The article explains the difference between correlation and causation, illustrates three mechanisms that create coincident trends, introduces the Third‑Thing Test for hidden confounders, and offers practical questions to avoid common causal‑mistake traps in data‑driven decision making.
The article argues that Python remains the top programming choice in 2026 because its concise syntax, massive ecosystem, and modern tooling deliver unmatched development speed, lower total cost, and a balanced blend of rapid prototyping with long‑term stability for a wide range of applications.
This article explains the concept of heterogeneous treatment effects, outlines how to select dimensions for HTE analysis, describes a Python‑based MVP tool for automated CATE exploration, and showcases a real‑world experiment case where sub‑group insights turned a non‑significant overall result into actionable findings.
Discover ten lesser‑known Jupyter Notebook features—from magic commands that list variables and benchmark code to shortcuts, export utilities, and interactive help—that turn a simple notebook into a highly efficient, production‑ready data‑science workspace.
Python remains a top language in 2025 thanks to its simple syntax, massive library ecosystem and broad applicability, and this article curates ten GitHub repositories—ranging from AI and data‑science tutorials to automation scripts and beginner‑friendly projects—each explained with concrete reasons why they’re valuable for learning and building real‑world applications.
This article explains the core ideas, mathematical steps, geometric intuition, and practical differences between Principal Component Analysis and Factor Analysis, guiding readers on when to apply each technique for dimensionality reduction and latent structure discovery in high‑dimensional data.
This concise guide presents twelve core AI algorithms—from gradient boosting and deep neural networks to decision trees and K‑nearest neighbors—detailing their strengths, typical applications such as fraud detection, image classification, and price forecasting, and offering practical tips for selecting the right model.
In this reflective case study, a first‑time undergraduate shares how competing in the 2025 China University Big Data Challenge—predicting Shanghai‑Shenzhen 300 index component movements—deepened his understanding of structured time‑series data processing, algorithm adaptability, iterative model optimization, and the broader value of data‑driven problem solving.
This article compiles a comprehensive list of high‑quality, publicly available datasets across domains such as general platforms, education, finance, health, text, and vision, providing URLs, key features, and practical usage tips to help researchers and practitioners quickly find the right data for their AI and data‑science projects.
This article introduces the book “Intelligent Supply Chain: Data Science Theory and Practice”, outlining how data science drives end‑to‑end supply‑chain optimization through real‑world case studies, covering topics from data preprocessing to advanced modeling, delivery efficiency, and customer‑service forecasting.
The article introduces Meituan’s trusted experiment analysis engine, detailing its rich methodological library, system architecture, integration options, and a step‑by‑step offline analysis case that together empower teams to conduct reliable, efficient A/B tests without deep statistical expertise.
The 2025 PSF and JetBrains Python Developer Survey, the largest ever with over 30,000 participants, shows rising professional use, a shift toward newer Python versions, growing adoption in web and data science, and funding challenges for the Python Software Foundation.
The article explains why Python’s popularity is soaring—its pivotal role in AI, rich ecosystem of libraries, and ease of use—while walking readers through its history, key milestones, and step‑by‑step instructions for installing Python, setting up Conda, using pip, and choosing development tools like PyCharm.
This article walks readers through the fundamentals of artificial intelligence, covering AI, machine learning, deep learning, data types, linear regression, supervised and unsupervised learning, reinforcement learning, feature engineering, tokenization, vectorization, embeddings, and includes a practical Word2Vec code example.
This article argues that successful programming and data‑science projects begin with clear problem definition, logical planning, and simple models before any code is written, emphasizing thinking over tools to ensure transparent, maintainable solutions.
This essay explains that every modeling choice rests on hidden assumptions that encode the modeler's values, showing how different assumptions lead to different perspectives, ethical trade‑offs, and ultimately influence real‑world decisions across domains such as education, insurance, and public policy.
The May 2024 TIOBE ranking shows Python soaring to a record 25.35% share, widening the gap over C++ and cementing its dominance in AI, data science, and automation, while highlighting the continued relevance of languages like Java, C++, and R for various development needs.
The author recounts attending PyCon LT 2025 Data Day, summarizing talks on building a simple static code analyzer with AST, challenges of data warehouses versus data lakes, cloud cost‑scraping pipelines, A/B testing libraries, privacy‑enhancing data processing, and tools like Panel and Dagster, while noting the inspiring presence of female speakers.
