機械学習初心者がKaggleの「入門」を高速で終えるための、おすすめ資料などまとめ（2023年12月版）｜カレーちゃん
機械学習初心者がKaggleの「入門」を高速で終えるための、おすすめ資料などまとめ（2023年12月版） こんにちわ、カレーちゃんです。Kaggle GrandMasterです。 Kaggleはデータサイエンスに入門するのにとても適しています。ですが、英語の問題などがあり、入門するのが難しい。そこで、Kaggleの「入門」をこうすれば高速に完了できるというnoteを書きます。 同じタイトルの記事を、2020年8月にも書いたのですが、それから2年以上がたちました。それから、おすすめできる資料が増え、また、私が思う入門のコースもやや変わりましたので、更新をしたいと思います。 1.Kaggleに入門（はじめに取り組むと良い資料）Kaggleには、「タイタニックコンペ」という、練習用のコンペがあります。 これは、事故が起こったタイタニックの乗客のデータから、乗客の生死を予測するという、やりたいこと
State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
GitHub - mckinsey/vizro: Vizro is a toolkit for creating modular data visualization applications.
Rapidly self-serve the assembly of customized dashboards in minutes - without the need for advanced coding or design experience - to create flexible and scalable, Python enabled data visualization applications Use a few lines of simple configuration to create complex dashboards, which are automatically assembled utilizing libraries such as Plotly and Dash, with inbuilt coding and design best pract
まえがき データ分析はなんて広いんだろう。影響力の強まりに応じ、自然・社会・人間ほぼすべてが対象となりどんどん拡大していく。対象に応じ手法も広がり複雑化し、学ぶべきことが多すぎる。データサイエンティスト協会のスキルチェックリストVer.3.001（ごめんもう4.00が出てるね）も500超の項目があります。読むべき図書も良書と思われるものだけでも増え続けており、もう手に負えない状況です。 ただ、これはやってはだめだ、ここを知らないと道に迷う、という絶対に知っておくべき点は学べる範囲だと思います。本書では、データ分析において間違えやすい、誤解しやすい点を共有し、データ分析全体をよくする目的で、かつ データ分析の入門書・専門書に分野ごとには書かれてはいますが1つにまとまっておらず目に触れにくいもの データ分析の入門書・専門書でもスルーされていたり場合によっては誤っていると思われるもの で自分なり
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
良いグラフと悪いグラフの違いとは？
棒グラフ、円グラフ、ヒストグラム等、データを視覚的に示すためのグラフにはさまざまな種類があります。どのデータをどのグラフで示せばいいのかについて、ジョージア大学応用遺伝子技術センター博士研究員のチェンシン・リー氏が解説しました。 GitHub - cxli233/FriendsDontLetFriends: Friends don't let friends make certain types of data visualization - What are they and why are they bad. https://github.com/cxli233/FriendsDontLetFriends ◆1：棒グラフ データの平均、分散、分布を示すときに棒グラフ(左)を用いると、データの分布がわからなくなります。これを避けるために箱ひげ図や散布図を用いるのが良いとのこと。 ◆2：サ
Elasticsearch piped query language, ES|QL, now generally available — Elastic Search Labs
Elasticsearch piped query language, ES|QL, now generally available Today, we are pleased to announce the general availability of ES|QL (Elasticsearch Query Language), a dynamic language designed from the ground up to transform, enrich, and simplify data investigations. Powered by a new query engine, ES|QL delivers advanced search using simple and familiar query syntax with concurrent processing, e
Announcing Observable 2.0
Today we’re launching Observable 2.0 with a bold new vision: an open-source static site generator for building fast, beautiful data apps, dashboards, and reports. Our mission is to help teams communicate more effectively with data. Effective presentation of data is critical for deep insight, nuanced understanding, and informed decisions. Observable notebooks are great for ephemeral, ad hoc data ex
Look-up-tables, more commonly referred to as LUTs, are as old as Mathematics itself. The act of precalculating things into a row or table is nothing new. But in the realm of graphics programming, this simple act unlocks some incredibly creative techniques, which both artists and programmers found when faced with tough technical hurdles. We’ll embark on a small journey, which will take us from simp
はじめに
このドキュメントは『指標・特徴量の設計から始めるデータ可視化学入門』で提供されているPythonによる可視化コードをR言語で書き直したものです。 ただし、Pythonのコードの直訳・逐語訳ではなくRらしい書き方・表現へ意訳しています。 以下が各章ごとのドキュメントです。 2023年2月時点で第8章まで完成。 第1章 データ可視化の本質 第2章 数量を把握するデータ可視化 第3章 メカニズムをとらえるデータ可視化 第4章 多変数をとらえるデータ可視化 第5章 データの分布をとらえる指標化 第6章 関係性をとらえる指標化 第7章 パターンをとらえる指標化 第8章 データ指標化・可視化のプロセス MATLAB版も公開されています。 以下の方針を取っています。 書籍とPythonのコードで微妙に異なる箇所は、極力書籍に合わせる。 配色の再現は目指さない。 jetカラーのグラデーションはviridi
