This article surveys the most useful open‑source Python packages for data science, covering core numerical libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, while showing GitHub contribution metrics and Google‑trend popularity.
During the first day of London Tech Week, four fintech experts debated how emerging technologies such as artificial intelligence, blockchain, and data analytics can narrow the global financial inclusion gap, offering practical insights, regulatory considerations, and collaborative strategies for achieving equitable financial services worldwide.
The article explains Facebook Prophet’s additive regression model and demonstrates its use to forecast Tencent Cloud database storage demand, showing upward trends and growing uncertainty from January‑June 2018 data, while highlighting practical applications for internal customer identification and capacity planning.
In the era of big data, this curated list highlights essential print titles—from machine learning and statistical learning to Hadoop, predictive analytics, data visualization, and data engineering—offering readers a comprehensive roadmap to deepen practical knowledge and stay current with rapidly evolving technologies.
The 2018 KDnuggets survey of over 2,000 data‑science professionals shows Python dominating with 66% usage, R dropping below 50%, TensorFlow leading deep‑learning frameworks, RapidMiner gaining traction, SQL remaining stable, Hadoop declining, and regional participation shifting toward Europe.
This article presents a rapid Q&A session with JD data scientists and architects, covering the benefits of algorithm contests for students, the unique advantages of the JDATA competition, scoring formulas, ways to improve results, strong feature extraction, real‑time modeling, algorithm selection, and the value of the competition’s special offer for future employment.
This guide shares practical advice from multiple AI interview experiences, covering how to build a standout profile, a curated list of target companies, interview techniques, motivation for meaningful work, and essential computer science, math, and machine‑learning fundamentals to help graduates secure AI roles.
A comprehensive 2017 timeline shows how Python surged to the top of machine‑learning, data‑science, and general programming rankings, moved its source code to GitHub, inspired new libraries, and even entered school curricula, highlighting its rapid adoption across diverse tech domains.
This article explores the growing complexity of AI adoption, the need for customized predictive solutions, and practical steps for enterprises to integrate machine learning without over‑hauling development teams, using IoT predictive‑maintenance as a concrete example.
This article introduces seven must‑know Python tools—including IPython, GraphLab Create, Pandas, PuLP, Matplotlib, Scikit‑Learn, and Spark—explaining their key features and how they empower data scientists to work efficiently in production environments.
The 2017 Python roundup highlights its surge as the leading language for AI, data science, and software development, covering its dominance in hiring, migration to GitHub, academic recognition, educational adoption, and major ecosystem updates such as Django 2.0 and the Tangent library.
Meitu leverages its vast user base and massive image‑video library to build a personalized recommendation system that tackles information overload by quickly surfacing relevant content, while exploring challenges such as timeliness, cold‑start scenarios, and diversity to enhance overall user experience.
From foundational concepts to practical examples, this guide walks engineers through ten essential supervised and unsupervised machine‑learning algorithms—decision trees, Naïve Bayes, linear regression, logistic regression, SVM, ensemble methods, clustering, PCA, SVD, and ICA—explaining their theory, real‑world uses, and why they matter.
This article surveys the most essential Python packages for data science in 2017, covering core scientific computing, data manipulation, visualization, machine learning, deep learning, natural language processing, and web scraping, and explains why each library remains indispensable for modern analysts.
Based on Stack Overflow's Developer Story tags and Kaggle's 2017 data science survey, this article reveals the programming languages and technologies developers dislike most, examines growth versus dislike rates, and highlights key trends and recommendations for aspiring data scientists.
An analysis of Indeed.com job‑trend data reveals how programming languages like Python, Java, R, C++, Scala and Julia rank in popularity for machine‑learning and data‑science positions, highlighting growth patterns and offering guidance on language selection based on career goals.
This article explains the fundamental concepts of machine learning and deep learning, compares their data and hardware dependencies, feature processing, problem‑solving approaches, execution time, and interpretability, and outlines real‑world applications and future development trends.
The article explains how Python’s elegant syntax, powerful libraries, and especially the low‑level buffer protocol introduced by Travis Oliphant propelled its rise during the big‑data boom, turning data‑hungry companies toward Python and creating the demand for data‑scientist programmers.
In this interview, Microsoft chief data scientist Xie Liang shares how his economics background led him to machine learning, describes practical AI applications in Azure cloud services, discusses challenges and advantages for economists entering the field, and outlines his upcoming Keras‑focused talk and book.
This article examines the booming demand for big data professionals, explains the 4V characteristics, lists essential open‑source tools, outlines learning routes and required skills, and presents salary data for various big‑data roles in China.
This article curates over 20 machine‑learning cheat sheets—including algorithm overviews, Python libraries, and essential mathematics—compiled from various online sources and illustrated with downloadable diagrams, offering a comprehensive reference as of June 1 2017.
