This guide walks you through installing Jupyter Notebook extensions, explains why they improve workflow, and highlights five must‑have add‑ons—including Table of Contents, Autopep8, Variable Inspector, ExecuteTime, and Hide Code—to streamline data‑science tasks.
This article explains what software frameworks are, why they are essential for developers, outlines the main types—including frontend, backend, mobile, and data‑science frameworks—and offers practical advice on how beginners can start learning them effectively.
This guide explains how to launch Jupyter Notebook, open existing notebook files, and begin working with them, providing clear step‑by‑step instructions for users new to the interactive computing environment, including tips on navigating the interface and saving your work.
The second virtual research meeting of China’s Data Science Curriculum Group gathered nearly a hundred educators and industry partners in Beijing to discuss new models for big‑data course design, curriculum construction, industry‑academia collaboration, and digital teaching platforms across multiple universities.
This article introduces ten powerful Python libraries—including Awkward Array, Jupytext, Gradio, Hub, AugLy, Evidently, YOLOX, LightSeq, Greykite, and Jina/Finetuner—highlighting their key features, performance benefits, and where to find them, offering developers essential tools for data handling, model deployment, and AI research.
This article explains the origins and core concepts of factor analysis, outlines its algorithmic steps, demonstrates how to perform the analysis using Python's factor_analyzer library—including data preparation, adequacy tests, eigenvalue selection, rotation, and visualization—culminating in extracting new latent variables.
The inaugural "Yujian Cup" Medical Testing AI Developer Competition, co‑hosted by Jinyu Medical and Huawei Cloud, gathered 405 international teams, showcased cutting‑edge AI algorithms on real pathology data, awarded a 100,000 CNY prize to the winning team, and outlined plans for future annual events to accelerate AI‑driven healthcare innovation.
The article explains Augmented Analysis—using AI‑driven natural‑language queries, intelligent assistants, and automated business insights—to enable non‑technical users to explore data, gain actionable recommendations, and boost business value, illustrated with real‑world use cases and practical guidance for embedding the technology into everyday workflows.
The Ministry of Education launched the national "Data Science Curriculum Group" virtual teaching and research office in Yinchuan, gathering over 70 universities, publishers and industry partners to discuss curriculum construction, talent training plans, core courses, high‑quality textbooks, Huawei Cloud support, and the development of domestic database systems for higher education.
This article introduces ten essential JupyterLab extensions—ranging from a visual debugger and table‑of‑contents generator to system monitoring and AI‑powered code completion—that can dramatically improve productivity for Python data scientists and engineers.
This article outlines a systematic three‑step framework—visual appeal, standout optimization, and comprehensive experiments—to help data‑science teams package and present their models effectively in competitions, complete with practical tips, visual examples, and a GitHub resource for creating compelling model graphics.
This article introduces ten essential JupyterLab extensions—ranging from a debugger and table of contents to spreadsheet integration, system monitoring, AI‑powered code completion, variable inspection, and interactive plotting—that together transform the JupyterLab environment into a more powerful, IDE‑like workspace for Python data‑science developers.
Explore a thorough NumPy tutorial covering array iteration with nditer, reshaping functions like reshape, flat, and flatten, dimension modifications, transposition, axis swapping, broadcasting, stacking, concatenation, splitting, element manipulation, string utilities, arithmetic, statistical, sorting, searching, and file I/O, all illustrated with clear Python code examples.
This guide demonstrates how to list and apply the 26 built‑in Matplotlib plotting styles, visualizes each style in a grid, and shows how to configure Chinese fonts to correctly display non‑ASCII characters in your figures.
In a June 1 online salon hosted by the Tsinghua‑Meituan Digital Life Joint Research Institute, Prof. Li Yong presented a novel multi‑agent urban mobility model that links individual movement and social interaction to reproduce city‑scale fractal laws such as Zipf’s distribution, super‑linear scaling, and negative‑exponential population patterns, illustrating a new bridge between micro‑level behavior and macro‑level urban evolution.
This article introduces fifteen widely used Python libraries covering data collection, cleaning, visualization, machine‑learning, and web development, providing a comprehensive toolkit for data scientists and analysts to build end‑to‑end data pipelines.
