modern Python workflows. We begin with...

In this tutorial, we implement a production-grade, large-scale graph analytics pipeline in NetworKit, focusing on speed, memory efficiency, and version-safe APIs in NetworKit 11.2.1....

In this tutorial, we demonstrate how to move beyond static, code-heavy charts and build a genuinely interactive exploratory data analysis workflow directly using PyGWalker....

In deep learning, classification models don’t just need to make predictions—they need to express confidence. That’s where the Softmax activation function comes in. Softmax...

Cisco and Splunk have introduced the Cisco Time Series Model, a univariate zero shot time series foundation model designed for observability and security metrics....

In this tutorial, we build an advanced multi-page interactive dashboard using Panel. Through each component of implementation, we explore how to generate synthetic data,...

Binary cross-entropy (BCE) is the default loss function for binary classification—but it breaks down badly on imbalanced datasets. The reason is subtle but important:...

In this tutorial, we build an advanced interactive dashboard using Textual, and we explore how terminal-first UI frameworks can feel as expressive and dynamic...

In this tutorial, we explore the advanced capabilities of PyGWalker, a powerful tool for visual data analysis that integrates seamlessly with pandas. We begin...

In this tutorial, we explore Hydra, an advanced configuration management framework originally developed and open-sourced by Meta Research. We begin by defining structured configurations...

One of the biggest challenges in real-world machine learning is that supervised models require labeled data—yet in many practical scenarios, the data you start...

In this tutorial, we explore how to harness Apache Spark's techniques using PySpark directly in Google Colab. We begin by setting up a local...

In this tutorial, we create a fully interactive, visually compelling data visualization dashboard using Bokeh. We start by turning raw data into insightful plots,...

In this tutorial, we build a compact, efficient framework that demonstrates how to convert tool documentation into standardized, callable interfaces, register those tools in...

In this tutorial, we explore the power of self-supervised learning using the Lightly AI framework. We begin by building a SimCLR model to learn...

In this tutorial, we set out to build an advanced interactive dashboard using Dash, Plotly, and Bootstrap. We highlight not only how these tools...

In this tutorial, we walk through an advanced end-to-end data science workflow where we combine traditional machine learning with the power of Gemini. We...

Table of contentsWhat pain point in forecasting is being eliminated?How does in-context fine-tuning work under the hood?What exactly is “few-shot” here?Does it actually match...

In this tutorial, we explore how we can seamlessly run MATLAB-style code inside Python by connecting Octave with the oct2py library. We set up...

LLMs have rapidly advanced with soaring parameter counts, widespread use of mixture-of-experts (MoE) designs, and massive context lengths. Models like DeepSeek-R1, LLaMA-4, and Qwen-3...

Salesforce AI Research has unveiled Moirai 2.0, the latest advancement in the world of time series foundation models. Built atop a decoder-only transformer architecture,...

Table of contentsStep-by-Step: Mounting Google Drive in Colab (2025)Additional Tips & Updates (as of August 2025)GPU & TPU Support (2025)Summary Google Colaboratory (Colab) remains one...

Alex Garcia announced the much-anticipated release of sqlite-vec v0.1.0. This new SQLite extension, written entirely in C, introduces a powerful vector search capability to...

A/B testing is a cornerstone of data science, essential for making informed business decisions and optimizing customer revenue. Here, we delve into six widely...

Time series data is used globally across various domains, including finance, healthcare, and sensor networks. Identifying patterns and anomalies within this data is crucial...

As we enter 2024, the field of data science continues to evolve rapidly, making it essential to stay updated with the latest knowledge and...

In today's time, the need to store enormous amounts of data in various frequently unrelated categories underlines the vital significance of highly efficient databases....

A big problem in space research is whether the same stars or galaxies are seen in different sky surveys. Telescopes today gather a ton...

The optimism that deep neural networks, particularly those based on the Transformer design, will speed up scientific discovery stems from their contributions to previously...

An increasingly popular method for representing data in a graph structure is the usage of knowledge graphs (KGs). A KG is a group of...

Julius AI Unlock the full potential of your data with Julius AI, an advanced yet user-friendly data analyst tool. Designed for accessibility, Julius AI is...

What are Data Contracts? A data contract is an agreement or set of rules defining how data should be structured and processed within a system....

In the fast-paced world of Artificial Intelligence (AI) and Machine Learning, staying updated with the latest trends, breakthroughs, and discussions is crucial. Reddit, the...

In the ever-evolving landscape of machine learning, ONNX (Open Neural Network Exchange) models have emerged as a pivotal technology, offering a standardized and flexible...

Artificial Intelligence has limitless possibilities, which is truly evident from the new releases and developments it introduces everyone to. With the release of the...

The most prevalent type of data is tabular data. This form contains many datasets from surveys, censuses, and administrative sources. These datasets could include...

