投稿時間:2021-06-11 19:50:23 RSSフィード2021-06-11 19:00 分まとめ(58件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
IT	気になる、記になる…	｢iPhone 13｣シリーズとみられるモデルがユーラシア経済委員会のデータベースに登録される	https://taisy0.com/2021/06/11/141724.html	consomac	2021-06-11 09:11:47
ROBOT	ロボスタ	藤田ワークス 溶接と金属プレス加工機への投入作業に協働ロボット「UR5」を導入 溶接技術の習得期間を数か月に短縮し、作業効率も向上	https://robotstart.info/2021/06/11/ur-robot-fujitaworks.html	株式会社藤田ワークス	2021-06-11 09:00:29
IT	ITmedia 総合記事一覧	[ITmedia Mobile] IoT製品が最大50％オフになる「＋Style 夏のボーナスセール」開催　6月21日まで	https://www.itmedia.co.jp/mobile/articles/2106/11/news139.html	itmediamobileiot	2021-06-11 18:30:00
IT	ITmedia 総合記事一覧	[ITmedia エンタープライズ] Chromeに緊急の脆弱性、サイバー攻撃での利用もすでに観測	https://www.itmedia.co.jp/enterprise/articles/2106/11/news129.html	chrome	2021-06-11 18:30:00
python	Pythonタグが付けられた新着投稿 - Qiita	RealSenseによるフィルター処理後の距離取得に関して	https://qiita.com/zebracrypto7/items/19ff0393185f1609e6b9	RealSenseによるフィルター処理後の距離取得に関して困ったこと以下のソースコードでRealSenseで得た画像からピクセル座標指定で深度を取得しようとしましたtestpyframespipelinewaitforframesrealsenseから画像取得depthframeflamesgetdepthframe画像から深度画像取得フィルター処理temporalrstemporalfilterdepthframetemporalprocessdepthframe深度画像からピクセル座標の深度を取得distancedepthframegetdistanceすると以下のようなエラーが起きます。	2021-06-11 18:01:51
js	JavaScriptタグが付けられた新着投稿 - Qiita	React-Reduxに関する理解	https://qiita.com/yut85/items/888a727fc7c6d41bf7d7	mapStateToPropsとmapDispatchToPropsmapStateToPropsは、コンポーネントに渡したいstateを入れることができます。	2021-06-11 18:22:05
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	目覚ましアプリのアプリ起動機能の実装方法（kotlin,java）	https://teratail.com/questions/343513?rss=all	android	2021-06-11 18:54:43
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	正規表現での特殊文字？置換	https://teratail.com/questions/343512?rss=all	poppler	2021-06-11 18:54:12
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	wordpressで記事が5000件を超えるとRSSが配信されなくなるのを解決したい。	https://teratail.com/questions/343511?rss=all	wordpressで記事が件を超えるとRSSが配信されなくなるのを解決したい。	2021-06-11 18:51:50
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	なぜvoidは戻り値を返さないのに、printlnメソッドが実行されるのか？	https://teratail.com/questions/343510?rss=all	なぜvoidは戻り値を返さないのに、printlnメソッドが実行されるのかpublicnbspclassnbspFoopublicnbspvoidnbspprintSystemoutprintlnquotFooprintquotのprintメソッドを使用すると、Fooprintが出力されるのですが、voidは戻り値を返さないと聞きました。	2021-06-11 18:44:47
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Wordpress の author.php に、カスタムフィールドの値を出力する方法が分かりません。	https://teratail.com/questions/343509?rss=all	Wordpressのauthorphpに、カスタムフィールドの値を出力する方法が分かりません。	2021-06-11 18:42:57
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	ラズパイ　リモート接続　無線LAN無し	https://teratail.com/questions/343508?rss=all	無線lan	2021-06-11 18:42:12
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	何故配列リテラルを使用しているのか意味がいまいち分からないので解決したい	https://teratail.com/questions/343507?rss=all	何故配列リテラルを使用しているのか意味がいまいち分からないので解決したい解決したいこと何でclassListremoveで配列リテラルを使用しているのかが分からないので意味がわかるよう解決したい分からないことclassListremoveの部分で、どうしてわざわざ配列リテラルを使用しているのかがわかりません。	2021-06-11 18:38:37
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	3Dが描画されるまでの座標変換を整理したい。	https://teratail.com/questions/343506?rss=all	行列	2021-06-11 18:37:38
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	「MCC テストアカウント」の作成方法	https://teratail.com/questions/343505?rss=all	「MCCテストアカウント」の作成方法解決したいことGooglenbspAdsnbspAPIを使用するにあたり下記を参考にしています。	2021-06-11 18:36:13
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	for分でK＝3.5.7.9の場合のK-NNの図を描図	https://teratail.com/questions/343504?rss=all		2021-06-11 18:31:31
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	rubyで ファイルから読み込んで配列に格納する方法	https://teratail.com/questions/343503?rss=all	rubyでファイルから読み込んで配列に格納する方法rubyで、ファイルの中身を配列に格納して処理に使用したくて、以下のように記述しました。	2021-06-11 18:21:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	K-Nearest Neighborの原理について、緊急です！	https://teratail.com/questions/343502?rss=all	KNearestNeighborの原理について、緊急です授業で下の問題を説明する課題が出されたのですけど、よく分かりません。	2021-06-11 18:20:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Realmに保存した情報を元にMapKitのピンの絵柄を変えたい	https://teratail.com/questions/343501?rss=all	Realmに保存した情報を元にMapKitのピンの絵柄を変えたい前提・実現したいこと本やネットで学びながら個人的にiOSのアプリを作りたいと思い始めました。	2021-06-11 18:16:28
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	git push herokuが成功しない(webpack not installedが消えない)	https://teratail.com/questions/343500?rss=all	gitpushherokuが成功しないwebpacknotinstalledが消えない前提・実現したいことRailsでwebサービスを作成しています。	2021-06-11 18:12:04
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	c++でハッシュテーブルを実装したいです。	https://teratail.com/questions/343499?rss=all	cでハッシュテーブルを実装したいです。	2021-06-11 18:08:19
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Rails】 Deviseでundefined local variable or method `resource_class'が出た時の対処	https://qiita.com/renyoshizawa0515/items/5a67f6a44cd58411d4f0	【Rails】Deviseでundefinedlocalvariableormethodresourceclassxが出た時の対処undefinedlocalvariableormethodresourceclassが出た時の対処法背景deviseのストロングパラメーターに新たなパラメーターを追加して新規登録画面を実装しようとしていたところ上記のエラーが発生ローカル変数またはメソッドresourceclassが未定義のためエラーが発生している様子該当箇所applicationcontrollerbeforeactionconfigurepermittedparametersprotecteddefconfigurepermittedparametersdeviseparametersanitizerpermitsignupkeysnamephonenumberpostcodeaddressend修正箇所beforeactionconfigurepermittedparametersの後にifdevisecontrollerを追加することで無事修正完了applicationcontrollerbeforeactionconfigurepermittedparametersifdevisecontrollerprotecteddefconfigurepermittedparametersdeviseparametersanitizerpermitsignupkeysnamephonenumberpostcodeaddressend補足情報devisecontollerはdeviseを導入することで利用可能なヘルパーメソッドの一つで、「deviseに関連する画面で処理を行った時に」という意味合いがあり、これによってapplicationcontrollerと紐付けている。	2021-06-11 18:28:10
