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Data Analysis

541 articles · Page 4 of 6
IT Services Circle
IT Services Circle
Feb 1, 2022 · Fundamentals

Comprehensive Guide to Recommended Programming Videos and Resources for Python, Go, Frontend, and Data Analysis

This article curates a wide range of high‑quality video tutorials, books, and online resources covering Python fundamentals, web crawling, data analysis, machine learning, Go language development, and front‑end technologies, offering practical recommendations and rating scores to help learners choose suitable study material.

Data AnalysisFrontendGo
0 likes · 12 min read
Comprehensive Guide to Recommended Programming Videos and Resources for Python, Go, Frontend, and Data Analysis
DataFunTalk
DataFunTalk
Jan 17, 2022 · Artificial Intelligence

Causal Analysis in Real Estate: Challenges, Methodology, and Practice at Beike

This article explains the wide applicability of causal inference, outlines three major challenges—correlation vs. causation, confounding factors, and selection bias—and demonstrates a scientific three‑level approach using examples such as smoking and a real‑world deployment of an intelligent client‑management tool at Beike, including experimental designs, results, and lessons learned.

A/B testingArtificial IntelligenceData Analysis
0 likes · 14 min read
Causal Analysis in Real Estate: Challenges, Methodology, and Practice at Beike
58UXD
58UXD
Jan 13, 2022 · Product Management

How 58.com Turned Data and Design Into a Smarter 11.0 Product

This case study details 58.com’s 11.0 product overhaul, covering six months of research, data analysis, live‑stream and short‑video integration, AI/AR/VR applications, redesign of the homepage layout, and the evolution of the LOCO Design System to improve user efficiency and service reach.

AIData Analysisdesign system
0 likes · 13 min read
How 58.com Turned Data and Design Into a Smarter 11.0 Product
DataFunTalk
DataFunTalk
Jan 2, 2022 · Fundamentals

Survival Analysis for User Churn: Concepts, Data Preparation, and Quantitative Modeling

This article introduces survival analysis, explains how to model user churn by defining purchase and cancellation times as birth and death events, describes data formatting, presents descriptive Kaplan‑Meier results, and shows how Cox regression quantifies the impact of factors such as membership and activity on user survival.

Data Analysiscox regressionstatistical modeling
0 likes · 7 min read
Survival Analysis for User Churn: Concepts, Data Preparation, and Quantitative Modeling
Programmer DD
Programmer DD
Dec 25, 2021 · Databases

Why SQL Struggles with Complex Queries and How SPL Offers a Solution

The article analyzes four fundamental limitations of SQL—lack of stepwise execution, incomplete set semantics, missing ordered operations, and no object reference mechanism—illustrates them with real queries, and introduces SPL as a more intuitive language that overcomes these issues.

Data AnalysisQuery OptimizationSPL
0 likes · 19 min read
Why SQL Struggles with Complex Queries and How SPL Offers a Solution
DeWu Technology
DeWu Technology
Dec 23, 2021 · Operations

Improving Technical Support's Autonomous Handling Ratio: Strategies and Data‑Driven Insights

The article presents a data‑driven framework for technical support to boost its autonomous handling ratio, covering knowledge‑base creation, diagnostic tool mastery, standardized issue templates, controlled feedback channels, self‑service infrastructure, detailed analytics, and disciplined retrospectives to reduce developer support time and improve efficiency.

Data AnalysisEfficiencyKnowledge Management
0 likes · 16 min read
Improving Technical Support's Autonomous Handling Ratio: Strategies and Data‑Driven Insights
DataFunSummit
DataFunSummit
Dec 15, 2021 · Fundamentals

Concrete Strength Data Analysis Using Pandas: A Step‑by‑Step Tutorial

This tutorial walks through a complete pandas‑based workflow for analyzing a concrete‑strength dataset, covering data loading, cleaning, exploratory visualizations, correlation analysis, and targeted sub‑group investigations to uncover factors influencing product strength and suggest improvement measures.

Data AnalysisPandasconcrete strength
0 likes · 13 min read
Concrete Strength Data Analysis Using Pandas: A Step‑by‑Step Tutorial
Alimama Tech
Alimama Tech
Dec 15, 2021 · Big Data

Channel Attribution Analysis: Touchpoint Path and Combination Models

The article explains how marketers can move beyond last‑click attribution by using touchpoint path and combination models to quantify each channel’s role, visualize journeys, assess overlap and efficiency, and analyze frequency effects, enabling data‑driven channel‑mix optimization and budget allocation.

Data Analysischannel attributioncombination model
0 likes · 14 min read
Channel Attribution Analysis: Touchpoint Path and Combination Models
58UXD
58UXD
Dec 6, 2021 · Fundamentals

How to Decode User Behavior Data to Uncover Hidden Design Insights

This article explains how designers can shift from macro metrics like DAU to analyzing raw user interaction data, infer user psychology, reconstruct real usage scenarios, and identify optimization opportunities through concrete examples and step‑by‑step methods.

Data AnalysisUX designUser Behavior
0 likes · 8 min read
How to Decode User Behavior Data to Uncover Hidden Design Insights
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
Dec 2, 2021 · Fundamentals

Web Scraping and Data Analysis of Pet Cat Breeds Using Python

This article demonstrates how to scrape cat breed information from a dedicated website, store the data in Excel, and perform comprehensive analysis and visualizations—including relationship graphs, geographic distribution, size ratios, price extremes, and word clouds—using Python libraries such as requests, lxml, pandas, pyecharts, and stylecloud.