This guide introduces a wide range of Python libraries and tools—including Flask, Django, FastAPI, Requests, HTTPX, SQLAlchemy, Pandas, NumPy, Scikit‑learn, TensorFlow, PyTorch, Pillow, OpenCV, spaCy, Click, asyncio, pytest, and logging—providing concise descriptions and ready‑to‑run code examples for each domain.
This article introduces ten major fields where Python excels—including web development, data science, automation, web crawling, game development, desktop applications, DevOps, IoT, blockchain, and education—provides brief overviews, lists common tools or libraries, and supplies concise code examples for each use case.
This article presents a curated list of 100 essential Python libraries covering web development, AI and machine learning, data science, automation, DevOps, security, databases, game development, and various utility tools, helping developers boost productivity across diverse projects.
This guide outlines the essential foundations for becoming a data science professional, debunking common myths, highlighting three core skill areas—mathematics, machine learning, and programming—and detailing key concepts such as linear algebra, probability, calculus, and essential Python libraries.
The MktAI Assistant combines LLM‑powered memory, skill planning, and tool‑calling with real‑time API data to replace slow, manual SQL dashboards, delivering sub‑minute, fresh, explainable marketing queries and attribution insights that boost decision speed, accuracy, and collaboration between data scientists and business users.
This article presents a concise tutorial on essential Python data science libraries, covering data cleaning with Pandas, numerical analysis with NumPy and SciPy, visualization with Matplotlib and Seaborn, machine learning with scikit‑learn, NLP with NLTK and spaCy, time‑series modeling, image processing, database access, and parallel computing, each illustrated with ready‑to‑run code examples.
This article introduces ten essential Python libraries—including SciPy, Matplotlib, Plotly, Scikit‑learn, TensorFlow, spaCy, BeautifulSoup, OpenPyXL, Feather/Parquet, and SQLAlchemy—detailing their primary uses for scientific computing, visualization, machine learning, deep learning, NLP, web scraping, Excel handling, efficient data storage, and ORM, with practical code examples.
The AI Expert Roadmap on GitHub offers a comprehensive, interactive guide covering data‑science fundamentals, machine‑learning algorithms, deep‑learning techniques, data‑engineering pipelines, and big‑data architectures, with linked resources, up‑to‑date references, and practical tool recommendations for aspiring AI professionals.
The January 2025 TIOBE Index reveals Python as the 2024 programming language of the year with a 9.3% growth, while C declines, C++ and Java surge, PHP exits the top ten, and Rust, Kotlin, and Go show varied trajectories, underscoring Python’s dominance across data science, web development, and automation.
This article compares Python and R for machine learning projects, outlining each language’s strengths, weaknesses, typical use cases, and offering guidance on when to use Python for data preprocessing and R for modeling, while also highlighting community adoption and library support.
This article presents a comprehensive guide to 45 essential NumPy operations, covering array creation, reshaping, arithmetic, statistical functions, linear algebra, and more, each illustrated with concise explanations and ready-to-run Python code examples to help readers efficiently leverage NumPy for scientific computing.
This article introduces 24 highly useful Python libraries that cover the entire data‑science lifecycle—from data collection and cleaning to visualization, modeling, interpretation, and deployment—helping readers build a comprehensive and visually appealing data‑analysis pipeline.
This article provides a comprehensive guide to using Scikit-Learn for data modeling, covering linear regression and decision tree algorithms, including data preparation, model training, evaluation metrics, and visualization techniques for predictive analysis.
This article introduces the Python "communities" library, explains its support for multiple graph clustering algorithms such as Louvain and Girvan‑Newman, demonstrates how to import algorithms, build adjacency matrices, visualize communities, create animation of the clustering process, and provides author and resource information.
This tutorial demonstrates how to use Matplotlib for basic data visualization, including creating line charts, scatter plots, and bar charts with practical Python code examples.
This tutorial introduces NumPy array creation, manipulation, and fundamental mathematical operations, providing step‑by‑step code examples for importing the library, generating arrays with various functions, reshaping, indexing, slicing, and computing mean, variance, and standard deviation.
This article explains the fundamentals of data science, its key components, and showcases how JD applies it across e‑commerce, finance, healthcare, and logistics, while also reviewing past innovations, common project pitfalls, and future directions such as quantum computing and supply‑chain digital twins.