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS Perspective の構築	https://qiita.com/leomaro7/items/101c6fa4514356f538ce	今回は、AdminUserEmailAddressの入力と、AlreadyHaveConfigSetupを「No」にする以外はデフォルトです。	2021-06-11 18:58:41
AWS	AWSタグが付けられた新着投稿 - Qiita	EKSクラスター上で起動したFargateのpodにセキュリティグループを設定してみた	https://qiita.com/araryo/items/c22fb9c3630e36010682	kubectlexecitnginxbdbbbfqvcurlcurlFailedtoconnecttoportConnectiontimedoutrolenginxのラベルがついているnginxのpodに対してセキュリティグループが適用されている状態となります。	2021-06-11 18:46:07
AWS	AWSタグが付けられた新着投稿 - Qiita	JPCYBER S3 DriveのACL適用オプションを確認する	https://qiita.com/handy-dd18/items/b30ea162bdbb491de548	アップロードしたファイルのオブジェクトACLを確認するとSバケットACLと同じ設定になっていました。	2021-06-11 18:01:40
Docker	dockerタグが付けられた新着投稿 - Qiita	Docker Machineインストールでエラー	https://qiita.com/ryu0121/items/ff095fd9941b41f088ec	DockerMachineインストールでエラー結論コマンドcurl L  gtusrlocalbindockermachine ampamp chmod x usrlocalbindockermachine以下のDoc通りのコマンドではDockerMachineをインストールすることはできませんでした。	2021-06-11 18:29:14
Git	Gitタグが付けられた新着投稿 - Qiita	Gitコマンドにエイリアスを付けてみる件	https://qiita.com/yuya88/items/c512dd1296cc8a9a2630	エイリアスとは何か、そしてどのような使い方をするのがこの記事でご説明させていただきます。	2021-06-11 18:24:36
Git	Gitタグが付けられた新着投稿 - Qiita	Git cmd (Note)	https://qiita.com/nayylin/items/3106ebc4272e636658ce	Git cmd Note ContentCheck Git versionAdd source to GitCloneCreate new branchDelete branchPullPushMergeCheck Git versiongit versionAdd source to remoteGo to source folder Add source to Git git init For first time using gitgit remote add origin GIT LINK Will get after created repository Set a new remotegit remote v Verify new remote Will show detail of processCloneClone master of Git repositorygit clone GIT LINK Clone branch of Git repositorygit clone b BRANCH NAME GIT LINK Create new branchClone the original project for create branch Go to No s project folder Switch to new bgit checkout b NEW BRANCH NAME No s BRANCH NAME or MASTER Push created branch to remote git push set upstream origin NEW BRANCH NAME Delete branchgit push delete origin BRANCH NAME PullFirstly go to project folder and run following command pull lastest source from mastergit pull origin masterpull lastest source from branchgit pull origin BRANCH NAME Pushgit pushMergeMergegit merge BRANCH NAME Cancel mergegit merge abort	2021-06-11 18:14:12
Ruby	Railsタグが付けられた新着投稿 - Qiita	Usersモデルの単体テストコードの記述について	https://qiita.com/hedgehog-genki/items/cbaf89e891431376f1af	ぶっちゃけここまでがめんどくさかっただけで、後のテストコードはバリデーションがきちんと働いているかのテストなので、記述自体は楽ちん。	2021-06-11 18:51:48
Ruby	Railsタグが付けられた新着投稿 - Qiita	Rails マイグレーションファイルでミスがあった時	https://qiita.com/hedgehog-genki/items/77695a5f246a6ea536a5	仮に上のマイグレーションファイルを修正したいときはもう一度railsdbrollbackを実行して、ファイルを修正したのちにいつも通り、マイグレーションを実行です。	2021-06-11 18:27:23
海外TECH	DEV Community	Introducing Otlio, a Powerful Todo List 🚀	https://dev.to/stanleyowen/introducing-otlio-a-powerful-todo-list-32n6	Introducing Otlio a Powerful Todo List Hello World ‍ ️I would like to introduce Otlio a powerful Todo List which also supports drag and drop Website Link FeaturesSupport for Google and GitHub OAuth LoginSupport for FA Two Factor Authentication Support for Drag and Drop Feature Heavy focus on security Customer Support Available in Dark Mode Otlio is also an open source project which is hosted on If you find this project useful leave a on GitHub to keep a beginner motivated Any comments will be appreciated and everyone is welcome to contribute to this project U ノ Happy Coding You may also like these articles Build a Login and Register Form with HTML and CSS Stanley Owen・Mar ・ min read tutorial html css webdev Todo Application An Open Source and Easy to Use Web Application Stanley Owen・Feb ・ min read showdev javascript webdev productivity Fire UI A User Friendly and Reliable CSS Framework Stanley Owen・Dec ・ min read css javascript opensource	2021-06-11 09:44:50
海外TECH	DEV Community	🅹🆂-🆁🅴🅰🅲🆃-🅳🆁🆈 🎉	https://dev.to/filoscoder/-4e7i	🅹🆂 🆁🅴🅲🆃 🅳🆁🆈🅹🆂 🆁🅴🅲🆃 🅳🆁🆈Hi I ve just opened a public space to build up a useful collection of helper functions for ordinary and concurrent daily problems I m a freelance Software Engineer my stack is Javascript amp React After some time working on different projects I found myself facing the same problems and solving them with the same pattern This is a repository for saving valuable time and stay as DRY as possible while working This open source has his goal on stay as 🄳🅁🅈 Don t Repeat Yourself as possible ExampleHow many times do I have to write a function to mask format a mobile phone number input This is how I implemented Parameter gt inputValue string Output gt xxx xxxx xxxx export const phoneMask inputValue gt inputValue replace D g replace d d d g function a b c return c a b c b a b a SAME CODE to solve the SAME PROBLEMS Contribute your grain of code	2021-06-11 09:39:11