Data AnalysisPandasPyecharts
0 likes · 12 min read
Web Scraping and Data Analysis of Pet Cat Breeds Using Python
Alimama Tech
Alimama Tech
Nov 24, 2021 · Big Data

Consumer Behavior Analysis Models: Path, Funnel, Retention, and Lifetime Value

The article explains how brands can leverage comprehensive consumer‑behavior analysis—using path, funnel, retention, and lifetime‑value models—to integrate multi‑channel data, visualize user journeys, identify conversion bottlenecks, track ongoing engagement, and quantify revenue impact, enabling data‑driven product and marketing optimization.

Data AnalysisRetention Analysisconsumer behavior
0 likes · 14 min read
Consumer Behavior Analysis Models: Path, Funnel, Retention, and Lifetime Value
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Nov 6, 2021 · Big Data

Why User Profiling Projects Fail: Common Pitfalls and Deep Causes

The article analyzes why user profiling initiatives frequently collapse, highlighting surface mistakes such as confusing past behavior with future predictions, mixing behavior with motivation, and mistaking correlation for causation, while also exposing deeper issues like unrealistic business expectations, over‑reliance on static tags, and insufficient predictive modeling and causal analysis.

Big DataBusiness IntelligenceData Analysis
0 likes · 9 min read
Why User Profiling Projects Fail: Common Pitfalls and Deep Causes
Full-Stack Internet Architecture
Full-Stack Internet Architecture
Nov 1, 2021 · Fundamentals

Nine Basic Data Analysis Methods for Business Insights

This article introduces nine fundamental data analysis techniques—including periodic, structural, layered, matrix, decomposition, funnel, correlation, tag, and MECE methods—explaining how to apply each to interpret business metrics, uncover insights, and guide decision‑making without requiring advanced mathematics or complex algorithms.

Data AnalysisFunnel AnalysisMEME
0 likes · 12 min read
Nine Basic Data Analysis Methods for Business Insights
DataFunSummit
DataFunSummit
Oct 25, 2021 · Big Data

Building a Multi-Dimensional Analysis System: Practice at Baixin Bank

This talk by Baixin Bank's BI leader outlines the bank's business model, multi-dimensional data analysis requirements, and the design of a laddered analysis solution, including indicator and analysis system construction, user‑product‑enterprise scenario modeling, and productization of data insights for operational decision‑making.

BankingBig DataBusiness Intelligence
0 likes · 20 min read
Building a Multi-Dimensional Analysis System: Practice at Baixin Bank
MaGe Linux Operations
MaGe Linux Operations
Oct 23, 2021 · Backend Development

How to Scrape and Analyze Taobao Snack Sales Data with Python

This article walks through a real‑world Python project that uses Selenium to crawl the first ten pages of Taobao snack listings, extracts sales, price and location data, visualizes price distribution and geographic concentration, generates a word‑cloud of top user comments, and lists the top‑selling stores, providing full source code for replication.

Data AnalysisPythonTaobao
0 likes · 12 min read
How to Scrape and Analyze Taobao Snack Sales Data with Python
Python Crawling & Data Mining
Python Crawling & Data Mining
Oct 12, 2021 · Fundamentals

Plotly Express Quick Start: Create Stunning Interactive Visualizations with Minimal Code

This article introduces Plotly Express, a high‑level Python visualization library, covering installation, built‑in datasets, color palettes, themes, and step‑by‑step examples for bar, scatter, bubble, matrix, area, line, pie, sunburst, funnel, 3D, map, and polar charts, demonstrating how to generate dynamic, publication‑ready plots with just a few lines of code.

Data AnalysisInteractive ChartsPlotly Express
0 likes · 13 min read
Plotly Express Quick Start: Create Stunning Interactive Visualizations with Minimal Code
Python Programming Learning Circle
Python Programming Learning Circle
Oct 11, 2021 · Fundamentals

Essential Pandas Techniques for Data Analysis in Python

This article presents a comprehensive guide to essential Pandas operations, including creating Series and DataFrames, common methods for data selection, indexing, grouping, reading and writing files, handling missing values, sorting, statistical analysis, and data transformation, with practical code examples for each feature.

Data AnalysisDataFramePandas
0 likes · 16 min read
Essential Pandas Techniques for Data Analysis in Python
ByteDance SE Lab
ByteDance SE Lab
Sep 17, 2021 · Product Management

Why A/B Testing Matters: Cases, Architecture & Best Practices

This article explains why A/B testing is essential, illustrates real-world examples from ByteDance, details the multi-layer architecture of the Volcano Engine A/B testing system, outlines experiment design, implementation, statistical analysis, best practices, and future trends, providing a comprehensive guide for product teams.

A/B testingData Analysisexperiment design
0 likes · 18 min read
Why A/B Testing Matters: Cases, Architecture & Best Practices
Python Programming Learning Circle
Python Programming Learning Circle
Sep 16, 2021 · Artificial Intelligence

10 Creative Python Project Ideas to Boost Your Skills

This article presents ten diverse Python project concepts—including voice‑controlled GUIs, AI betting and trading bots, a virtual assistant, concert monitoring, automatic SSL renewal, facial recognition, contact tracing, file organization, and YouTube career‑path aggregation—to help developers practice and expand their programming and artificial‑intelligence expertise.