The 2024 GitHub Octoverse report reveals that Python has surpassed JavaScript as the most used language, driven by a surge in generative AI projects and a 92% rise in Jupyter Notebook usage, highlighting the expanding role of AI and data‑science communities on the platform.
This article provides a curated collection of Python libraries spanning general utilities, file handling, image processing, game development, data science, GUI frameworks, testing tools, and deployment utilities, each accompanied by a concise description of its primary purpose and typical use cases.
While Python dominates data analysis and machine learning with its ease of use, Rust offers memory safety and near‑C performance; this article examines their respective strengths, the challenges of rewriting the Python interpreter in Rust, and how combining both can boost library speed and reliability.
This article presents a curated list of fifty essential Python libraries spanning data analysis, scientific computing, visualization, machine learning, web development, database access, testing, and utilities, providing brief descriptions to help developers quickly identify and master the most useful tools in the Python ecosystem.
This guide walks through ten essential advanced SQL concepts—including CTEs, recursive queries, temporary functions, CASE‑based pivoting, EXCEPT vs NOT IN, self‑joins, ranking functions, delta calculations, cumulative totals, and date‑time manipulation—providing clear explanations and runnable examples to help data‑science professionals ace interview challenges.
This guide walks through ten essential advanced SQL techniques—including CTEs, recursive CTEs, temporary functions, CASE‑WHEN pivots, EXCEPT vs NOT IN, self‑joins, ranking functions, delta calculations with LAG/LEAD, cumulative sums, and date‑time manipulation—to help data professionals ace interview challenges and write cleaner, more powerful queries.
The 2024 JetBrains and Python Software Foundation survey of 25,000 developers reveals Python's continued dominance, detailed demographics, tool preferences, language trends, age distribution, web and data‑science usage, cloud adoption, and security insights, highlighting why newcomers still favor Python as their first language.
An in‑depth comparison of Python, Julia, and Rust reveals each language’s strengths and weaknesses for data‑science tasks, covering ease of development, library ecosystems, performance, deployment challenges, and suitability for rapid prototyping versus high‑performance, memory‑safe applications.
This article compares Python, Julia, and Rust—the three leading languages in data science—detailing their strengths, ecosystem support, performance, and deployment challenges to help developers choose the most suitable tool for their projects.
This article explores how power‑law and other heavy‑tailed distributions appear in content ecosystems, explains their statistical foundations, discusses why they are common, and presents data‑driven strategies—including integer programming, graph‑based creator analysis, and causal inference—to optimize content production, recommendation, and settlement policies.
This article presents a comprehensive knowledge map of causal inference covering its overall framework, how to evaluate decision‑making scenarios, typical causal algorithms, practical challenges in deployment, a JD Tech credit‑limit case study, and future research directions.
Positron, the new beta IDE from Posit built on Visual Studio Code, offers a ready‑to‑use, cross‑platform environment for R and Python with integrated data exploration tools, seamless language switching, and easy extension management via OpenVSX, while still being an early‑stage project.
This article introduces eight Python libraries—Optuna, ITMO_FS, Shap-hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch-Handle—that streamline data science tasks such as hyperparameter optimization, feature selection, model building, visualization, and rapid prototyping, helping users save coding time and improve productivity.
This article explores the origins, key innovations, and lasting impact of Jupyter notebooks, highlighting how their multi‑language support, interactive computing, reproducibility, and extensibility have transformed data exploration, collaboration, education, and research in modern data science and machine learning.
This article introduces the open‑source Python library TRACC, explains how to install it, prepare destination and travel‑cost data, and walk through a step‑by‑step workflow—including code snippets—to compute potential, passive, and minimum travel‑cost accessibility metrics for urban areas.
Since its 2009 debut, Go’s simple syntax, strong concurrency, and performance have driven its adoption, and this article examines how those strengths position Go for future growth in cloud computing, microservices, blockchain, data science, IoT, and web/API development.
This talk explains how combining heavy randomization with regression adjustment can effectively mitigate AA problems in A/B testing, improving experiment credibility by addressing covariate imbalance and enhancing result validity for data‑driven decision making.
This article introduces and demonstrates more than twenty widely used Python libraries for artificial intelligence, computer vision, natural language processing, and data analysis, providing concise explanations and runnable code snippets that illustrate each library's core functionality and typical use cases.