海外TECH	DEV Community	Solution: Stone Game VII	https://dev.to/seanpgallivan/solution-stone-game-vii-3lei	Solution Stone Game VIIThis is part of a series of Leetcode solution explanations index If you liked this solution or found it useful please like this post and or upvote my solution post on Leetcode s forums Leetcode Problem Medium Stone Game VII Description Jump to Solution Idea Code JavaScript Python Java C Alice and Bob take turns playing a game with Alice starting first There are n stones arranged in a row On each player s turn they can remove either the leftmost stone or the rightmost stone from the row and receive points equal to the sum of the remaining stones values in the row The winner is the one with the higher score when there are no stones left to remove Bob found that he will always lose this game poor Bob he always loses so he decided to minimize the score s difference Alice s goal is to maximize the difference in the score Given an array of integers stones where stones i represents the value of the ith stone from the left return the difference in Alice and Bob s score if they both play optimally Examples Example Input stones Output Explanation Alice removes and gets points Alice Bob stones Bob removes and gets points Alice Bob stones Alice removes and gets points Alice Bob stones Bob removes and gets points Alice Bob stones Alice removes and gets points Alice Bob stones The score difference is Example Input stones Output Constraints n stones length lt n lt lt stones i lt Idea Jump to Problem Description Code JavaScript Python Java C Like most of the Stone Game problems this one boils down to a system of ever repeating subproblems as the there are many different ways to get to the same board condition as we move towards the end of the game This naturally points to a dynamic programming DP solution In order to represent the different board positions we d normally build an N N DP matrix where N is the length of the stones array S In this DP array dp i j would represent the best score difference with i representing the leftmost remaining stone s index and j representing the rightmost remaining stone s index Since we re using a top down DP approach we ll start at i N and iterate backwards and start each nested for loop at j i This ensures that we re building the pyramid of DP results downward always starting each row with i and j next to each other For each row we ll keep track of the sum total of the stones in the range i j by adding S j at each iteration of j Then we can represent the current player s ideal play by choosing the best value between picking the stone at i total S i and picking the stone at j total S j For each option we have to also subtract the best value that the other player will get from the resulting board position dp i j or dp i j Since we will only be building off the current and previously finished rows however we can actually eliminate the DP matrix and instead just define two N length arrays representing the current and previous rows dpCurr dpLast and swap between them at each iteration of i This will drop the space complexity from O N to O N At the end the solution will be the value stored in the DP array representing the board position with all stones present We should therefore return dpCurr N Time Complexity O N where N is the length of SSpace Complexity O N for the two dp arrays Javascript Code Jump to Problem Description Solution Idea var stoneGameVII function S let N S length dpCurr new UintArray N dpLast new UintArray N for let i N i i let total S i temp dpLast dpLast dpCurr dpCurr temp for let j i j lt N j total S j dpCurr j Math max total S i dpLast j total S j dpCurr j return dpCurr N Python Code Jump to Problem Description Solution Idea class Solution def stoneGameVII self S List int gt int N len S dpCurr dpLast N N for i in range N total S i dpLast dpCurr dpCurr dpLast for j in range i N total S j dpCurr j max total S i dpLast j total S j dpCurr j return dpCurr Java Code Jump to Problem Description Solution Idea class Solution public int stoneGameVII int S int N S length int dpCurr new int N dpLast new int N for int i N i gt i int total S i int temp dpLast dpLast dpCurr dpCurr temp for int j i j lt N j total S j dpCurr j Math max total S i dpLast j total S j dpCurr j return dpCurr N C Code Jump to Problem Description Solution Idea class Solution public int stoneGameVII vector lt int gt amp S int N S size vector lt int gt dpCurr N dpLast N for int i N i i int total S i dpLast swap dpCurr for int j i j lt N j total S j dpCurr j max total S i dpLast j total S j dpCurr j return dpCurr N	2021-06-11 09:08:33