Artificial IntelligenceData Analysisproject ideas
0 likes · 11 min read
10 Creative Python Project Ideas to Boost Your Skills
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 14, 2021 · Fundamentals

What TV Fans Say: Analyzing 97,331 Danmu Comments with Python

Using Python and pandas, this article collects and analyzes 97,331 danmu comments from the first episode of Mango TV’s “Brother” show, presenting data previews, word clouds, top‑liked remarks, super‑active users, and favorite performers, while also sharing the data‑scraping script.

DanmuData AnalysisPandas
0 likes · 8 min read
What TV Fans Say: Analyzing 97,331 Danmu Comments with Python
DeWu Technology
DeWu Technology
Sep 12, 2021 · Operations

How Technical Support (TS) Improves Self-Processing Rate

Technical Support teams boost self‑processing rates by building personal knowledge bases, using diagnostic tools, standardizing issue templates, managing feedback channels, creating automation tools, analyzing data trends, and conducting regular post‑mortems, thereby reducing developer support load and increasing overall efficiency.

Data AnalysisKnowledge Managementissue management
0 likes · 15 min read
How Technical Support (TS) Improves Self-Processing Rate
Xianyu Technology
Xianyu Technology
Sep 7, 2021 · Big Data

Analyzing Business Data Fluctuations and Attribution Methods

The article outlines a systematic framework for detecting abnormal KPI fluctuations in daily dashboards—verifying data accuracy, applying period‑over‑period and 3‑sigma rules, then attributing causes across product, competitor and market scopes using MECE‑based horizontal, vertical funnel, and cross analyses, and quantifying impacts with control‑variable, slot, marginal‑effect, prior‑judgment and difference‑in‑differences methods for rapid analyst response and potential automation.

AttributionBusiness IntelligenceData Analysis
0 likes · 7 min read
Analyzing Business Data Fluctuations and Attribution Methods
Taobao Frontend Technology
Taobao Frontend Technology
Aug 20, 2021 · Frontend Development

How to Measure and Improve Front‑End Code Review Quality: Metrics, Insights, and Best Practices

This article examines the evolution of code review, defines key quality metrics such as LOC, inspection time, defect count, and derived rates, analyzes data from CodeCollaborator and Alibaba’s DEF platform, and offers actionable insights to enhance front‑end code review effectiveness.

Code ReviewData AnalysisSoftware Engineering
0 likes · 15 min read
How to Measure and Improve Front‑End Code Review Quality: Metrics, Insights, and Best Practices
DataFunTalk
DataFunTalk
Aug 16, 2021 · Fundamentals

Common Pitfalls in Problem Identification and Analysis Thinking for Data Analysts

The article explains how analysts should responsibly identify problems and choose analysis frameworks, illustrating typical traps such as unclear metric definitions, Simpson's paradox, and false causation through three practical scenarios and offering structured thinking methods to avoid chaotic analysis.

Data AnalysisSimpson's paradoxanalysis framework
0 likes · 7 min read
Common Pitfalls in Problem Identification and Analysis Thinking for Data Analysts
DataFunTalk
DataFunTalk
Aug 12, 2021 · Artificial Intelligence

Causal Inference and Experiment Design in Kuaishou Live Streaming

This article presents Dr. Jin Yaran’s comprehensive overview of causal inference challenges, frameworks, and practical case studies—including DID, double machine learning, causal forests, and meta‑learners—applied to Kuaishou’s live‑streaming product, and discusses complex experimental designs such as bilateral and time‑slice experiments.

A/B testingData AnalysisKuaishou
0 likes · 15 min read
Causal Inference and Experiment Design in Kuaishou Live Streaming
Xianyu Technology
Xianyu Technology
Aug 10, 2021 · Product Management

Design of Full-Traffic AB Experiments for Seller Growth on Xianyu

The article describes a full‑traffic A/B testing framework for Xianyu that hashes seller IDs to create exclusive experiment and control groups, ensuring each seller sees only one strategy, and demonstrates that a chat‑incentive for new or churned sellers boosted chat exposure by 22 % and modestly improved overall buyer‑seller metrics without harming transaction efficiency.

AB testingData Analysise-commerce
0 likes · 9 min read
Design of Full-Traffic AB Experiments for Seller Growth on Xianyu
Python Programming Learning Circle
Python Programming Learning Circle
Aug 6, 2021 · Fundamentals

A Comprehensive List of Commonly Used Pandas Functions Categorized by Purpose

This article presents a curated collection of 100 frequently used pandas functions, organized into six categories—statistical aggregation, data cleaning, data selection, plotting and element‑wise operations, time‑series utilities, and miscellaneous helpers—providing concise Chinese explanations for each function’s purpose.

Data AnalysisData manipulationPandas
0 likes · 10 min read
A Comprehensive List of Commonly Used Pandas Functions Categorized by Purpose
Python Programming Learning Circle
Python Programming Learning Circle
Aug 6, 2021 · Fundamentals

Essential Pandas Functions for Data Analysis in Python

This article introduces Python's pandas library as a powerful open‑source alternative to MATLAB for data modeling competitions, covering basic, intermediate, and advanced functions—including data I/O, inspection, logical filtering, visualization, aggregation, and integration with tqdm for progress tracking—complete with code examples.