SAS announced native Python support in its analytics platform, offering code examples, cloud‑native AI capabilities, and seamless integration with SAS Studio and other SAS products to empower data scientists to develop and deploy Python models alongside SAS code.
The DataFun Summit 2022 features an Experimental Science and Causal Inference forum where leading data scientists from Didi, Tencent, Google, ByteDance, and others present deep technical talks on causal inference methods, A/B testing, game operations, and advertising experiments, offering practical insights and audience takeaways.
The article examines the challenges advertisers face in a monopolized traffic environment, presents a three‑layer audience insight system, explores graph‑based relationship network methods, and discusses various modeling approaches—including single‑stage, cross‑stage, and multi‑task learning—along with practical considerations for data security and platform integration.
NetEase Yanxuan built an end‑to‑end time‑series forecasting system for e‑commerce that integrates rich user, product, business and external features with a suite of statistical, machine‑learning and deep‑learning models, delivers predictions via a Tornado‑based service for thousands of SKUs, warehouses, advertising and app traffic, and shows that simpler models like XGBoost often outperform complex deep nets while interpretability and external shocks remain key challenges.
This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics such as KS and PSI, handling pandemic effects, and methods for model explainability.
The article reviews a decade‑long growth of data science, defines its multidisciplinary nature, outlines the four high‑level and fourteen low‑level processes, describes nine distinct data‑science roles, and discusses how these insights can guide the design of next‑generation data‑visualization and analysis tools.
This presentation explores how to scientifically define, apply, evaluate, and interpret data in financial risk management, covering data alignment with business goals, feature selection, model metrics like KS and PSI, handling pandemic impacts, and methods for model explanation and improvement.
This article provides a comprehensive overview of machine learning, explaining its definition, relationship to AI, workflow, historical evolution, major paradigms, real‑world applications, and the most common algorithms with practical usage tips.
Microsoft offers four open‑source, 12‑week beginner courses on GitHub—Web Development, Machine Learning, Internet of Things, and Data Science—each with over 20 lessons, hands‑on projects, quizzes, assignments, and publicly available repositories, providing a comprehensive entry point for aspiring developers and data professionals.
This article presents a comprehensive collection of 100 common NumPy tasks, each accompanied by concise explanations and ready‑to‑run Python code examples that demonstrate array creation, manipulation, mathematical operations, indexing, broadcasting, and advanced techniques for scientific computing.
This article reviews the seven most notable Python libraries and packages of 2018 for data scientists and AI practitioners, including AdaNet, TPOT, SHAP, Optimus, spaCy, Jupytext, and Chartify, with descriptions, installation commands, and usage examples.
The 2021 Alibaba Mama Technology review, covering its May launch and 237‑day output of 50 technical articles on advertising algorithms, system engineering, intelligent creativity, risk control, plus two open‑source projects and numerous SIGIR, KDD, CIKM and CVPR papers, highlights data‑science, marketing series and engineer‑growth stories.
This article surveys the ten most prominent Python libraries for machine learning released in 2021, highlighting both internationally popular and high‑performing Chinese open‑source projects, and explains their main features, performance advantages, and where to find them on GitHub.
This talk by Meituan data analyst Wang Qing explains why diagnostic analytics is essential, outlines its methodology using logical trees and hypothesis-driven approaches, and presents two case studies—weather index modeling and an intelligent anomaly detection system—to illustrate how data-driven diagnosis can pinpoint root causes and improve decision‑making in online food delivery.
This article examines industrial intelligence from the perspectives of flow and fusion, detailing its current state, talent needs, pain points, AI development processes, edge‑cloud architecture, and key characteristics such as timeliness, reliability, explainability, and applicability in manufacturing.
This comprehensive guide walks you through Jupyter Notebook’s web‑based interactive computing features, major users, installation via Anaconda, launching, dual command/edit modes, core functionalities, useful extensions, keyboard shortcuts, magic commands, and theme customization, providing practical code snippets and visual examples for Python developers.