海外TECH	DEV Community	What is data cleaning?	https://dev.to/skillpaythebil1/what-is-data-cleaning-3njl	What is data cleaning Data cleaning is one of the most important procedures you should learn in data analysis You will constantly be working with different sets of data and the accuracy or completeness of the same is never guaranteed Because of this reason you should learn how to handle such data and make sure the incompleteness or errors present do not affect the final outcome Why should you clean data Especially if you did not produce it in the first place Using unclean data is a sure way to get poor results You might be using a very powerful computer capable of performing calculations at a very high speed but what they lack is intuition Without this you must make a judgement call each time you go through a set of data In data analysis your final presentation should be a reflection of the reality in the data you use For this reason you must eliminate any erroneous entries Possible Causes of Dirty Data One of the most expensive overheads in many organizations is data cleaning Unclean data is present in different forms Your company might suffer in the form of omissions and errors present in the master data you need for analytical purposes Since this data is used in important decision making processes the effects are costly By understanding the different ways dirty data finds its way into your organization you can find ways of preventing it thereby improving the quality of data you use In most instances automation is applied in data collection Because of this you might experience some challenges with the quality of data collected or consistency of the same Since some data is obtained from different sources they must be collated into one file before processing It is during this process that concerns as to the integrity of the data might arise The following are some explanations as to why you have unclean data Incomplete dataThe problem of incomplete data is very common in most organizations When using incomplete data you end up with many important parts of the data blank For example if you are yet to categorize your customers according to the target industry it is impossible to create a segment in your sales report according to industry classification This is an important part of your data analysis that will be missing hence your efforts will be futile or expensive in terms of time and resources invested before you get the complete and appropriate data Errors at inputMost of the mistakes that lead to erroneous data happen at data entry points The individual in charge might enter the wrong data use the wrong formula misread the data or innocently mistype the wrong data In the case of an open ended report like questionnaires the respondents might input data with typos or use words and phrases that computers cannot decipher appropriately Human error at input points is always the biggest challenge in data accuracy Data inaccuraciesInaccurate data is in most cases a matter of context You could have the correct data but for the wrong purpose Using such data can have far reaching effects most of which are very costly in the long run Think about the example of a data analyst preparing a delivery schedule for clients but the addresses are inaccurate The company could end up delivering products to their customers but with the wrong address details As a matter of context the company does have the correct addresses for their clients but they are not matched correctly Duplicate dataIn cases where you collect data from different sources there is always a high chance of data duplication You must have a lot of checks in place to ensure that duplicates are identified For example one report might list student scores under Results while another will have them under Performance The data under these tags will be similar but your sensors will consider them as two independent entities Problematic sensorsUnless you are using a machine that periodically checks for errors and corrects them or alerts you it is possible to encounter errors as a result of problematic sensors Machines can be faulty or breakdown too which increases the likelihood of a problematic data entry Incorrect dataEntries An incorrect entry will always deliver the wrong result Incorrect entry happens when your dataset includes entries that are not within the acceptable range For example data for the month of February should range from to or If you have data for February ranging up to there is definitely an error in your entries Data munglingIf at your data entry point you use a machine with problematic sensors it is possible to record erroneous values You might be recording people s ages and the machine inputs a negative figure In some cases the machine could actually record correct data but between the input point and the data collection point the data might be mungled hence the erroneous results If you are accessing data from a public internet connection a network outage during data transmission might also affect the integrity of the data Read Standardization concerns For data obtained from different sources one of the concerns is often how to standardize the data You should have a system or method in place to identify similar data and represent them accordingly Unfortunately it is not easy to