Data AnalysisPandasPython
0 likes · 7 min read
Essential Pandas Functions for Data Analysis in Python
DataFunTalk
DataFunTalk
Jul 26, 2021 · Artificial Intelligence

Essential Skills for Algorithm Engineers in a Highly Competitive Landscape

The article outlines the core abilities algorithm engineers need—strong data analysis, solid coding and engineering practices, problem definition, product mindset, and continuous learning—illustrated with real project cases and practical advice for thriving in today’s increasingly competitive tech environment.

AIAlgorithm EngineeringData Analysis
0 likes · 14 min read
Essential Skills for Algorithm Engineers in a Highly Competitive Landscape
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 24, 2021 · Fundamentals

Master Pandas: A Step‑by‑Step Guide to Data Analysis with Python

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.

Data AnalysisJupyter NotebookPandas
0 likes · 18 min read
Master Pandas: A Step‑by‑Step Guide to Data Analysis with Python
Alimama Tech
Alimama Tech
Jul 14, 2021 · Big Data

A/B Testing Framework for Online Experiments: Design, Implementation, Analysis, and Decision Making

The paper presents a comprehensive A/B testing framework for online experiments that guides practitioners through four stages—designing objectives and metrics, implementing random traffic allocation with robustness checks, evaluating effects using descriptive statistics and hypothesis testing, and making rollout decisions based on multidimensional significance and attribution analyses.

A/B testingData AnalysisExperimental Design
0 likes · 22 min read
A/B Testing Framework for Online Experiments: Design, Implementation, Analysis, and Decision Making
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 13, 2021 · Artificial Intelligence

What Python Reveals About Public Reaction to “Chinese Doctor” – A Data‑Driven Review Analysis

Using Python, the author collected and visualized Douban and Weibo comments on the film “Chinese Doctor,” showing rating distributions, keyword word clouds, and fan sentiment, and compares the 2021 movie with the 2019 documentary to illustrate audience perception of the pandemic drama.

Data AnalysisNLPPython
0 likes · 5 min read
What Python Reveals About Public Reaction to “Chinese Doctor” – A Data‑Driven Review Analysis
MaGe Linux Operations
MaGe Linux Operations
Jul 7, 2021 · Fundamentals

Why Spyder Is the Ideal Python IDE for Scientists and Data Analysts

Spyder, a powerful native-Python scientific IDE, offers integrated editing, interactive consoles, variable browsing, documentation viewing, and development tools, plus extensibility via plugins and APIs, and can be installed easily through Anaconda or other package managers, making it a versatile choice for engineers, scientists, and data analysts.

AnacondaData AnalysisPython IDE
0 likes · 4 min read
Why Spyder Is the Ideal Python IDE for Scientists and Data Analysts
Python Crawling & Data Mining
Python Crawling & Data Mining
Jul 7, 2021 · Fundamentals

Unlock Fast Pandas Mastery with a Free Cheat Sheet and 10 eBooks

This article introduces a free Pandas cheat sheet covering data creation, reshaping, selection, exploration, modification, grouping, and merging, provides direct download links, showcases key functions with images, and explains how to obtain high‑resolution versions for quick reference.

Cheat SheetData AnalysisData manipulation
0 likes · 5 min read
Unlock Fast Pandas Mastery with a Free Cheat Sheet and 10 eBooks
JD Retail Technology
JD Retail Technology
Jun 29, 2021 · Big Data

The Value of Data and Data Products: From Concept to Practice

This article explains how data has become a critical production resource, outlines the limitations of traditional data‑analysis workflows, defines data products and their components, describes their advantages and key characteristics, and shares practical case studies of data‑product implementations in a large e‑commerce environment.

Big DataData AnalysisData Product
0 likes · 16 min read
The Value of Data and Data Products: From Concept to Practice
21CTO
21CTO
Jun 21, 2021 · Fundamentals

Discover Spyder: The Powerful Python IDE for Scientific Computing

Spyder is a native‑Python scientific IDE offering advanced editing, debugging, variable exploration, and interactive consoles, with extensible plugins and API support, and can be installed via Anaconda or other package managers, making it ideal for scientists, engineers, and data analysts.

AnacondaData AnalysisIDE Features
0 likes · 4 min read
Discover Spyder: The Powerful Python IDE for Scientific Computing
Python Crawling & Data Mining
Python Crawling & Data Mining
Jun 19, 2021 · Fundamentals

Essential Python Data Analysis Libraries You Must Know

This article provides a concise overview of key Python data‑analysis libraries—including NumPy, pandas, matplotlib, IPython/Jupyter, SciPy, scikit‑learn, and statsmodels—explaining their core features, typical use cases, and how they interoperate to form a powerful scientific computing ecosystem.

Data AnalysisMatplotlibNumPy
0 likes · 12 min read
Essential Python Data Analysis Libraries You Must Know
MaGe Linux Operations
MaGe Linux Operations
Jun 7, 2021 · Artificial Intelligence

How to Analyze Your WeChat Friends with Python: Gender, Avatar, Signature & Location Insights

This tutorial shows how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu to fetch WeChat friend data and produce visual analyses of gender distribution, avatar face detection and tags, signature word clouds and sentiment, and geographic location, all illustrated with charts and code examples.