This article distills complex marketing analysis into a concise, data‑driven framework by revisiting classic models, introducing simplified 4P/4C/4R concepts, and presenting the C.M.O. (Customer Insight, Multi‑Touch Attribution, Opportunity) methodology for actionable, high‑dimensional insight generation.
The presentation offers a comprehensive overview of online experiment fundamentals, design variations, and real-world case studies from Tencent Lookpoint, emphasizing hypothesis validation, causal analysis, best practices, and actionable recommendations for improving product growth and decision‑making.
This article explains why diagnostic analytics is essential, defines the concept, outlines problem types, presents a logical‑tree and hypothesis‑driven methodology, showcases two real‑world projects (weather‑impact index and an intelligent anomaly‑diagnosis system), and highlights frequent mistakes to avoid.
The article explains how to design experiment evaluation metrics—from top‑down objectives to core, quality, and observation types—and construct an Overall Evaluation Criterion by processing, weighting, and aggregating metrics, providing a robust, scalable framework for credible AB‑test assessment and product optimization.
The article explains how the non‑parametric Bootstrap resampling method provides a practical, computationally efficient way to perform statistical inference in AB testing—especially with small samples, skewed data, or ratio metrics—by generating confidence intervals and hypothesis tests via repeated sampling, outperforming traditional approaches.
This article presents a comprehensive, categorized collection of essential technical books covering frontend development, backend engineering, mobile app creation, server architecture, testing practices, multimedia processing, computer vision, data mining, recommendation systems, and 3D/AR technologies, offering readers valuable resources for deepening their expertise across the software development spectrum.
This article explains why mastering SQL and PostgreSQL is essential for data scientists, outlines the core skills of the role, describes PostgreSQL’s features, lists its advantages and drawbacks for data science, and suggests resources for getting started.
The 2021 State of Data Science report shows how COVID‑19 has impacted investment, highlights Python's dominance, examines automation's growing role, and reveals corporate attitudes toward open‑source contributions, offering data‑driven insights for professionals and educators alike.
Julia, the high‑performance language created at MIT, has secured $24 million in Series A funding, is used by thousands of top companies, offers some of the highest developer salaries, and now has a curated list of free courses and books for beginners.
The 2021 State of Data Science report by Anaconda analyzes how COVID‑19 influences investment, highlights that 63% of respondents frequently use Python, shows automation is viewed as a complementary tool, and reveals mixed corporate attitudes toward open‑source contributions.
This article presents a collection of straightforward yet practical Python functions for data cleaning tasks—including dropping columns, changing data types, converting categorical variables, handling missing values, removing unwanted characters, trimming whitespace, conditional concatenation, and converting string timestamps—designed to streamline preprocessing in data analysis projects.
NumPy arrays are homogeneous, memory‑efficient data structures that outperform Python lists, offering fast creation, shape and rank metadata, and indexed access; this article explains why to use NumPy, defines arrays, and demonstrates array creation and element retrieval with example code.
This comprehensive tutorial introduces Pandas—the powerful Python library for data manipulation and analysis—covers installation, data import, inspection, cleaning, indexing, selection, sorting, grouping, transformation, statistical functions, visualization, and exporting, all illustrated with clear code examples and visual outputs.
This article presents an extensive glossary of more than fifty big‑data concepts—including Apache projects, data‑analysis methods, storage formats, AI‑related terms, and emerging metrics—providing concise English explanations for each term.
This article introduces eight widely used Python tools—including IDLE, Scikit-learn, Theano, Selenium, TestComplete, Beautiful Soup, Pandas, and PuLP—detailing their primary features, typical use cases in data science, web automation, and optimization, and offering guidance for programmers and students seeking to enhance their workflow.
This article introduces a curated selection of lesser‑known Python libraries for data‑science tasks—including data acquisition, date‑time handling, imbalanced‑learning, fast keyword extraction, fuzzy string matching, time‑series modeling, 3‑D visualization, web‑app building, and reinforcement‑learning—providing installation commands and concise usage examples.
AWS introduces a new EMR Studio IDE to accelerate data science workflows, while the Linux community bans University of Minnesota contributions over malicious patches and Google Chrome adopts Intel‑Microsoft hardware‑enforced stack protection to harden browser security.