manage this level of standardization As a result you end up with erroneous entries Apart from data obtained from multiple sources you can also experience challenges dealing with data obtained from the same source Everyone inputs data uniquely and this might pose a challenge in data analysis How to Identify Inaccurate Data More often you need to make a judgement call to determine whether the data you are accessing is accurate or not As you go through data you must make a logical decision based on what you see The following are some factors you should think about Study the range First check the range of data This is usually one of the easiest problems to identify Let s say you are working on data for primary school kids You know the definitive age bracket for the students If you identify age entries that are either too young or too old for primary school kids whose data you have you need to investigate further Essentially what you are doing here is an overview of a max min approach With these ranges in mind you can skim through data and identify erroneous entries Skimming through is easy if you are working with a few entries If you have thousands or millions of data entries a max min function code can help you identify the wrong entries in an instant You can also plot the data on a graph and visually detect the values that don t fall within the required distribution pattern Investigate the categories How many categories of data do you expect This is another important factor that will help you determine whether your data is accurate or not If you expect a dataset with nine categories anything less is acceptable but not more If you have more than nine categories you should investigate to determine the legitimacy of the additional categories Say you are working with data on marital status and your expected options are single married divorced or widowed If the data has six categories you should investigate to determine why there are two more Read Data consistency Look at the data in question and ensure all entries are consistent In some cases inaccuracies appear as a result of inconsistency This is common when working with percentages Percentages can either be fed into data sets as basis points or decimal points If you have data that has both sets of entries they might be incompatible Inaccuracies across multiple fields This is perhaps one of the most difficult challenges you will overcome when cleaning inaccurate data The following entries for example are valid individually A year old girl is a valid age entry children is also a valid entry However a datapoint that depicts Grace as a year old girl with children is absurd You would need to check for inconsistencies and inaccuracies in several rows and columns Data visualization Plotting data in visual form is one of the easiest ways of identifying abnormal distributions or any other errors in the data Say you are working with data whose visualization should result in a bimodal distribution but when you plot the data you end up with a normal distribution This would immediately alert you that something is not right and you need to check your data for accuracy Number of errors in your data set Having identified the unique errors in the data set you must enumerate them Enumeration will help you make a final decision on how and whether to use the data How many errors are there If you have more than half of the data as inaccurate it is obvious that your presentation would be greatly flawed You must then follow up with the individuals who prepared the data for clarification or find an alternative Missing entries A common data concern that data analysts deal with is working with datasets missing some entries Missing entries is relative If you are missing two or three entries this should not be a big issue However if your data set is missing many entries you have to find out the reason behind this Missing entries usually happen when you are collating data from multiple sources and in the process some of the data is either deleted overwritten or skipped You must investigate the missing entries because the answer might help you determine whether you are missing only a few entries that might be insignificant going forward or important entries whose absence affects the outcome How to Clean Data Having gone through the procedures described above and identified unclean data your next challenge is how to clean it and use accurate data for analysis You have five possible alternatives for handling such a situation Data imputation If you are unable to find the necessary values you can impute them by filling in the gaps for the inaccurate values The closest explanation for imputation is that it is a clever way of guessing the missing values but through a data driven scientific procedure Some of the techniques you can use to impute missing data include stratification and statistical indicators like mode mean and median If you have studied the data and identified unique patterns you can stratify the missing values based on the trend