Data AnalysisSentiment AnalysisWeChat
0 likes · 15 min read
How to Analyze Your WeChat Friends with Python: Gender, Avatar, Signature & Location Insights
Python Programming Learning Circle
Python Programming Learning Circle
May 28, 2021 · Fundamentals

Top 25 pandas tricks for efficient data analysis in Python

This tutorial presents 25 practical pandas techniques, covering version checking, DataFrame creation, column renaming, row and column reversal, dtype selection, type conversion, memory optimization, reading and concatenating multiple files, handling missing values, string splitting, series expansion, aggregation, pivot tables, categorizing continuous data, DataFrame styling, and profiling, all illustrated with clear code examples.

Data AnalysisData manipulationPandas
0 likes · 19 min read
Top 25 pandas tricks for efficient data analysis in Python
Didi Tech
Didi Tech
May 21, 2021 · Fundamentals

Introduction to Causal Inference and Its Application in Ride‑Hailing Business

The article introduces causal inference for ride‑hailing businesses, explaining the difference between causality and correlation, common misconceptions, and how randomized experiments and observational techniques like propensity‑score matching can quantify effects of actions such as coupons, driver assignments, and platform growth decisions.

Data AnalysisRide Hailingbusiness decision
0 likes · 7 min read
Introduction to Causal Inference and Its Application in Ride‑Hailing Business
Python Crawling & Data Mining
Python Crawling & Data Mining
May 10, 2021 · Fundamentals

Master NumPy: Turn Math Formulas into Python Code

This article explains how to use Python's NumPy library to translate common mathematical formulas—such as powers, roots, absolute values, vector and matrix operations—into concise, executable code, covering setup, basic operations, and practical examples for data analysis and machine learning.

Data AnalysisNumPyPython
0 likes · 11 min read
Master NumPy: Turn Math Formulas into Python Code
NiuNiu MaTe
NiuNiu MaTe
May 2, 2021 · Fundamentals

How to Master Python Quickly: A Complete Learning Roadmap for 2024

This guide explains why Python is essential, presents a step‑by‑step learning roadmap covering beginner basics, backend web development, web crawling, data analysis, and machine learning, and provides curated resources and project links to help learners progress efficiently.

Data AnalysisWeb Scrapingbackend development
0 likes · 8 min read
How to Master Python Quickly: A Complete Learning Roadmap for 2024
Python Crawling & Data Mining
Python Crawling & Data Mining
Apr 24, 2021 · Fundamentals

Discover 140+ Must‑Know Python Libraries for Data Science & AI

The article presents a comprehensive guide to Python's built‑in functions, standard libraries, and third‑party packages across file I/O, web scraping, databases, data cleaning, statistical analysis, machine learning, visualization, and more, rating each with stars and offering a free e‑book collection for readers.

Data AnalysisDatabaseLibraries
0 likes · 32 min read
Discover 140+ Must‑Know Python Libraries for Data Science & AI
MaGe Linux Operations
MaGe Linux Operations
Apr 17, 2021 · Backend Development

Boost Your Python Projects: 7 Essential Efficiency Tools You Must Try

Discover seven powerful Python tools—including Pandas, Selenium, Flask, Scrapy, Requests, Faker, and Pillow—that streamline data analysis, web testing, API calls, web development, web scraping, mock data generation, and image processing, complete with installation steps and code examples to boost your development efficiency.

Data AnalysisPythonproductivity tools
0 likes · 6 min read
Boost Your Python Projects: 7 Essential Efficiency Tools You Must Try
MaGe Linux Operations
MaGe Linux Operations
Apr 16, 2021 · Fundamentals

Master Data Analysis with Python: From Excel/SQL to Pandas in 10 Steps

This tutorial walks data analysts through transitioning from Excel and SQL to Python, covering environment setup, data import with pandas, web scraping, cleaning, renaming, type conversion, filtering, grouping, merging, and visualization using Jupyter Notebook and popular libraries.

Data AnalysisData VisualizationJupyter Notebook
0 likes · 13 min read
Master Data Analysis with Python: From Excel/SQL to Pandas in 10 Steps
Taobao Frontend Technology
Taobao Frontend Technology
Apr 15, 2021 · Frontend Development

Boosting Frontend UX with AB Testing: Principles, Architecture & Real Cases

This article explains why AB testing enhances user experience, outlines its end‑to‑end workflow, details configuration, split, and data models, describes a decoupled frontend AB testing architecture with Core and Coupler packages, and shares real case studies demonstrating measurable improvements.

AB testingData Analysisexperiment architecture
0 likes · 22 min read
Boosting Frontend UX with AB Testing: Principles, Architecture & Real Cases
Liangxu Linux
Liangxu Linux
Apr 13, 2021 · Fundamentals

Master awk: From Basics to Advanced Text Processing on Linux

This comprehensive guide introduces awk as a powerful Linux text‑analysis tool, explains its underlying record‑field model, demonstrates practical one‑liners and scripts for reporting, filtering, formatting, file splitting, and shows advanced features like built‑in variables, conditionals, arrays, and string functions.

Command LineData AnalysisLinux
0 likes · 11 min read
Master awk: From Basics to Advanced Text Processing on Linux
JD.com Experience Design Center
JD.com Experience Design Center
Apr 7, 2021 · Artificial Intelligence

How to Use CHAID Decision Trees in SPSS for Market Segmentation

This article explains why simple single‑feature analysis can miss important user groups, introduces decision trees—especially the CHAID algorithm—as a way to uncover multi‑attribute segments, and provides step‑by‑step instructions for building descriptive and predictive trees in SPSS, including how to interpret tree visuals and benefit tables.

