{"id":2063,"date":"2018-10-16T10:20:03","date_gmt":"2018-10-16T08:20:03","guid":{"rendered":"http:\/\/jakisproblem.pl\/?p=2063"},"modified":"2018-10-16T10:59:56","modified_gmt":"2018-10-16T08:59:56","slug":"analiza-danych-vs-analityka-danych","status":"publish","type":"post","link":"http:\/\/u239160.webh.me\/jakisproblem.pl\/index.php\/2018\/10\/16\/analiza-danych-vs-analityka-danych\/","title":{"rendered":"Analiza danych vs. analityka danych"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Analityka danych<\/strong><\/td>\nAnaliza danych<\/strong><\/td>\n<\/tr>\n
Forma<\/strong><\/td>\n<\/tr>\n
Analityka danych jest og\u00f3ln\u0105 form\u0105 analityki, kt\u00f3ra\u00a0jest wykorzystywana w biznesie do podejmowania decyzji na podstawie\u00a0danych kt\u00f3rymi s\u0105 nap\u0119dzane<\/td>\nAnaliza danych jest szeczg\u00f3ln\u0105 form\u0105 analityki\u00a0wykorzystywan\u0105 w biznesie do\u00a0analizy danych i \u00a0wgl\u0105du\u00a0do nich<\/td>\n<\/tr>\n
Struktura<\/strong><\/td>\n<\/tr>\n
Analityki danych polega na gromadzenia danych i og\u00f3lnej\u00a0kontroli czy maj\u0105 one\u00a0jedno lub wi\u0119cej zastosowa\u0144<\/td>\nAnaliza danych\u00a0polega na zdefiniowaniu danych,
\noczyszczeniu, przekszta\u0142ceniu danych w celu uzyskania znacz\u0105cego wyniku<\/td>\n<\/tr>\n
Narz\u0119dzia<\/strong><\/td>\n<\/tr>\n
G\u0142ownie R, Tableau Public, SAS, Apache Spark, Excel<\/td>\nOpenRefine, KNIME, RapidMiner, Google Fusion Tables,\u00a0Tableau Public, NodeXL,\u00a0 WolframAlpha<\/td>\n<\/tr>\n
Sekwencja<\/strong><\/td>\n<\/tr>\n
Cykl analityki danych sk\u0142ada si\u0119 z:<\/p>\n
    \n
  • oceny przypadku biznesowego<\/li>\n
  • rozpoznania danych<\/li>\n
  • pobrania i filtrowania danych<\/li>\n
  • ekstrakcji danych<\/li>\n
  • sprawdzeniu poprawno\u015bci i czyszczeniu danych<\/li>\n
  • agregacji i przedstawieniu danych<\/li>\n
  • analizie danych<\/li>\n
  • wizualizacji danych<\/li>\n
  • wykorzystaniu wynik\u00f3w analiz<\/li>\n<\/ul>\n<\/td>\n
Proces analizy danych sk\u0142ada si\u0119 z:<\/p>\n
    \n
  • gromadzenia danych<\/li>\n
  • szorowania danych (zmiana lub usuni\u0119cie danych, kt\u00f3re s\u0105 niepoprawne, niekompletne, niew\u0142a\u015bciwie sformatowane lub powielone)<\/li>\n
  • analizy danych i dok\u0142adnej\u00a0interpretacji danych\u00a0aby\u00a0zrozumie\u0107, co twoje dane chc\u0105 powiedzie\u0107<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n
Zastosowanie<\/strong><\/td>\n<\/tr>\n
Analityki danych mog\u0105 by\u0107 stosowane do znajdowania ukrytych wzorc\u00f3w, anonimowych korelacji, oczekiwa\u0144 klient\u00f3w, trend\u00f3w rynkowych i innych koniecznych informacji, kt\u00f3re mog\u0105 pom\u00f3c podj\u0105\u0107 bardziej znacz\u0105ce decyzje biznesowe<\/td>\nAnaliza danych mo\u017ce by\u0107 wykorzystana na r\u00f3\u017cne sposoby poprzez analiz\u0119
\nopisow\u0105,\u00a0eksploracyjn\u0105, inferencyjn\u0105, predyktywn\u0105 i wydobywanie\u00a0 przydanych informacji z danych<\/td>\n<\/tr>\n
Przyk\u0142ad<\/strong><\/td>\n<\/tr>\n
Za\u0142\u00f3\u017cmy, \u017ce masz 1 GB danych dotycz\u0105cych zam\u00f3wie\u0144 z ostatniego roku. Chcesz przewidzie\u0107 jakie b\u0119dzie nast\u0119pne zam\u00f3wienie. U\u017cyjesz\u00a0 do tego analityki danych<\/td>\nZa\u0142\u00f3\u017cmy, \u017ce masz 1 GB danych dotycz\u0105cych zam\u00f3wie\u0144 z ostatniego roku. Chcesz si\u0119 dowiedzie\u0107 co si\u0119 dot\u0105d wydarzy\u0142o. To znaczy, \u017ce w analizie danych patrzymy w przesz\u0142o\u015b\u0107<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u0179r\u00f3d\u0142o:\u00a0https:\/\/www.educba.com\/data-analytics-vs-data-analysis\/<\/p>\n