ITスキルロードマップ roadmap.sh がすごい。Data Analyst について対応する本をまとめた - Qiita
ITスキルロードマップ roadmap.sh がすごい。Data Analyst について対応する本をまとめた機械学習データ分析キャリアデータアナリティクスデータアナリスト Developer Roadmapsというサイトがすごいです。ITエンジニアの分野別にスキルアップのロードマップが示されています。 言語、基盤、アプリ、かなり網羅されています。 ということで、AI and Data Scientist Roadmap について書きましたが 今回は Data Analyst Roadmap です。 雑感 このロードマップの続きにAI and Data Scientistがあり、Data AnalystをData Scientistの前段階的に位置付けているのが疑問。Data AnalystとData Scientistは並ぶものではないでしょうか。 そして、ビジネス、ドメイン知識や分析目
State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
Rに出会って、早5年（2023年現在）。これまでに出会った、無料で、RやRを使った統計解析を学ぶことができるウェブサイトのメモです。ブックマークしているもの、Xでツイート、リツイートしてきたものを公開します。 随時更新して追加していきます。他にもあればコメント欄にお願いします。 （英語の記事多い！） Rで統計解析 UCLA Statistical Methods and Data Analytics 【英語】コーディング方法など細かい事例が豊富です。 An Introduction to Bayesian Data Analysis for Cognitive Science 【英語】ベイズに特化しています。 New statistics for design researchers A Bayesian workflow in tidy R 【英語】ベイズに関する分析法がまとめてあります
An easy look at Grafana architecture Delve into Grafana's architecture in this article, where I strive to simplify complex concepts. Explore the components that make up Grafana and gain a clear understanding of how they work together to create a seamless monitoring and visualization experience. What is Grafana? Grafana is an open-source platform for monitoring and observability, designed to visual
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
Let’s see a brief description of the columns of our dataset: age (numeric)job : type of job (categorical: “admin.” ,”unknown”,”unemployed”, ”management”, ”housemaid”, ”entrepreneur”, ”student”, “blue-collar”, ”self-employed”, ”retired”, ”technician”, ”services”)marital : marital status (categorical: “married”,”divorced”,”single”; note: “divorced” means divorced or widowed)education (categorical: “
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
Datadog provides OSS community support for ruby-lang.org
We are excited to announce that Ruby’s official website, ruby-lang.org, has adopted Datadog for monitoring by Datadog OSS community support. This allows us to effectively monitor the performance and availability of the site in real time for Ruby users. This key benefits of using Datadog include the following. CDN Visibility cache.ruby-lang.org provided by Fastly is most important infrastructure of
Best Python Chart Examples | The Python Graph Gallery
The Python Graph Gallery has always been a reservoir of inspiration, providing hundreds of foundational chart examples for newcomers and seasoned developers alike. While our vast collection offers a stepping stone into the world of data visualization, the following list stands out. Every chart here represents the pinnacle of craftsmanship, exhibiting the depths to which matplotlib can be customize
K-means clustering is a type of unsupervised learning when we have unlabeled data (i.e., data without defined categories or groups). Clustering refers to a collection of data points based on specific similarities. K-Means Algorithm K-means aims to find groups in the data, with the number of groups represented by the variable K. Based on the provided features, the algorithm works iteratively to ass
Why Did We Copy Flutter? Because Even Google’s Castoffs Produce Greatness! “Google’s geniuses designed Flutter’s API to be elegant and efficient.” “Copying Flutter’s API gives us a top-tier data visualization framework with minimal effort.” “Flitter offers 50+ widgets, just like Flutter.” “Need help? Just ask ChatGPT or search YouTube for Flutter tips. Apply them to Flitter, and voilà, it works!”