CHAIDData AnalysisSPSS
0 likes · 11 min read
How to Use CHAID Decision Trees in SPSS for Market Segmentation
DataFunTalk
DataFunTalk
Apr 4, 2021 · Big Data

User Profiling: Concepts, Practices, and Data‑Driven E‑Commerce Case Study

This article introduces the fundamentals of user profiling, explains tag types and their business value, and demonstrates a data‑driven e‑commerce case study that analyzes gender, age, region, marital status, education, profession, product preferences, purchase timing, and price sensitivity to guide targeted promotion strategies.

Data AnalysisPythone‑commerce
0 likes · 16 min read
User Profiling: Concepts, Practices, and Data‑Driven E‑Commerce Case Study
Efficient Ops
Efficient Ops
Mar 30, 2021 · Fundamentals

Mastering Awk: Powerful Text Processing for Linux with Real‑World Examples

This tutorial introduces awk as a versatile Linux text‑analysis tool, explains its execution model (BEGIN, body, END), demonstrates practical commands for reporting, filtering, formatting, and advanced scripting, and provides numerous code snippets and visual examples to help readers quickly apply awk in real‑world scenarios.

Data AnalysisScriptingawk
0 likes · 12 min read
Mastering Awk: Powerful Text Processing for Linux with Real‑World Examples
Python Programming Learning Circle
Python Programming Learning Circle
Mar 29, 2021 · Databases

User Retention, Funnel, and Session Analysis in ClickHouse Using Bitmap and Retention Functions

The article explains how to perform efficient user retention, funnel, and session analysis on large ClickHouse datasets by replacing costly multi‑table joins with bitmap compression, the built‑in retention function, windowFunnel, and high‑order array functions, providing practical SQL examples and performance insights.

BitmapClickHouseData Analysis
0 likes · 18 min read
User Retention, Funnel, and Session Analysis in ClickHouse Using Bitmap and Retention Functions
Python Programming Learning Circle
Python Programming Learning Circle
Mar 17, 2021 · Big Data

Eight Python Techniques for Efficient Data Analysis

This article presents eight Python data analysis techniques—including list comprehensions, lambda expressions, map/filter, NumPy arange and linspace, pandas axis handling, and DataFrame concatenation, merging, joining, applying, and pivot tables—to improve code efficiency, readability, and analytical capabilities.

Data AnalysisNumPyPython
0 likes · 7 min read
Eight Python Techniques for Efficient Data Analysis
Python Crawling & Data Mining
Python Crawling & Data Mining
Mar 3, 2021 · Fundamentals

Uncovering Tianjin’s Bus Network: From Raw GPS Data to Complex Network Insights

This article walks through acquiring Tianjin bus line data via the Gaode Map API, cleaning and converting geographic coordinates, visualizing station distributions with matplotlib and Baidu maps, and then applying complex‑network analysis to reveal degree distributions, clustering coefficients, and small‑world characteristics of the city’s public‑transport system.

Complex NetworksData AnalysisNetwork Metrics
0 likes · 19 min read
Uncovering Tianjin’s Bus Network: From Raw GPS Data to Complex Network Insights
Xianyu Technology
Xianyu Technology
Feb 23, 2021 · Artificial Intelligence

Pricing Guidance System for Xianyu Secondhand Marketplace

The Xianyu pricing guidance system blends new‑product market values with depreciation factors derived from usage, condition and category attributes—extracted via real‑time text mining and image analysis—to recommend dynamic price ranges adjusted for supply‑demand and seller urgency, currently covering 60% of listings with over 65% overall accuracy.

Data AnalysisPricinge-commerce
0 likes · 6 min read
Pricing Guidance System for Xianyu Secondhand Marketplace
DevOps
DevOps
Feb 21, 2021 · Big Data

GitHub 2020 Digital Insight Report: Data‑Driven Analysis of the Global Open‑Source Ecosystem

The GitHub 2020 Digital Insight Report, produced by X‑lab and multiple research institutions, analyzes 860 million event logs, 54.21 million active repositories and 14.54 million developers to reveal growth trends, activity metrics, regional distributions, project influence networks (OpenGalaxy), and monthly‑star highlights, offering actionable insights for open‑source governance and community management.

2020Data AnalysisGitHub
0 likes · 18 min read
GitHub 2020 Digital Insight Report: Data‑Driven Analysis of the Global Open‑Source Ecosystem
DataFunTalk
DataFunTalk
Feb 5, 2021 · R&D Management

Three Stages of Technical Colleagues and How to Drive Business

The article outlines three developmental stages for engineers—from merely implementing PRD specifications, to understanding business and selecting appropriate technical solutions, and finally proactively contributing business ideas—while describing practical methods for demand exploration, project initiation, management, and data‑driven iteration within a mobile development context.

Data AnalysisProject ManagementR&D Management
0 likes · 11 min read
Three Stages of Technical Colleagues and How to Drive Business
Java Captain
Java Captain
Jan 26, 2021 · Big Data

Five Open-Source Stock Trading Tools for Developers

This article introduces five open-source stock trading tools—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match-trade—detailing their authors, star counts, features, and repository links, offering developers practical resources to build or enhance their own trading applications.