Wojciech Januszko, Systemy informacji gospodarczej. , Stowarzyszenie Bibliotekarzy Polskich, Warszawa 2001, ISBN: 83-87629-72-3 (17+4)
\n(cytat, str. 105) Przez ekstrakcj\u0119 danych<\/strong> rozumiemy proces automatycznego odkrywania znacz\u0105cych, po\u017cytecznych, dotychczas nie znanych i wyczerpuj\u0105cych informacji z du\u017cych baz danych, informacji ujawniaj\u0105cych ukryt\u0105 wiedz\u0119 o badanym przedmiocie;<\/span> wiedza ta przyjmuje posta\u0107 regu\u0142, prawid\u0142owo\u015bci, tendencji i korelacji, i jest nast\u0119pnie przedstawiana przygotowanemu do jej spo\u017cytkowania u\u017cytkownikowi w celu rozwi\u0105zania stoj\u0105cych prze ni\u0105\/nim problem\u00f3w i podj\u0119cia istotnych decyzji.<\/p>\n

 <\/p>\n

What is Data Science?<\/h2>\n

Data science is a multidisciplinary field focused on finding actionable insights from large sets of raw and structured data. The field primarily fixates on unearthing answers to the things we don\u2019t know we don\u2019t know. Data science experts use several different techniques to obtain answers, incorporating computer science, predictive analytics, statistics, and machine learning to parse through massive data sets in an effort to establish solutions to problems that haven\u2019t been thought of yet.<\/p>\n

Data scientists<\/a>\u00a0main goal is to ask questions and locate potential avenues of study, with less concern for specific answers and more emphasis placed on finding the right question to ask. Experts accomplish this by predicting potential trends, exploring disparate and disconnected data sources, and finding better ways to analyze information.<\/p>\n

What is Data Analytics?<\/h2>\n

Data analytics focuses on processing and performing statistical analysis on existing data sets. Analysts concentrate on creating methods to capture, process, and organize data to uncover actionable insights for current problems, and establishing the best way to present this data. More simply, the field of\u00a0data and analytics<\/a>\u00a0is directed towards solving problems for questions we know we don\u2019t know the answers to. More importantly, it\u2019s based on producing results that can lead to immediate improvements.<\/p>\n

Data analytics also encompasses a few different branches of broader statistics and analysis which help combine diverse sources of data and locate connections while simplifying the results.<\/p>\n

What is The Difference?<\/h2>\n

While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science is an umbrella term for a group of fields that are used to mine large data sets. Data analytics is a more focused version of this and can even be considered part of the larger process. Analytics is devoted to realizing actionable insights that can be applied immediately based on existing queries.<\/p>\n

Another significant difference in the two fields is a question of\u00a0exploration<\/a>. Data science isn\u2019t concerned with answering specific queries, instead parsing through massive data sets in sometimes unstructured ways to expose insights. Data analysis works better when it is focused, having questions in mind that need answers based on existing data. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked.<\/p>\n

More importantly, data science is more concerned about asking questions than finding specific answers. The field is focused on establishing potential trends based on existing data, as well as realizing better ways to analyze and model data.<\/p>\n

 <\/p>\n\n\n\n\n\n\n\n
<\/th>\nData Science<\/th>\nData Analytics<\/th>\n<\/tr>\n
Scope<\/strong><\/td>\nMacro<\/td>\nMicro<\/td>\n<\/tr>\n
Goal<\/strong><\/td>\nTo ask the right questions<\/td>\nFind actionable data<\/td>\n<\/tr>\n
Major Fields<\/strong><\/td>\nMachine learning, AI, search engine engineering, corporate analytics<\/td>\nHealthcare, gaming, travel, industries with immediate data needs<\/td>\n<\/tr>\n
Using Big Data<\/strong><\/td>\nYes<\/td>\nYes<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

The two fields can be considered different sides of the same coin, and their functions are highly interconnected. Data science lays important foundations and parses big data sets to create initial observations, future trends, and potential insights that can be important. This information by itself is useful for some fields especially modeling, improving machine learning, and enhancing AI algorithms as it can improve how information is sorted and understood. However, data science asks important questions that we were unaware of before while providing little in the way of hard answers. By adding data analytics into the mix, we can turn those things we know we don\u2019t know into actionable insights with practical applications.<\/p>\n

When thinking of these two disciplines, it\u2019s important to forget about viewing them as \u2018data science vs data analytics\u2019. Instead, we should see them as parts of a whole that are vital to understanding not just the information we have, but how to better analyze and review it.<\/p>\n

Data Science vs. Data Analytics – What’s the Difference?<\/a><\/p><\/blockquote>\n