This article provides a comprehensive introduction to popular Python libraries for artificial intelligence, computer vision, data analysis, and machine learning—such as NumPy, OpenCV, scikit‑image, Pillow, TensorFlow, PyTorch, and many others—accompanied by concise code snippets and performance comparisons to help beginners select suitable tools.
This article introduces eight Python libraries—including Optuna, ITMO_FS, shap‑hypetune, PyCaret, floWeaver, Gradio, Terality, and Torch‑Handle—that streamline data‑science tasks such as hyperparameter optimization, feature selection, model building, visualization, and deployment, helping users save coding time and improve productivity.
This article explains why current AI cannot achieve self‑awareness, outlines data‑science steps for large models—including preprocessing, exploratory analysis, modeling, and evaluation—then surveys general and vertical applications of large language models and details a complete machine‑learning workflow with transformer fine‑tuning techniques.
Discover the essential Python libraries for data science in 2024, from versatile tools like Taipy and Pandas to powerful machine‑learning frameworks such as TensorFlow, PyTorch, and Scikit‑Learn, each with key features, use‑cases, and GitHub links to boost your analytics career.
This tutorial provides a visual, step‑by‑step guide to NumPy, covering array creation, arithmetic and broadcasting, indexing, aggregation, matrix operations, reshaping, and practical examples such as computing mean‑squared error for machine‑learning models, illustrated with code snippets and diagrams.
Taipy is a free, open‑source Python framework that lets data scientists and machine‑learning engineers create full‑stack, production‑grade web applications without writing HTML, JavaScript or CSS, offering three components—frontend, backend pipeline, and REST API—plus integration with common data sources and low‑code UI tools.
This article explains the concept of mediation effects, outlines how to select mediators, describes step‑by‑step testing procedures—including the classic Baron‑Kenny approach and modern bootstrap methods—and demonstrates the whole workflow with a real‑world economic example.
This article explains how to build user portrait tags—from basic attribute tags to business and strategy tags—covers methods for data collection, anomaly handling, time decay, smoothing, and evaluates tag quality using cohesion, stability, and AUC-related metrics to support data‑driven product decisions.
This article provides a comprehensive introduction to popular Python libraries used in artificial intelligence, such as NumPy, OpenCV, scikit-image, Pillow, SimpleCV, Mahotas, Ilastik, Scikit-learn, SciPy, NLTK, spaCy, LibROSA, Pandas, Matplotlib, Seaborn, Orange, PyBrain, Theano, Keras, Caffe, MXNet, PaddlePaddle, CNTK, and more, including code snippets and usage examples.
This article introduces key Python libraries for data analysis, visualization, statistical modeling, and machine learning—including NumPy, Pandas, Matplotlib, Seaborn, SciPy, Statsmodels, Scikit-learn, BeautifulSoup, TensorFlow, and Plotly—each accompanied by concise code examples demonstrating their core functionality.
This article explains a chi‑square‑based method for detecting and removing extreme‑value orders in freight AB experiments to preserve group homogeneity, improve metric reliability, and enhance the scientific validity of experimental conclusions.
While A/B testing is widely used for data-driven decisions, short-term experimental results often diverge from long-term impacts, leading to misguided strategies; this article examines why such inconsistencies arise and reviews major methods—including extended experiments, holdout groups, post‑analysis, CCD, and surrogate‑metric modeling—to reliably estimate long‑term effects.
Huya’s data‑driven experiment platform showcases how causal inference, AB testing, and uplift modeling are applied to advertising, user activation, and growth scenarios, detailing platform evolution, metric design, statistical challenges, and practical solutions such as multi‑test correction, CUPED, RTA, and propensity‑score methods.
Taobao’s Technical Industry Data team designed a four‑level VOC tagging hierarchy to unify fragmented user‑feedback sources, evaluated label similarity with vector‑based distance matrices, optimized tag groups via entropy‑driven re‑grouping, built a stacking ensemble of FastText and TextCNN achieving over 90% accuracy, and deployed an automated production pipeline that generates tags, maintains ODPS tables, and provides APIs for rapid experimentation.
This article explores how freight platforms can optimize transaction strategies through AB experiments, detailing common challenges such as split‑testing interference, SUTVA violations, capacity competition, homogeneity issues, and Simpson's paradox, and presents practical solutions like time‑slice routing, city isolation, and advanced statistical corrections.