Data Analysisfinanceopen-source
0 likes · 5 min read
Five Open-Source Stock Trading Tools for Developers
Python Crawling & Data Mining
Python Crawling & Data Mining
Jan 24, 2021 · Fundamentals

Master Python Data Analysis: From Reading Files to Visualization

This guide walks you through the complete Python data‑analysis workflow—reading and writing data, processing with NumPy and pandas, modeling with statsmodels and scikit‑learn, and visualizing results with Matplotlib—while highlighting the key tools and learning path for beginners and busy professionals alike.

Data AnalysisNumPyPython
0 likes · 6 min read
Master Python Data Analysis: From Reading Files to Visualization
Python Crawling & Data Mining
Python Crawling & Data Mining
Dec 27, 2020 · Fundamentals

What Do China’s Divorce Rates Reveal? A Decade of Data Visualization and Analysis

Using publicly available statistics from the National Bureau of Statistics, this article visualizes and analyzes China’s divorce rates over the past decade, comparing national trends, provincial variations, and city-specific patterns such as Beijing versus Shanghai, revealing a steady increase and regional disparities.

AltairData AnalysisPyecharts
0 likes · 8 min read
What Do China’s Divorce Rates Reveal? A Decade of Data Visualization and Analysis
Ctrip Technology
Ctrip Technology
Dec 17, 2020 · Artificial Intelligence

Time Series Forecasting: Tools, Models, and Lessons from Ctrip

This article outlines Ctrip's approach to time series forecasting, covering background, common tools such as factor‑based models, traditional statistical methods like ARIMA, and machine‑learning techniques including tree and neural networks, and shares practical experiences on data splitting, feature engineering, model stability, and evaluation.

ARIMACtripData Analysis
0 likes · 13 min read
Time Series Forecasting: Tools, Models, and Lessons from Ctrip
Baobao Algorithm Notes
Baobao Algorithm Notes
Dec 11, 2020 · Industry Insights

How to Cultivate Data Sensitivity: The Core Skill Behind Algorithm Engineers

This article explores the concept of data sensitivity for algorithm engineers, defines its meaning, discusses how to measure it, offers practical steps to develop the skill through data analysis and feature engineering, and reveals the hidden pattern in a label‑prediction example that illustrates its importance.

Algorithm EngineeringData AnalysisIndustry Insights
0 likes · 6 min read
How to Cultivate Data Sensitivity: The Core Skill Behind Algorithm Engineers
MaGe Linux Operations
MaGe Linux Operations
Nov 29, 2020 · Big Data

How Vaex Enables Billion‑Row Data Analysis on a Laptop

This article explains how Vaex, an open‑source DataFrame library, lets data scientists efficiently explore, visualize, and analyze massive datasets—such as the over‑billion‑row NYC taxi records—using memory‑mapping and virtual columns, all on a standard notebook without costly cloud resources.

Data AnalysisNYC taxi datasetPython
0 likes · 11 min read
How Vaex Enables Billion‑Row Data Analysis on a Laptop
Yuewen Technology
Yuewen Technology
Nov 10, 2020 · Artificial Intelligence

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.

Data AnalysisLambdaMARTLearning-to-Rank
0 likes · 9 min read
Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion
Liangxu Linux
Liangxu Linux
Nov 2, 2020 · Big Data

Master Shell Tricks to Analyze Beijing Points‑Based Residency Data in Seconds

This article demonstrates how to use standard shell utilities such as grep, cut, sort, uniq, awk, and join to quickly extract insights—like top companies, common surnames, popular given names, age distribution, and hometown rankings—from a JSON dataset of Beijing points‑based residency applicants.

Big DataData Analysisawk
0 likes · 13 min read
Master Shell Tricks to Analyze Beijing Points‑Based Residency Data in Seconds
58UXD
58UXD
Oct 19, 2020 · Product Management

Turning Used‑Car Search into a Smart Recommendation Engine

This article analyzes why the used‑car search conversion is low, reconstructs user search scenarios from query data, categorizes search intents, identifies pain points across the search funnel, and proposes product‑level redesigns and recommendation strategies to educate vague users and deliver more precise results.

Data AnalysisSearch Optimizatione‑commerce
0 likes · 9 min read
Turning Used‑Car Search into a Smart Recommendation Engine
JD Retail Technology
JD Retail Technology
Oct 16, 2020 · Industry Insights

How JD’s PLUS Membership Used Data and Algorithms to Drive Growth

This article examines JD.com’s transition from traffic‑driven acquisition to a data‑centric, algorithm‑powered membership model, detailing the construction of a robust data foundation, multi‑level analysis methods, productized dashboards, and growth‑hacking experiments that boosted PLUS member retention and revenue.

AlgorithmData AnalysisGrowth Hacking
0 likes · 24 min read
How JD’s PLUS Membership Used Data and Algorithms to Drive Growth
DataFunTalk
DataFunTalk
Oct 10, 2020 · Product Management

Search Product Optimization: From System Architecture to User Demand and Content Strategies

This article outlines a comprehensive approach for search product managers to drive system improvements, covering overall architecture, query understanding, recall and ranking optimization, business and presentation rules, content enrichment, frontend design, and methods for uncovering user needs through data and behavior analysis.

Content StrategyData AnalysisQuery Optimization
0 likes · 24 min read
Search Product Optimization: From System Architecture to User Demand and Content Strategies
Python Crawling & Data Mining
Python Crawling & Data Mining
Sep 24, 2020 · Big Data

What Shanghai’s 2020 Second‑Hand Housing Data Reveals About Prices and Market Trends

This article analyzes a Scrapy‑Redis crawl of Lianjia data from July 2020, cleans and enriches the dataset, and uses pandas profiling and visualizations to uncover Shanghai’s second‑hand housing price distribution, regional hotspots, housing types, and market dynamics during the first half of 2020.