Python has become the go‑to language for AI and machine learning across startups and enterprises because of its rapid prototyping, flexible syntax, readability, extensive libraries like NumPy, SciPy, scikit‑learn, Pandas, Keras, and powerful visualization tools, making development faster, scalable, and easier to maintain.
This article introduces Python as a beginner‑friendly, multi‑purpose language and presents eleven notable Python compilers and interpreters—including Brython, Pyjs, WinPython, Skulpt, Shed Skin, ActivePython, Transcrypt, Nutika, Jython, CPython, and IronPython—highlighting their main features, platforms, and typical use cases.
This article provides a visual, step‑by‑step introduction to NumPy’s core concepts—vectors, matrices, higher‑dimensional arrays, creation, indexing, arithmetic, broadcasting, and common functions—helping developers and researchers understand how the library works and apply it efficiently in Python data‑science workflows.
This article presents the 2020 Python library ranking, explaining the selection criteria and highlighting ten standout libraries—ranging from CLI tools like Typer and Rich to AI‑focused frameworks such as Hydra, PyTorch Lightning, Hummingbird, and HiPlot—plus several honorable mentions.
Alibaba’s DAMO Academy unveils its 2021 top ten technology trends, highlighting breakthroughs in third‑generation semiconductors, post‑quantum computing, flexible carbon‑based electronics, AI‑driven drug discovery, brain‑computer interfaces, autonomous data processing, cloud‑native architectures, smart agriculture, industrial IoT, and intelligent city operation centers.
This article explains the fundamentals of linear regression, including regression formulas, loss functions, and error metrics, and provides complete Python code using scikit‑learn to perform both simple and multiple linear regression on the Iris and Boston housing datasets, along with model evaluation and visualization.
Learn how to translate common mathematical symbols such as summation, product, factorial, conditional expressions, and matrix multiplication into clear Python code, revealing the underlying computations and helping data scientists and ML practitioners deepen their mathematical intuition through practical examples.
This article discusses a deep spatial-temporal tensor factorization framework for large-scale user visits understanding and forecasting, addressing challenges in advertising inventory prediction and demonstrating significant improvements over traditional methods.
The article explains how test engineers can extend their impact beyond functional testing by engaging stakeholders, adopting QAOps practices, listening to user feedback, exploring automation tools, participating in code reviews, focusing on user experience, meeting deadlines, and leveraging data science to improve software quality.
This article explores the definition of a model, distinguishes business, data, and function models, discusses criteria for a good model—including performance, fidelity to real‑world relationships, and interpretability—and examines why a universal model does not exist, all within the context of data science and AI.
This paper explores user preference mining techniques in real estate, utilizing statistical methods and machine learning models like XGBoost, LSTM, and Seq4Rec to address challenges in high-dimensional, sparse data, enhancing personalized recommendations and marketing strategies.
The article explains why SQL is essential for data scientists, introduces PostgreSQL as a powerful open‑source relational database suited for large‑scale data science, outlines its key features, advantages and disadvantages, and provides practical learning resources for beginners.
This article walks through constructing a multiple linear regression model for house price prediction using Python, covering data exploration, dummy variable creation, model fitting with statsmodels, diagnosing multicollinearity via VIF, and applying optimizations to improve predictive accuracy.
This article introduces Alibaba Cloud’s free PAI‑DSW cloud IDE for AI development, explains the evolution of machine learning, guides users through creating notebooks, running Python code, and demonstrates a complete speed‑dating dataset analysis and predictive modeling pipeline using logistic regression and data‑balancing techniques.
This article introduces a curated selection of lesser‑known Python packages—such as wget, pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, ipyvolume, Dash, and Gym—detailing their installation commands, core functionalities, and code examples to help data scientists expand their toolkit beyond the usual pandas, scikit‑learn, and matplotlib.
This article reviews eight essential Python tools—including IDLE, Scikit‑learn, Theano, Selenium, TestComplete, BeautifulSoup, Pandas, and PuLP—explaining their main features, typical use cases, and why they are valuable for developers and students across web, data science, automation, and optimization tasks.