The article explains how rapid advances in data and large language models force mathematical modelers to continuously update their models and skills, discard obsolete knowledge, and adopt lifelong learning to stay effective in a fast‑changing AI‑driven environment.
The article surveys the most important data‑science tools—including Jupyter Notebooks, notebook lab platforms, RStudio, Sweave/Knitr, IDEs, domain‑specific solutions, hardware, and data sources—explaining how they support modern, real‑time analytics and help organizations turn raw data into actionable insights.
This article examines the unique characteristics of freight two‑sided markets, outlines the experimental challenges across transaction, pricing, marketing, and product scenarios, and presents a comprehensive technical framework—including allocation strategies, homogeneity controls, efficient interpretation, and observational study methods—to achieve reliable, actionable insights.
The 2022 JetBrains Python Developer Survey of over 23,000 respondents shows 93% now use Python 3, highlights the decline of Python 2, rising popularity of FastAPI, dominant IDEs like PyCharm and VS Code, and shifting preferences in frameworks, databases, big‑data tools, and operating systems.
The DataFunSummit2023 online conference brings together experts from Tencent and Kuaishou to present cutting‑edge research on causal inference for large‑scale A/B testing, including deep‑learning‑based multi‑experiment effect estimation, a distributed causal inference framework (Fast‑Causal‑Inference), and strategies for evaluating long‑term policy impacts.
The article explains the importance of data analysis for business insight, problem detection, decision support, operational optimization, forecasting, and competitiveness, and then presents ten practical Python code scenarios covering data loading, cleaning, filtering, aggregation, visualization, statistics, transformation, time‑series analysis, export, and machine‑learning applications.
The fourth article in a ten‑part Taobao series introduces the BPPISE framework—Business, Problem, Data, Insight, Strategy, Evaluation—as a product‑data‑science case structure, contrasting it with CRISP‑DM, detailing each stage, offering writing tips, and noting the team’s recruitment for data‑science talent.
This article compiles a comprehensive alphabetically ordered list of 75 crucial data science and machine learning terms—from accuracy and AUC to zero-shot learning—providing concise definitions that help practitioners quickly grasp essential concepts and improve their analytical vocabulary.
The August 2023 TIOBE ranking announced Julia's debut in the top‑20, highlighting its high‑performance, cross‑platform nature and extensive use in scientific computing, data science, machine learning, finance, engineering simulation, big data processing, and scientific visualization.
This article provides a concise yet thorough introduction to the most commonly used Python AI libraries—including NumPy, OpenCV, scikit‑image, Pillow, SimpleCV, Mahotas, Ilastik, scikit‑learn, LibROSA, Pandas, Matplotlib, Seaborn, Orange, PyBrain, Milk, TensorFlow, PyTorch, Theano, Keras, MXNet, PaddlePaddle and CNTK—explaining their main features and offering ready‑to‑run code examples for each.
This tutorial demonstrates how to use Python's Seaborn library to create a variety of attractive statistical charts—including bar, scatter, line, box, histogram, heatmap, violin, and KDE plots—by importing the library, setting styles, and executing concise code snippets for each chart type.
This article explains how Tencent leverages A/B testing across its overseas games, detailing the current market situation, experimental capabilities, multi‑cloud platform architecture, and case studies that illustrate how data‑driven experiments improve user retention, engagement, and overall business growth.
IntelliJ IDEA's Kotlin Notebook plugin brings interactive notebooks to Kotlin, enabling developers to combine code, visualizations, and narrative in a single document, supporting rapid prototyping, data analysis, and AI workflows with seamless library integration and flexible execution controls.
This article introduces ten widely used Python libraries—NumPy, Pandas, Matplotlib, Scikit‑learn, Requests, Beautiful Soup, Selenium, Pygame, Flask, and PyTorch—each accompanied by a concise real‑world project and complete code examples to help readers understand and apply them effectively.
JupyterLab 4.0 introduces a flexible multi‑document interface, customizable layouts, an integrated file browser, modular extensibility, built‑in terminal, significant performance improvements, a separate real‑time collaboration package, and a PyPI‑based extension manager, while noting that AI‑assisted coding tools may now outpace it.
This article reviews a decade of data‑science growth, defines data science as a multidisciplinary field, outlines its four high‑level and fourteen low‑level work processes, categorises nine distinct data‑science roles, and discusses how these insights should shape the next generation of data‑visualisation and analysis tools.