identified For example men are generally taller than women You can use this presumption to fill in missing values based on the data you already have The most important thing however is to try and seek a second opinion on the data before imputing your new values Some datasets are very critical and imputing might introduce a personal bias which eventually affects the outcome Data scaling Data scaling is a process where you change the data range so that you have a reasonable range Without this some values that might appear larger than others might be given prominence by some algorithms For example the age of a sample population generally exists within a smaller range compared to the average population of a city Some algorithms will give the population priority over age and might ignore the age variable altogether By scaling such entries you maintain a proportional relationship between different variables ensuring that they are within a similar range A simple way of doing this is to use a baseline for the large values or use percentage values for the variables Correcting data Correcting data is a far better alternative than removing data This involves intuition and clarification If you are concerned about the accuracy of some data getting clarification can help allay your fears With the new information you can fix the problems you identified and use data you are confident about in your analysis Data removal One of the first things you could think about is to eliminate the missing entries from your dataset Before you do this it is advisable that you investigate to determine why the entries are missing In some cases the best option is to remove the data from your analysis altogether If for example more than of entries in a row is missing and you cannot replace them from any other source that row will not be useful to your analysis It makes sense to remove it Data removal comes with caveats If you have to eliminate any data from your analysis you must give a reason for this decision in a report accompanying your analysis This is important so as to safeguard yourself from claims of data manipulation or doctoring data to suit a narrative Some types of data are irreplaceable so you must consult experts in the associated fields before you remove them Most of the time data removal is applied when you identify duplicates in the data especially if removing the duplicates does not affect the outcome of your analysis Flagging data There are situations where you have columns missing some values but you cannot afford to eliminate all of them If you are working with numeric data a reprieve would be to introduce a new column where you indicate all the missing values The algorithm you are using should identify these values as such In case the flagged values are necessary in your analysis you can impute them or find a better way to correct them then use them in your analysis In case this is not possible make sure you highlight this in your report Cleaning erroneous data can be a difficult process A lot of data scientists generally hope to avoid it especially since it is time consuming However it is a necessary process that will bring you closer to using appropriate data for objective is to use clean data that will give you the closest reflection of the true picture of events How to Avoid Data Contamination From empty data fields to data duplication and invalid addresses there are so many ways you can end up with contaminated data Having looked at possible causes and methods of cleaning data it is important for an expert in your capacity to put measures in place to prevent data contamination in the future The challenges you experienced in cleaning data could easily be avoided especially if the data collection processes are within your control Looking back to the losses your business suffers in dealing with contaminated data and the resource wastage in terms of time you can take significant measures to reduce inefficiencies which will eventually have an impact on your customers and their level of satisfaction One of the most important steps today is to invest in the appropriate CRM programs to help in data handling Having data in one place makes it easier to verify the credibility and integrity of data within your database The following are some simple methods you can employ in your organization to prevent data contamination and ensure you are using quality data for decision making Proper configurations Irrespective of the data handling programs you use one of the most important things is to make sure you configure applications properly Your company could be using CRM programs or simple Excel sheets Whichever the case it is important to configure your programs properly Start with the critical information Make sure the entries are accurate and complete One of the challenges of incomplete data is that there is always the possibility that someone could complete them with inaccurate data to make them presentable when this is not the real picture Data integrity is just as important so make