The article argues that Go+, by preserving Go's concise syntax while adding Python‑like type inference and tensor support, can address Python's flexibility‑induced code‑quality and performance issues, making it a viable front‑end language for data‑science, deep‑learning libraries, and compiler ecosystems.
After years of using Python for AI and data science, the author examines its flexibility drawbacks and argues that Go+, with its static typing and concise syntax, can address Python’s limitations, offering comparable ease for tensor operations, potential for a new deep‑learning front‑end language.
This article examines six core data‑science concepts—Big Data, Machine Learning, Data Mining, Deep Learning, Artificial Intelligence, and Data Science itself—explaining their definitions, interrelationships, and how they fit together as pieces of a larger analytical puzzle.
This article compiles 53 common Python interview questions covering fundamentals, data structures, functions, OOP, and standard library features, providing concise explanations and code examples to help data scientists and developers prepare for technical interviews.
The 2020 developer internet survey reveals that Go tops the list of languages developers want to learn, data science dominates interest areas, security and IoT follow, and remote/online work and learning are increasingly preferred, while experienced developers show limited expertise in machine learning.
This essay traces the historical shift from political power to engineering dominance, exploring how algorithms—originating from thinkers like Leibniz and popularized by tech giants such as Facebook—have reshaped governance, culture, and the very fabric of human decision‑making.
An analysis of Indeed's 2014‑2019 US job data reveals that SQL, Java, and Python dominate employer demand, while AWS shows explosive growth, highlighting shifting trends in programming languages, cloud services, and data‑science‑related roles across the tech job market.
The article shares personal strategies for mastering Python within six months, emphasizing the importance of clear goals, diverse learning resources, hands‑on projects, and active use of platforms like Udemy, Datacamp, edX, and GitHub to reinforce data‑science skills.
This guide walks through the five essential steps of a data‑science project—acquiring, cleaning, exploring, modeling, and presenting data—while highlighting key Python libraries such as Beautiful Soup, Requests, Pandas, NumPy, Seaborn, Matplotlib, and Scikit‑learn, and providing installation and import commands.
This article shares ten practical Python plotting tricks—covering subplots, axis labels, titles, annotations, color hues, point sizes, threshold lines, multiple Y‑axes, overlapping plots, and bar‑plot ordering—to help data scientists create clear, explanatory visualizations.
Partnering with Ant Financial, Didi enhanced the open-source SQLFlow platform—translating SQL into end-to-end AI workflows with added deep-learning, XGBoost, clustering and SHAP explanation capabilities and Hive support—to create a “SQL garden” marketplace where analysts can deploy ready-made AI models via simple SQL, speeding enterprise AI adoption.
The article describes how Didi's data science team partnered with Ant Financial to co‑build the open‑source SQLFlow platform, enabling analysts to launch AI models via simple SQL, detailing the models contributed, technical extensions, and the broader vision for a universal AI ecosystem.
The article describes how Didi's data science team partnered with Ant Financial to open‑source SQLFlow, a tool that translates SQL into Python for AI model training and inference, enabling analysts to use familiar SQL to run deep‑learning, XGBoost, and clustering models across diverse business scenarios.
Physicist‑turned‑Kaggle Grandmaster Bojan Tunguz shares his journey from academia to industry, the challenges of becoming a data‑science competitor, his workflow, favorite tools, and practical advice for newcomers seeking to excel in machine‑learning competitions.
Tencent Cloud’s Intelligent Ti Machine Learning Platform (TI‑ONE) offers a one‑stop, drag‑and‑drop solution for data preprocessing, model training, and deployment across industrial panel defect detection and financial risk prediction, delivering real‑time monitoring, automated pipelines, and high‑accuracy results that dramatically improve operational efficiency.
This article examines how Python has surged to become the leading language for data science, while R's popularity has declined, citing TIOBE rankings, industry surveys, academic adoption, and expert opinions to assess whether R is truly on the brink of obsolescence.
This guide introduces NumPy's core features—including array creation, arithmetic, indexing, aggregation, multi‑dimensional handling, matrix operations, and practical examples such as computing mean‑square error—providing a comprehensive foundation for Python‑based data analysis and machine‑learning workflows.