Here is a list of common data engineering interview questions, with answers, which you may encounter for an interview as a data engineer. The questions during an interview for a data engineer aim to check not only the grasp of data systems and architectures but also a keen understanding of your technical prowess and problem-solving skills. This article lists essential interview questions and answe
Tableauはいいぞ - ブログ - 株式会社JADE
こんにちは。篠原です。 Googleのコアアップデートが3月5日にロールアウトされて、このタイミングでコアアップデートのレポートを書く予定だったのですが全然終わらないですね…。 そんな中の社内のスレッドがこちらです。 活発な社内スレッド。こうして今日も新しいアイデアが生まれる。 ということで「Tableauはいいぞ」になりました。 自分で言っておいてなんですが何書くの… Tableauはいいぞ そもそもTableauを知らないという方に紹介をすると、Tableauはデータ探索を行うためのBIツールです。 LookerStudioのようなものをイメージしていただくと分かりやすいかもしれません。 モダン BI 市場で選ばれている Tableau の分析プラットフォームは、データの探索と管理を簡単に行い、ビジネスや世界を変革する可能性があるインサイトを迅速に見出して共有することを可能にします。
Introduction to Pandas Pandas is a great library of Python for data science for most industry applications with massive amounts of different types of data. In this tutorial, we will discuss the use of Pandas, including the advanced concepts of the Pandas library for data science. We generally have a massive amount of data. And to handle it, we have already explored NumPy for data science. But is N
We have seen that Python language is a powerful tool for data science and data operations, but how powerful is Python for Data visualization? One of the key responsibilities of Data scientists is to communicate results effectively with the stakeholders. This is where the power of visualization comes into play. Creating effective visualizations helps businesses identify patterns and subsequently he
A table of named individuals along with a select set of characteristics. This table arranges records containing personal characteristics as columns and rows. Each person is a row, and each characteristic makes up a different column. The characteristics use different types of data, like dates, numbers, and text. This arrangement makes it easy to look up individual values or make comparisons across
State of JavaScript 2023
It should be clear by now that, for better or for worse, JavaScript is not slowing down. Between server components, server actions, signals, compilers, and more, we're seeing new innovations pop up faster than most of us can handle. The trick to avoiding the dreaded JavaScript fatigue is remembering that you can pick your lane: sure, you can live life on the cutting edge with the early adopters; b
Machine Learning is making a buzz in the industry. And it’s the right time to get familiar with it. Let’s get the basics right. Let’s get started. What is Machine Learning What the heck is machine learning? If I had to quote it in a single sentence, I would say, ‘Machine Learning is a way to find a pattern in data to predict the future. The above is not the only definition of machine learning. The
State of HTML 2023
While JavaScript was taking over the web, and CSS was gaining new superpowers year over year, it could seem like HTML was content to stay dormant, happy to cede center stage to its younger siblings. After all once you've learned about <div>s and <h>s 1 through 6, what else is there to know? Quite a lot, as it turns out! Once again we drafted Lea Verou to put her in-depth knowledge of the web platf
This article will discuss the Matplotlib library,” in the data scientist’s toolbox on Python. Matplotlib is a library very commonly used by data scientists…. In addition to “Matplotlib,” “Pandas,” and “NumPy” are important parts of the data scientist’s toolbox. Introduction to Matplotlib Is it possible to know your data’s trend or pattern without visualization? In my view, the answer is definitely
K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance betwee
世界中でビッグデータの活用ニーズは高まり続けています。しかしながら、コストをかけ粉骨砕身集めたデータをテーブル形式にして眺めているだけでは、そのストーリーを理解し次のアクションに活用できているとは言い切れません。 データ分析作業において重要なのは「データの可視化」です。可視化により、既成概念に新たな切り口を与え、組織的に共有し、効果的な行動に繋げることが期待できます。 そしてデータ可視化する際に有効なツールが「BIツール」です。BIツールは「データ理解の深堀り」や「全社的な情報共有」の手段として、注目されています。今回はデジタルマーケティングの視点から、GA4の登場により比較される事が多くなった Tableau Looker Studio を5つの側面から比較してみました。 データ可視化ツール（BIツール）とは まず、Tableauの公式サイトのBIツールの定義を見てみましょう。 BI ツ
Introducing Spectrum 2: Our vision for the future of Adobe experience design