The process of converting data into understandable pictures is known as data visualization. The visual depiction of numerical data using different graphs, charts, and...

Artificial intelligence and machine learning are now essential components in various tasks that contribute to a company's growth, such as marketing, thanks largely to...

Dimensionality reduction (DR) is a method for analyzing high-dimensional data that involves minimizing the number of variables taken into account. Data visualization in two...

The two of the biggest problems for both large and small enterprises are analysis and storage. To begin, the rate at which Big Data...

Data de-identification, a subset of dynamic data masking, disassociates data from the original person to whom it was tied. Data de-identification makes it possible...

Dynamic load balancing, adaptive caching, and the creation of comprehensive big data frameworks and libraries are all best done in C++. The vast majority...

Artificial intelligence has made waves throughout the past decade, where advancements are showing up in everyday applications. But getting there requires a ton of...

The phrase "data engineering tools" refers to a broad category of technologies that comprise the contemporary data stack. Modern data stacks require specialized technologies...

Most of the data is kept in a "data lake," a centralized and unprocessed area. A data lake uses a flat design and object...

Velox, a unified execution engine, was recently developed and made publicly available by Meta in association with Intel, ByteDance, and Ahana. This function library...

Big data describes the large, challenging volumes of structured and unstructured data that inundate businesses daily. However, what organizations do with the data matters...

A multidisciplinary research team has created a device that consumes a fraction of the energy needed by current general-purpose AI computing platforms to run...

Information representation technologies and innovations are required to dissect various data metrics and make the best information-driven decisions in the world of big data....

Data has become essential for businesses to comprehend and analyze underlying patterns, sales, and growth. One problem is that data-driven companies may employ inaccurate...

This Article is written as a summary by Marktechpost Staff based on the research article 'Intel Labs Accelerates Single-cell RNA-Seq Analysis'. All Credit For...

Biologists today create 10,000 times more data for each experiment than a decade ago, which is far more challenging to comprehend. Transforming this enormous...

Every change in a data-driven firm must be tested through trials to guarantee that it has a positive, measurable impact on key performance measures....

As databases grow in size, queries become slower. Database queries frequently contain multiple repetitive procedures that can be eliminated. To discover a full name...

This Article Is Based On The Cleanlab article 'cleanlab 2.0: Automatically Find Errors in ML Datasets'. All Credit For This Research Goes To The...

We have a plethora of AI models being developed by numerous businesses. But these require a lot of time and resources. Because high-performance AI...

This article is based on the research article from Linkedin Engineering 'Opal: Building a mutable dataset in data lake' Trusted data platforms and high-quality data...

Artificial intelligence is the way of the future. People are afraid of this since they've seen AI run amok in science fiction films like...

Scientists conduct trial and error procedures which experimenting, that many times lear to freat scientific breakthroughs. Similarly, foundational research provides for developing large-scale AI...

Data cleaning is the crucial process of identifying and resolving broken, inaccurate, or unnecessary data. Data defects include missing numbers, misplaced entries, and typographical...

Data scientists and analysts don't work alone. As a result, they must continuously check each other's progress in order to collaborate effectively. Sending files,...

Datacenter accelerators are pieces of hardware that are specifically built to process visual data. It's a physical device or software program that boosts a...

Probability is concerned with interpreting and comprehending life's random events. Randomness and uncertainty are present in our everyday lives and every field of research,...

LinkedIn is the world's largest professional network, producing enormous amounts of high-quality data. Data scientists and AI developers have been using various tools to...

Naveed Ahmed Janvekar is a Senior Data Scientist working at Amazon in the United States. He works on solving fraud and abuse problems on...

Understanding indoor 3D scenes are becoming increasingly important in augmented reality, robotics, photography, games, and real estate. Many state-of-the-art scene interpretation algorithms have lately...

In this tutorial, you will learn Python Logistic Regression. Here you’ll know what exactly is Logistic Regression and you'll also see an Example with...

Data augmentation Data augmentation in machine learning is a technique that helps reduce overfitting. It increases the amount of data by adding slightly modified copies...

Machine learning has been possible partly due to the accumulation of data, and within that data, an important step is that of data validation....

Researchers at the University of California San Diego developed a novel artificial neuron device, with the help of which training neural networks to perform...

Recently, NVIDIA launched its “NVIDIA Base Command,” a comprehensive platform that manages the end-to-end lifecycle of AI development and reporting dashboards. It is a...

Pyodide, a Mozilla's independent community-driven project, provides a full Python data science stack running entirely in the browser. It uses the CPython 3.8 interpreter...

Last year, Facebook AI released Dynabench, a platform that radically rethinks benchmarking in AI, starting with natural language processing (NLP) models. Going forward, they...

The approximation of the game’s San Andreas to the real-life Los Angeles and Southern California makes the Game Theft Auto V more special. But...