This guide outlines a step‑by‑step learning roadmap for ordinary programmers aiming to become data scientists, covering essential math, statistics, machine learning fundamentals, feature engineering, deep learning resources, open‑source tools, and practical project advice to navigate the AI field effectively.
This article compiles a wide range of interview questions sourced from Glassdoor covering general topics, machine learning, statistics, programming, big‑data frameworks, SQL, and brain‑teasers, providing candidates with English translations and insights into the types of problems asked by companies such as Apple, Google, Microsoft, Uber, and many others.
This comprehensive tutorial walks beginners from zero to proficiency in Python‑based machine learning, covering essential Python skills, core ML concepts, key scientific libraries, fundamental algorithms, advanced techniques like SVM and ensemble methods, and an introduction to deep learning with practical resources and code examples.
JD.com's JData Algorithm Competition concluded with record-breaking participation, awarding 600,000 yuan in prizes and offering special employment opportunities to top performers, while showcasing how AI and big data enhance e-commerce experiences.
This article outlines the critical competencies needed across the data field—from building and maintaining big data platforms and data warehouses to mastering visualization, analysis, mining, and deep learning—offering practical guidance for aspiring data professionals seeking long‑term career growth.
This tutorial explains why R programmers should add Python to their toolkit, compares core data types and structures between the two languages, introduces essential Python libraries for data analysis, and walks through a practical Boston housing dataset example to solidify the concepts.
Advancing a machine‑learning project requires focusing first on the core business problem, then designing comprehensive features, ensuring high‑quality data, and finally selecting an appropriate model, because business impact drives success while features and data set the performance ceiling and model choice balances accuracy with interpretability.
This article provides a concise overview of sixteen popular Python machine‑learning libraries—including scikit‑learn, NLTK, Theano, and Orange—detailing their main features, typical use cases, and where to find their project pages, making it a handy reference for data‑science practitioners.
The article announces a PhD workshop, outlines Hulu’s research center and its six AI‑focused teams—personalization, data science, video codec, content understanding, intelligent search, and ad intelligence—while highlighting key projects and inviting PhD candidates to apply.
The KDnuggets 2016 poll of 844 data scientists reveals the most popular algorithms, shifts since 2011, differences in usage across employment sectors, regional participation, and an industry‑academic affinity metric, highlighting a rise in boosting, text mining, visualization, and deep learning while noting declines in association rules and uplift modeling.
This article outlines five practical commandments that help data scientists and data engineers collaborate more effectively, covering data awareness, tool familiarity, technical limits, mutual respect, and shared responsibility to ensure smooth project delivery.
This article highlights eleven lesser‑known Python packages—from date‑time handling with Delorean to Bayesian analysis with PyMC—providing concise code examples and practical tips that can enrich the toolkit of both novice and seasoned developers.
The article outlines data science as an interdisciplinary field centered on three key steps—pre‑processing, interpretation, and modeling—while providing concise recommendations of foundational books for R, Python, exploratory analysis, machine learning, and essential tools to guide practitioners.
Aspiring data specialists should cultivate curiosity and hands‑on experience with production‑grade tools, and this guide highlights seven indispensable Python libraries—IPython, GraphLab Create, pandas, PuLP, matplotlib, scikit‑learn, and Spark—each explained with key features to boost your data‑science career.
The article describes how Airbnb built and continuously improved a machine‑learning‑driven dynamic pricing tool—Aerosolve—that extracts property features, compares similar listings, incorporates seasonal and event‑driven demand, and automatically updates nightly price suggestions to help hosts set optimal rates.
This article traces the history and modern challenges of big data, illustrating how Microsoft has leveraged data‑driven culture, large‑scale data collection, and machine‑learning services such as Azure ML to transform product development and user experience across decades.
This article surveys the ten most popular machine‑learning APIs from leading cloud providers, detailing their key features, documentation quality, ease of use, and popularity metrics, while also noting several noteworthy APIs that fell just outside the top ten.
The article recounts Riley Newman's five‑year journey at Airbnb, describing how the data science team evolved from a small, centralized group to an embedded, cross‑functional partner that treats user behavior as data, democratizes insights, and drives product and business decisions at scale.
This guide outlines a comprehensive, step‑by‑step Python learning path for aspiring data scientists, covering environment setup, core language fundamentals, regular expressions, scientific libraries such as NumPy, SciPy, Matplotlib, Pandas, data visualization, machine‑learning with scikit‑learn, and an introduction to deep learning, with curated resources and practice projects.
This article introduces ten notable open‑source projects in artificial intelligence and machine‑learning, summarizing each tool’s core capabilities, typical use cases, and where to find the source code, offering developers a quick guide to selecting the right platform for their needs.
