이 주기율표는 데이터 과학 공간의 주요 플레이어를 탐색하는 가이드 역할을 할 수 있습니다. 이 테이블의 자료는 O’Reilly의 2016 Data Science Salary Survey, Gartner의 2017 Magic Science Quadrant 및 KD Nuggets 2016 Software Poll 결과와 같은 데이터 과학 사용자로부터 얻은 설문 조사를보고 선택했습니다. 다른 출처 중. 표의 카테고리가 모두 상호 배타적 인 것은 아닙니다.

이 주기율표는 데이터 과학 공간의 주요 플레이어를 탐색하는 가이드 역할을 할 수 있습니다. 이 테이블의 자료는 O’Reilly의 2016 Data Science Salary Survey, Gartner의 2017 Magic Science Quadrant 및 KD Nuggets 2016 Software Poll 결과와 같은 데이터 과학 사용자로부터 얻은 설문 조사를보고 선택했습니다. 다른 출처 중. 표의 카테고리가 모두 상호 배타적 인 것은 아닙니다.

 

데이터 과학의 주기율표 탐색

테이블의 왼쪽 섹션에는 교육과 관련이있는 회사 목록이 나와 있습니다. 여기에는 코스, 부트 캠프 및 컨퍼런스가 있습니다. 반면에 오른쪽에는 최신 뉴스, 가장 인기있는 블로그 및 데이터 과학 커뮤니티의 관련 자료로 최신 정보를 얻을 수있는 리소스가 있습니다. 중간에는 데이터 과학을 시작하는 데 사용할 수있는 도구가 있습니다. 프로그래밍 언어, 프로젝트 및 문제, 데이터 시각화 도구 등을 찾을 수 있습니다.

이 표는 데이터 과학 자료, 도구 및 회사를 다음 13 가지 범주로 분류합니다.

교육 과정 : 데이터 과학을 배우려는 사람들에게는 데이터 과학 과정을 제공하는 많은 사이트 또는 회사가 있습니다.DataCamp, Coursera 및 Edx의 MOOC 등 학습 스타일에 어울리는 다양한 옵션을 찾을 수 있습니다!

부트 캠프: this section includes resources for those who are looking for more mentored options to learn data science. You’ll see that boot camps like The Data Incubator or Galvanize have been included.

이 섹션에는 데이터 과학을 배우기위한 더 많은 멘토 옵션을 찾고있는 사람들을위한 자료가 포함되어 있습니다. Data Incubator 또는 Galvanize와 같은 부트 캠프가 포함되어 있습니다.

컨퍼런스: learning is not an activity that you do when you go on courses or boot camps. Conferences are something that learners often forget, but they also contribute to learning data science: it’s important that you attend them as a data science aspirant, as you’ll get in touch with the latest advancements and the best industry experts. Some of the ones that are listed in the table are UseR!, Tableau Conference and PyData.

Data: practice makes perfect, and this is also the case for data science. You’ll need to look and find data sets in order to start practicing what you learned in the courses on real-life data or to make your data science portfolio. Data is the basic building block of data science and finding that data can be probably one of the hardest things. Some of the options that you could consider when you’re looking for cool data sets are data.world, Quandl and Statista.

Projects & Challenges, Competitions: after practicing, you might also consider taking on bigger projects: data science portfolios, competitions, challenges, …. You’ll find all of these in this category of the Periodic Table of Data Science! One of the most popular options is probably Kaggle, but also DrivenData or DataKind are worth checking out!

Programming Languages & Distributions:  data scientists generally use not only one, but many programming languages; Some programming languages like Python have recently gained a lot of traction in the community and also Python distributions, like Anaconda, seem to find their way to data science aspirants.

Search & Data Management: this enormous category contains all tools that you can use to search and manage your data in some way. You’ll see, on the one hand, a search library like Lucene, but also a relational database management system like Oracle.

Machine Learning & Stats: this category not only offers you libraries to get started with machine learning and stats with programming languages such as Python, but also entire platforms, such as Alteryx or DataRobot.

Data Visualization & Reporting: after you have analyzed and modeled your data, you might be looking to visualize the results and report on what you have been investigating. You can make use of open-source options like Shiny or Matplotlib to do this, or all back on commercial options such as Qlikview or Tableau.

Collaboration: collaboration is a trending topic in the data science community. As you grow, you’ll also find the need to work in teams (even if it’s just with one other person!) and in those cases, you’ll want to make use of notebooks like Jupyter. But even as you’re just working on your own, working with an IDE can come in handy if you’re just starting out. In such cases, consider Rodeo or Spyder.

Community & Q&A: asking questions and falling back on the community is one of the things that you’ll probably do a lot when you’re learning data science. If you’re ever unsure of where you can find the answer to your data science question, you can be sure to find it in sites such as StackOverflow, Quora, Reddit, etc.

News, Newsletters & Blogs: you’ll find that the community is evolving and growing rapidly: following the news and the latest trends is a necessity. General newsletters like Data Science Weekly or Data Elixir, or language-specific newsletters like Python Weekly or R Weekly can give you your weekly dose of data science right in your mailbox. But also blogging sites like RBloggers or KD Nuggets are worth following!

Podcasts: last, but definitely not least, are the podcasts. These are great in many ways, as you’ll get introduced to expert interviews, like in Becoming A Data Scientist or to specific data science topics, like in Data Stories or Talking Machines!

Are you thinking of another resource that should be added to this periodic table?  Leave a comment below and tell us about it!

 

소스: The Periodic Table of Data Science | R-bloggers