sure you have the appropriate data privileges in place for anyone who has to access critical information Set the correct range for your data entries This way anyone keying in data will be unable to enter incorrect data not within the appropriate range Where possible set your system up such that you can receive notifications whenever someone enters the wrong range or is struggling so that you can follow up later on and ensure you captured the correct data Proper training Human error is one of a data analyst s worst nightmares when trying to prevent data contamination Other than innocent mistakes many errors from human entry are usually about context It is important that you train everyone handling data on how to go about it This is a good way to improve accuracy and data integrity from the foundation data entry Your team must also understand the challenges you experience when using contaminated data and more importantly why they need to be keen at data entry If you are using CRM programs make sure they understand different functionality levels so they know the type of data they should enter Another issue is how to find the data they need When under duress most people key in random or inaccurate data to get some work done or bypass some restrictions By training them on how to search for specific data it is easier to avoid unnecessary challenges with erroneous entries This is usually a problem when you have new members joining your team Ensure you train them accordingly and encourage them to ask for help whenever they are unsure of anything Entry formats The data format is equally important as the desired level of accuracy Think about this from a logical perspective If someone sends you a text message written in all capital letters you will probably disregard it or be offended by the tone of the message However if the same message is sent with proper formatting your response is more positive The same applies to data entry Try and make sure that everyone who participates in data handling is careful enough to enter data using the correct format Ensure the formats are easy to understand and remind the team to update data they come across if they realize it is not in the correct format Such changes will go a long way in making your work easier during analysis Empower data handlersBeyond training your team you also need to make sure they are empowered and aware of their roles in data handling One of the best ways of doing this is to assign someone the data advocacy role A data advocate is someone whose role is to ensure and champion consistency in data handling Such a person will essentially be your data administrator Their role is usually important especially when implementing new systems They come up with a plan to ensure data is cleaned and organized One of their deliverables should include proper data collection procedures to help you improve the results obtained from using the data in question Overcoming data duplication Data duplication happens in so many organizations because the same data is processed at different levels Duplication might eventually see you discard important and accurate data accidentally affecting any results derived from the said data For example ensure your team searches for specific items before they create new ones Provide an in depth search process that increases the search results and reduces the possibility of data duplication For example beyond looking for a customer s name the entry should also include contact information Provide as many relevant fields that can be searched into thereby increasing the possibility of arresting and avoiding duplicates You can find data for a customer named Charles McCarthy in different databases labeled as Charles MacCarthy or Charles Mc Carthy The moment you come across such duplicates the last thing you want to do is to eliminate them from the database Instead investigate further to ascertain the similarities and differences between the entries Consult verify and update the correct entry accordingly Alternatively you can escalate such issues to your data advocate for further action At the same time put measures in place that scans your database to warn users whenever they are about to create a duplicate entry Data filtration Perhaps one of the best solutions would be cleaning data before it gets into your database A good way of doing this would be creating clear outlines on the correct data format to use With such procedures in place you have an easier time handling data If all the conditions are met you will probably handle data cleaning at the entry point instead of once the data is in your database making your work easier Create filters to determine the right data to collect and the data that can be updated later It doesn t make sense to collect a lot of information to give you the illusion of a complete and elaborate database when in a real sense very little of what you have is relevant to your cause The misinformation that arises from inaccurate data can be avoided if you take the right precautionary measures in data handling Data security is also important especially if you are using data sources where lots of other users have access Restrict access to data where possible and make sure you create different access privileges for all users	2021-06-11 09:07:11
