Data Science

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A Coding Guide to Implement a pgvector-Powered Semantic, Hybrid, Sparse, and Quantized Vector Search System

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Big Data, Data Science, Editors Pick, Staff, Technology, Tutorials

In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start by installing PostgreSQL, compiling the pgvector extension, connecting through Psycopg, and registering vector types for smooth Python integration. Then, we create embeddings with SentenceTransformers, store them […]

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How to Build Knowledge Graph Generation Pipelines From Text With kg-gen, NetworkX Analytics, and Interactive Visualizations

agentic ai, ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Editors Pick, Knowledge Graphs, Software engineering, Staff, Technology, Tutorials

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 required dependencies and configuring an LLM through LiteLLM, then we extract entities, predicates, and relationships from simple text. As we move forward, we work with longer passages using chunking and clustering,

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A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Machine Learning, Staff, Technology, Tutorials

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 tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches. We also examine how

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A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Software engineering, Staff, Technology, Tutorials

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 the available CUDA device, checking the CuPy version, runtime details, GPU memory, and compute capability so that we understand the hardware environment before running heavy computations. Then, we compare NumPy and

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A Coding Implementation to Portfolio Optimization with skfolio for Building Testing, Tuning, and Comparing Modern Investment Strategies

ai, AI (Artificial Intelligence), Artificial Intelligence, Data Science, Editors Pick, Staff, Technology, Tutorials

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 structured Python workflow. We start by loading S&P 500 price data, converting it into returns, and creating a time-based train-test split suitable for financial analysis. From there, we build simple baseline

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How to Build Technical Analysis and Backtesting Workflow with pandas-ta-classic, Strategy Signals, and Performance Metrics

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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 required libraries, downloading historical OHLCV stock data with yfinance, cleaning the returned data structure, and inspecting the available indicator categories inside the library. We then calculate popular indicators such as SMA,

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How to Build a Single-Cell RNA-seq Analysis Pipeline with Scanpy for PBMC Clustering, Annotation, and Trajectory Discovery

ai, AI (Artificial Intelligence), Applications, Artificial Intelligence, Big Data, Data Science, Editors Pick, Software engineering, Staff, Technology, Tutorials

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, inspecting its structure, and applying quality control checks to evaluate gene counts, total counts, mitochondrial content, and ribosomal gene signals. We then filter low-quality cells and genes, detect potential doublets with

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Meta FAIR Releases NeuralSet: A Python Package for Neuro-AI That Supports fMRI, M/EEG, Spikes, and HuggingFace Embeddings

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Researchers at Meta’s FAIR lab have released NeuralSet, a Python framework designed to eliminate one of the most persistent bottlenecks in Neuro-AI research: the painful, fragmented process of getting brain data into a deep learning pipeline. https://kingjr.github.io/files/neuralset.pdf The Problem: Neuroscience Data Is Stuck in the Pre-Deep-Learning Era Neuroscience already has excellent, battle-tested software. Tools like

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A Coding Implementation on Document Parsing Benchmarking with LlamaIndex ParseBench Using Python, Hugging Face, and Evaluation Metrics

ai, AI (Artificial Intelligence), Artificial Intelligence, Big Data, Data Science, Editors Pick, Software engineering, Staff, Technology, Tutorials

In this tutorial, we explore how to use the ParseBench dataset to evaluate document parsing systems in a structured, practical way. We begin by loading the dataset directly from Hugging Face, inspecting its multiple dimensions, such as text, tables, charts, and layout, and transforming it into a unified dataframe for deeper analysis. As we progress,

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