Introducing Spectrum 2: Our vision for the future of Adobe experience design A preview of the comprehensive update coming to Adobe's design system Imagine you’re designing a house. Your first step might be to draw a picture. To turn that picture into a house would require the input of many experts—architects, engineers, and builders—and would involve planning, teamwork, and time with each collabor
Python Interview Questions
Here is a list of common Python interview questions with detailed answers to help you prepare for the interview as a Python developer. Python, with its versatile use cases and straightforward syntax, has seen its popularity growing continuously in software development, data science, artificial intelligence, and many other fields. As such, interviews for Python-related positions are designed not on
The declarative paradigm is becoming ubiquitous in data engineering, to the point we sometimes feel we became YAML developers. Yet, I’ll argue it’s a good thing! Let’s take a step back and look at what it means to be declarative, and how it impacts the data systems we build. Data & logicFundamentally, a data platform is made of 2 pieces: Data. On the frontend, we find the actual files, tables, das
February 2024 Feb 27, 2024 What Microsoft Fabric means for your semantic models: New options and approaches Feb 27, 2024 Feb 27, 2024 What Microsoft Fabric means for your semantic models: Scenario 1 Feb 27, 2024 Feb 27, 2024 What Microsoft Fabric means for your semantic models: Scenario 2 Feb 27, 2024 Feb 27, 2024 What Microsoft Fabric means for your semantic models: Scenario 3 Feb 27, 2024 Januar
Business Intelligence Market Size, Share | Forecast 2032
Business Intelligence Market Research Report Information by Technology (Mobile BI, Cloud BI, and Social BI), By Service (Hosted and Managed), By Deployment (On-Premises and On Cloud), By Component (Hardware and Software), And by Region (North America, Europe, Asia-Pacific, And Rest Of The World) –Market Forecast Till 2032. Business Intelligence Market Overview Business Intelligence Market Size was
It used to be easy to pick colors for design systems. Years ago, you could pick a handful of colors to match your brand’s ethos, or start with an off-the-shelf palette (remember flatuicolors.com?). Each hue and shade served a purpose, and usually had a quirky name like “idea yellow” or “innovation blue”. This hands-on approach allowed for control and creativity, resulting in color schemes that cou
Mastering LLM Techniques: LLMOps | NVIDIA Technical Blog
Businesses rely more than ever on data and AI to innovate, offer value to customers, and stay competitive. The adoption of machine learning (ML), created a need... Businesses rely more than ever on data and AI to innovate, offer value to customers, and stay competitive. The adoption of machine learning (ML), created a need for tools, processes, and organizational principles to manage code, data, a
大谷翔平 大リーグでホームラン王を獲得 日本選手初の快挙 | NHK つらい記憶のフラッシュバックは「テトリス」をやると減る、研究 海外のCGクリエイターが『シン・ゴジラ』から日米の怪獣映画を比較する動画が面白い「大事なのは視点」「ハリウッドは怪獣を擬人化しすぎ」 四谷大塚の別講師も新たに逮捕 共謀し女子児童を盗撮疑い | NHK サーバレスに最適化したJava実行基盤「GraalOS」、オラクルが発表。Javaをネイティブバイナリにコンパイルし瞬時に起動 岡田准一11月にもジャニーズ退所か 周囲に意向伝える 事務所２度目の会見と同日発表も（日刊スポーツ） - Yahoo!ニュース [第41話]正反対な君と僕 - 阿賀沢紅茶 | 少年ジャンプ＋ イドの底 - 須田雄太 | 少年ジャンプ＋ [第73話]ラーメン赤猫 - アンギャマン | 少年ジャンプ＋ 世界初！？Vtuberと生身のアイドル
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ヤバいデータ分析（書籍・記事であまり扱われてないが重要なこと） - Qiita
How video games use LUTs and how you can too
無料で学ぶRと統計解析：おすすめのウェブサイト - Qiita
An easy look at Grafana architecture
Mastering Customer Segmentation with LLM
K-Means Clustering for Unsupervised Machine Learning
Flitter - Data Visualization Framework
Data Engineer: Interview Questions
Pandas: An Ultimate Library for Data Science
Data Visualization Using Python
great_tables - The Design Philosophy of Great Tables
Introduction to Machine Learning
Fundamentals of Matplotlib Library for Data Science
k-NN (k-Nearest Neighbors) in Supervised Machine Learning
【徹底比較】データ可視化ツール LookerStudio VS Tableau | 株式会社プリンシプル
YAML developers and the declarative data platforms
Does it feel like too much? — DATA GOBLINS
How to generate color palettes for design systems
2023/10/02(月)の出来事 - My Bookmark