Apple	AppleInsider - Frontpage News	Regulatory database adds seven 'iPhone 13' models	https://appleinsider.com/articles/21/06/11/regulatory-database-adds-seven-iphone-13-models?utm_medium=rss	Regulatory database adds seven x iPhone x modelsSeven new iPhone models have been added to the EEC regulatory database a mandatory filing before they can be released The Eurasian Economic Commission database has added a series of part numbers that are listed as smartphones Apple models It further says that these models run software version iOS confirming that they are iPhones and almost certainly variants of the forthcoming iPhone range Model numbers A A A A A A and A have been entered under a single filing in the EEC database Previously the EEC has divided model numbers into two or more different sections but that does not appear to be an indication of any technological difference between the models or ranges Read more	2021-06-11 09:33:30
海外TECH	Engadget	UK competition regulator to supervise Google's Privacy Sandbox changes	https://www.engadget.com/uk-competition-regulator-google-privacy-sandbox-092728177.html?src=rss_b2c	privacy	2021-06-11 09:27:28
医療系	医療介護 CBnews	外国籍コロナ陽性、アフガン・フィリピンが半数-厚労省が空港検疫の検査実績を更新	https://www.cbnews.jp/news/entry/20210611175525	厚生労働省	2021-06-11 18:05:00
金融	金融庁ホームページ	「拠点開設サポートオフィス」の新オフィスの開設について公表しました。	https://www.fsa.go.jp/policy/marketentry/index_5.html	開設	2021-06-11 10:53:00
金融	金融庁ホームページ	「サステナブルファイナンス有識者会議」（第８回）議事次第について公表しました。	https://www.fsa.go.jp/singi/sustainable_finance/siryou/20210611.html	有識者会議	2021-06-11 09:55:00
海外ニュース	Japan Times latest articles	Elon Musk’s China nemesis William Li survived once, but he has a fight ahead	https://www.japantimes.co.jp/news/2021/06/11/business/nio-electric-vehicles/	Elon Musk s China nemesis William Li survived once but he has a fight aheadOn the road to recovery Li s Nio delivered more than vehicles all of them SUVs in the first quarter at an average price of	2021-06-11 18:36:04
ニュース	BBC News - Home	Assisted dying campaigner dies	https://www.bbc.co.uk/news/uk-england-shropshire-57441095	assisted	2021-06-11 09:40:46
ニュース	BBC News - Home	June 21: Delay lockdown lifting, urge local health leaders	https://www.bbc.co.uk/news/uk-57438745	delay	2021-06-11 09:46:31
ニュース	BBC News - Home	UK economy grows in April as shops reopen	https://www.bbc.co.uk/news/business-57438437	hospitality	2021-06-11 09:34:55
ニュース	BBC News - Home	Euro 2020: Johnson must back Rashford taking the knee - Gordon Brown	https://www.bbc.co.uk/news/uk-politics-57439088	country	2021-06-11 09:13:51
ニュース	BBC News - Home	Jail for rapist who faked his death at 'Mortuary Beach'	https://www.bbc.co.uk/news/uk-scotland-highlands-islands-57430215	beaches	2021-06-11 09:16:50
ニュース	BBC News - Home	G7 tax deal: What is it and are Amazon and Facebook included?	https://www.bbc.co.uk/news/business-57384352	companies	2021-06-11 09:19:51
ニュース	BBC News - Home	Covax: How many Covid vaccines have the US and the other G7 countries pledged?	https://www.bbc.co.uk/news/world-55795297	covax	2021-06-11 09:39:10
GCP	Google Cloud Platform Japan 公式ブログ	WebGL による新しいマップ機能の使用	https://cloud.google.com/blog/ja/products/maps-platform/using-new-webgl-powered-maps-features/	傾きと回転の設定設定された傾きと回転でマップを読み込むには、マップを作成するときに「tilt」および「heading」プロパティの値を指定します。	2021-06-11 11:00:00
北海道	北海道新聞	画面上で楽しむオンライン俳句会　若い人も参加しやすく　北見	https://www.hokkaido-np.co.jp/article/554514/	感染拡大	2021-06-11 18:16:00
北海道	北海道新聞	真野由佳梨、目標は「金メダル」　ホッケー、男女主将らが会見	https://www.hokkaido-np.co.jp/article/554504/	日本代表	2021-06-11 18:10:00
北海道	北海道新聞	「手上げ横断」、交通教則に復活　警察庁、改正で４３年ぶり	https://www.hokkaido-np.co.jp/article/554503/	道交法	2021-06-11 18:07:00
北海道	北海道新聞	視覚障害者を靴でナビ　ホンダ従業員が起業、開発	https://www.hokkaido-np.co.jp/article/554502/	視覚障害者	2021-06-11 18:05:00
北海道	北海道新聞	国民投票法改正に市民ら抗議　コロナ下「急ぐ必要ない」	https://www.hokkaido-np.co.jp/article/554501/	国民投票法	2021-06-11 18:03:00
ビジネス	東洋経済オンライン	｢ランドクルーザー｣14年ぶりの新型に見る課題 ラダーフレーム､非電動化…守る“らしさ" | トレンド | 東洋経済オンライン	https://toyokeizai.net/articles/-/433867?utm_source=rss&utm_medium=http&utm_campaign=link_back	世界初公開	2021-06-11 19:00:00
ビジネス	東洋経済オンライン	うつの人が苦しまずに仕事で力を発揮できる心得 ｢丁寧じゃないと許せない｣性格がお金に変わる | ワークスタイル | 東洋経済オンライン	https://toyokeizai.net/articles/-/431300?utm_source=rss&utm_medium=http&utm_campaign=link_back	新型コロナウイルス	2021-06-11 19:00:00
IT	週刊アスキー	【連載】5G活用サービスの実証実験、西新宿で公募開始！	https://weekly.ascii.jp/elem/000/004/058/4058780/	デジタルサービス局は、デジタルの力を活用した行政を総合的に推進し、都政のQOSを飛躍的に向上させるため、新たに設置した組織です。	2021-06-11 18:30:00
IT	週刊アスキー	『とあるIF』にて新レイドイベント「とある花嫁の神聖挙式」が6月12日より開催！	https://weekly.ascii.jp/elem/000/004/058/4058797/	開催	2021-06-11 18:30:00
GCP	Cloud Blog JA	WebGL による新しいマップ機能の使用	https://cloud.google.com/blog/ja/products/maps-platform/using-new-webgl-powered-maps-features/	傾きと回転の設定設定された傾きと回転でマップを読み込むには、マップを作成するときに「tilt」および「heading」プロパティの値を指定します。	2021-06-11 11:00:00