Data AnalysisHousing MarketPython
0 likes · 14 min read
What Shanghai’s 2020 Second‑Hand Housing Data Reveals About Prices and Market Trends
Programmer DD
Programmer DD
Sep 10, 2020 · Artificial Intelligence

Can You Predict Speed‑Dating Success? A Data‑Driven Exploration

This article walks through loading the Speed Dating dataset, examining its features and missing values, visualizing match rates by gender and age, performing correlation analysis, and building a logistic regression model with SMOTE oversampling to predict whether a pair will successfully match.

Data AnalysisLogistic RegressionPandas
0 likes · 11 min read
Can You Predict Speed‑Dating Success? A Data‑Driven Exploration
Alibaba Cloud Developer
Alibaba Cloud Developer
Sep 9, 2020 · Artificial Intelligence

Can You Predict Speed‑Dating Success? A Data‑Driven AI Analysis

This article explores the classic Speed Dating dataset, performing data cleaning, exploratory analysis of match rates, gender and age effects, correlation studies, and finally building a logistic regression model with SVMSMOTE oversampling to predict matchmaking success, achieving around 83% accuracy.

Data AnalysisLogistic RegressionPandas
0 likes · 11 min read
Can You Predict Speed‑Dating Success? A Data‑Driven AI Analysis
MaGe Linux Operations
MaGe Linux Operations
Aug 28, 2020 · Fundamentals

7 Powerful Jupyter Tricks to Supercharge Your Data Analysis

This guide presents seven practical techniques—from using Pandas Profiling and Cufflinks‑Plotly visualizations to mastering IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple outputs, and live slide conversion with RISE—to accelerate and enrich everyday data analysis workflows.

Data AnalysisIPythonPython
0 likes · 9 min read
7 Powerful Jupyter Tricks to Supercharge Your Data Analysis
Youzan Coder
Youzan Coder
Aug 21, 2020 · Industry Insights

From PRD Translator to Business Driver: The Three Growth Stages for Engineers

The article outlines three progressive stages for technical staff—from merely following PRD specifications, to understanding business needs and selecting appropriate solutions, and finally to proactively shaping business strategy—while sharing practical methods for demand exploration, project initiation, management, and data‑driven iteration.

Data AnalysisProject Managementbusiness thinking
0 likes · 13 min read
From PRD Translator to Business Driver: The Three Growth Stages for Engineers
Liangxu Linux
Liangxu Linux
Aug 19, 2020 · Operations

How to Quickly Analyze Beijing Residency Data with Shell Commands

This tutorial shows how to use standard Unix shell tools such as grep, cut, sort, uniq, awk, and join to extract insights—top companies, most common surnames, popular given names, age distribution, and hometown statistics—from a JSON dataset of over 6,000 Beijing residency applicants.

Big DataData Analysisjson
0 likes · 13 min read
How to Quickly Analyze Beijing Residency Data with Shell Commands
DevOps Coach
DevOps Coach
Aug 13, 2020 · Databases

How to Benchmark Elasticsearch Clusters with Rally: A Step‑by‑Step Guide

This article explains why large‑scale Elasticsearch deployments need rigorous performance testing, compares available testing tools, walks through installing and configuring the official Rally benchmark suite, details hardware recommendations, shows how to run tests against multiple cloud providers, and teaches you how to interpret the resulting metrics to make informed cluster‑selection decisions.

BenchmarkingData AnalysisElasticsearch
0 likes · 16 min read
How to Benchmark Elasticsearch Clusters with Rally: A Step‑by‑Step Guide
MaGe Linux Operations
MaGe Linux Operations
Aug 12, 2020 · Fundamentals

Why Learn Python? 6 Powerful Application Areas You Should Know

Python’s versatility makes it essential across fields—from AI and cloud computing to web development, web scraping, game creation, and data analysis—offering high demand, lucrative careers for IT professionals and productivity boosts for non‑IT workers, as illustrated by real‑world examples and industry trends.

Artificial IntelligenceCloud ComputingData Analysis
0 likes · 5 min read
Why Learn Python? 6 Powerful Application Areas You Should Know
MaGe Linux Operations
MaGe Linux Operations
Aug 5, 2020 · Big Data

8 Must‑Know Python Tools for Data Mining and Analysis

This article introduces eight essential Python libraries—Gensim, TensorFlow, SciPy, NumPy, Matplotlib, Pandas, Scikit‑Learn, and Keras—that empower developers to clean, prepare, merge, and accurately analyze data for effective data mining.

Data AnalysisLibrariesPython
0 likes · 4 min read
8 Must‑Know Python Tools for Data Mining and Analysis
58UXD
58UXD
Aug 5, 2020 · Product Management

Mastering Product Management: How to Decompose Problems and Boost Your Value 10x

This talk guides product managers and designers on breaking down complex goals—like achieving a million‑yuan sales target in three months—through structured problem‑decomposition methods such as MECE and SMART, while outlining a personal development framework that cultivates analytical, communication, and leadership skills across career stages.

Data AnalysisDesign thinkingMECE
0 likes · 7 min read
Mastering Product Management: How to Decompose Problems and Boost Your Value 10x