The article introduces Mojo, a new programming language positioned as a superset of Python that offers Rust‑like speed and safety through ahead‑of‑time compilation, discusses its early‑stage features, performance claims, online playground, and evaluates its potential to complement or replace Python in data‑science and high‑performance scenarios.
This article provides an extensive tutorial on NumPy, covering array creation functions such as array, linspace, arange, and random generators, a wide range of array operations including reshaping, slicing, statistical calculations, set operations, splitting, stacking, comparison, repetition, and data persistence, all illustrated with clear Python code examples.
Using eight years of Chinese recruitment data, the article analyses how large language models like GPT could automate specific tasks across thousands of jobs, presents the most and least vulnerable occupations, and explores the relationship between wage growth, job experience, and AI replaceability.
The article examines how ChatGPT automates many manual tasks, reducing demand for translators, editors, customer support reps, and data analysts, while boosting opportunities for chatbot developers, NLP engineers, data scientists, content creators, and software developers, and outlines broader industry advancements driven by AI.
This article introduces the No‑Free‑Lunch principle in machine learning and provides concise explanations of ten fundamental supervised‑learning algorithms—including linear regression, logistic regression, LDA, decision trees, Naïve Bayes, K‑Nearest Neighbors, LVQ, SVM, random forest, and boosting—highlighting their mathematical basis, typical applications, advantages, and limitations.
Business analytics, data science, and related fields are gaining prominence, and this article outlines a straightforward three‑layer classification—descriptive, predictive, and prescriptive analytics—explaining their purposes, techniques, and how organizations progress through these maturity levels to turn data into actionable insights.
This article explains how to design and evaluate experiments for bilateral markets in advertising platforms, covering the limitations of traditional randomization, the four‑cell traffic‑advertisement experiment, various mitigation strategies such as counterfactual interleaving and joint sampling, and the use of a simulation system to validate methods.
This article presents the JD Retail search data‑science team's practical approaches to e‑commerce search, covering the scene’s unique data characteristics, order attribution methods, AB experiment design, causal‑inference frameworks, variance‑reduction techniques, quasi‑experimental evaluations, and metric design for traffic distribution, all illustrated with real‑world examples and visualizations.
The article uses a dating analogy to illustrate how enterprise data strategy must combine clean, high‑quality data, governance, and analytics with clear tactical components to support strategic goals, drive market advantage, and enable reliable, mission‑focused outcomes in the experience economy.
The study applies machine‑learning models—including logistic regression, SVM, random forest, deep neural networks and a DNN‑SVM ensemble—to 17‑dimensional team features and 51‑dimensional bookmaker odds, achieving up to 54.5% match‑outcome accuracy, proposing a profit‑condition betting strategy and extending the approach to stock‑price forecasting.
On November 26, 2022, at the DataFun Summit 2022 online causal inference conference, PhD candidate Wan Shu from Arizona State University will present a talk titled “Construction of Generalized Causal Forests and Their Application in Online Transaction Markets,” covering treatment effect estimation, model building, performance comparison, and practical use cases.
Discover the ten highest‑paying technology positions in 2022/2023, from software architects to AI architects, with detailed role descriptions, required skills, and average salaries in the United States and China.
The article describes Tubi’s internal experimentation platform, explaining how traditional user‑group A/B tests can suffer from network interference and how Switchback experiments—time‑window based designs—address these issues, detailing their implementation, statistical methods, and the practical challenges overcome.
This article presents a detailed curriculum covering programming fundamentals, web development (HTML, CSS, JavaScript, jQuery), backend frameworks (Flask, Django), database concepts, data analysis with Python (NumPy, pandas, matplotlib, seaborn), machine learning, AI, and related project tutorials, along with a brief promotional note.
This article compiles a curated list of Python programming fundamentals and popular third‑party libraries for data processing, visualization, scientific computing, optimization, and more, linking to detailed tutorials for each topic, including Numpy, Matplotlib, Pandas, Scipy, PuLP, NetworkX, Geatpy, Numba, and Taichi.
This article explains how statistics—through data collection, exploratory analysis, descriptive metrics, visualization, and model‑centric inference—provides a framework for understanding and predicting phenomena, emphasizing the role of programming (e.g., Python) and Bayesian modeling principles such as simplicity and the Occam razor.