A team of three contestants shares their experience and insights from participating in the Tencent Advertising Algorithm Contest, discussing their strategies, challenges, and advice for fellow competitors.
In this presentation, a veteran participant of the Tencent Advertising Algorithm Competition shares practical advice on building simple, efficient models, optimizing code performance, managing version control with Git, and maintaining clean coding practices to improve competition outcomes and foster better engineering habits.
This article explains the various roles of data scientists, compares data science with machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics, and clarifies the distinctions and overlaps among these fields.
The article explains the Weber‑Fechner law and its role in Prospect Theory, then shows how Didi applies these concepts—using a log‑linear order‑distance model and reference‑point‑based strategy evaluation—to reduce cancellations, improve driver perception, and guide data‑driven product decisions.
This article provides a beginner-friendly overview of artificial intelligence, explaining its core concepts, the relationship between AI, machine learning and deep learning, common real-world applications such as search and recommendation, and practical steps and resources for newcomers to start learning AI with Python and basic statistics.
The article examines the emerging data‑science function, clarifies who data scientists are, outlines their core tasks—describing the current state, uncovering patterns, and driving improvement—while proposing future growth through capability and culture building, and summarising ten guiding principles for effective analysis.
The AI Scientific Frontier Conference 2019, co‑hosted by the Chinese Academy of Sciences AI Alliance and Beijing Institute of Technology, gathers leading researchers to present cutting‑edge talks on AI theory, deep learning, computer vision, robotics, NLP, big data, and related applications, with detailed schedules, speaker bios, venue information, and registration details provided.
This article introduces the open‑source H2O platform, walks through installing the Python package, demonstrates data import, model building with GBM and AutoML, evaluates results, explains model deployment via POJO/MOJO, and discusses the visual Flow UI and broader implications of automated modeling.
This article introduces the open‑source H2O platform, explains how to install and use its Python API for data loading, preprocessing, model training with GBM and AutoML, evaluates results with AUC, and describes model deployment via POJO/MOJO as well as the visual Flow UI, concluding with reflections on the role of automated modeling in data science.
This article presents a detailed infographic comparison of R and Python from a data‑science perspective, outlining their histories, ecosystems, usability, community support, and advantages in data analysis to help readers decide which language better fits their projects.
This article reviews eight Python-based simple linear regression algorithms, examining their computational complexity and speed on datasets up to ten million points, highlighting trade‑offs between ease of use, flexibility, and performance to help data scientists choose the most efficient method.
This step‑by‑step guide shows beginners how to download and install the R programming language and its RStudio IDE on both Windows and macOS, covering package selection, installation options, shortcut creation, verification of a successful setup, and common troubleshooting tips.
This article explains why Python has overtaken Java in popularity, presents PYPL index data, and outlines ten practical reasons—from data science and machine learning to web development and automation—that make learning Python in 2018 a smart career move.
This comprehensive guide walks beginners through fourteen practical steps to learn Python machine learning, covering essential Python skills, core scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, dimensionality reduction, and deep learning with TensorFlow, all using free online resources.
This article outlines the ten most sought‑after technical skills for IT professionals in 2019, covering DevOps, Hadoop, Python/Django, data‑science tools, machine learning, AI, RPA, AWS, Tableau, and digital‑marketing analytics, with market trends and certification guidance.
This article outlines the ten most sought‑after technical competencies for 2019—including DevOps, Hadoop, Python/Django, data‑science tools, machine learning, AI, RPA, AWS, Tableau, and digital‑marketing analytics—explaining why each skill matters, key tools to learn, and the market demand behind them.
The presentation by senior algorithm expert Chen Bingqiang details how 51 Credit Card leverages AI through a comprehensive model platform and a personalized recommendation system that combines traditional machine‑learning and deep‑learning techniques to improve user experience and boost conversion rates in the financial sector.
The article clarifies that artificial intelligence encompasses machine learning, which in turn includes deep learning, and uses real‑world examples—from fraud detection and customer clustering to image recognition and language translation—to illustrate how these data‑driven models learn patterns, make predictions, and transform many industries.
