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===위키데이터===
 
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* ID :  [https://www.wikidata.org/wiki/Q2374463 Q2374463]
 
* ID :  [https://www.wikidata.org/wiki/Q2374463 Q2374463]
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===Spacy 패턴 목록===
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* [{'LOWER': 'data'}, {'LEMMA': 'science'}]
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* [{'LOWER': 'data'}, {'OP': '*'}, {'LOWER': 'driven'}, {'LEMMA': 'science'}]

2021년 2월 16일 (화) 23:42 기준 최신판

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말뭉치

  1. What Can Data Science be Used for?[1]
  2. Data science has led to a number of breakthroughs in the healthcare industry.[1]
  3. UPS turns to data science to maximize efficiency, both internally and along its delivery routes.[1]
  4. Using data science, the music streaming giant can carefully curate lists of songs based off the music genre or band you’re currently into.[1]
  5. We offer five unique programs to support your career goals in the data science field.[2]
  6. C.F. Jeff Wu used the term Data Science for the first time as an alternative name for statistics.[3]
  7. C.F. Jeff Wu again suggested that statistics should be renamed data science.[3]
  8. "Data science" became more widely used in the next few years: in 2002, the Committee on Data for Science and Technology launched Data Science Journal.[3]
  9. There are a variety of different technologies and techniques that are used for data science which depend on the application.[3]
  10. All the ideas which you see in Hollywood sci-fi movies can actually turn into reality by Data Science.[4]
  11. By the end of this blog, you will be able to understand what is Data Science and its role in extracting meaningful insights from the complex and large sets of data all around us.[4]
  12. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.[4]
  13. Let’s see how the proportion of above-described approaches differ for Data Analysis as well as Data Science.[4]
  14. Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals.[5]
  15. Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries.[5]
  16. Gaining specialized skills within the data science field can distinguish data scientists even further.[5]
  17. You also may want to consider a company where there’s room for growth since your first data science job may not have the title data scientist, but could be more of an analytical role.[6]
  18. When it comes to most data science jobs, is a master’s required?[6]
  19. It depends on the job and some working data scientists have a bachelor’s or have graduated from a data science bootcamp.[6]
  20. Every company will have a different take on data science job tasks.[6]
  21. Data science is an essential part of any industry today, given the massive amounts of data that are produced.[7]
  22. Its popularity has grown over the years, and companies have started implementing data science techniques to grow their business and increase customer satisfaction.[7]
  23. Are you considering a profession in the field of Data Science?[7]
  24. Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.[7]
  25. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.[8]
  26. More and more companies are coming to realize the importance of data science, AI, and machine learning.[8]
  27. Ramping up data science efforts is difficult even for companies with near-unlimited resources.[8]
  28. Abstract Data science has attracted a lot of attention, promising to turn vast amounts of data into useful predictions and insights.[9]
  29. In this article, we ask why scientists should care about data science.[9]
  30. To answer, we discuss data science from three perspectives: statistical, computational, and human.[9]
  31. The term “data science” has attracted a lot of attention.[9]
  32. Common job titles of Lambda School data science graduates include data analyst, data scientist, and machine learning engineer.[10]
  33. Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning.[11]
  34. While closely related, data analytics is a component of data science, used to understand what an organization’s data looks like.[11]
  35. "Data science is coming to conclusions that drive your data forward," says Adam Hunt, CTO at RiskIQ.[11]
  36. Data science and big data are often viewed in concert, but data science can be used to extract value from data of all sizes, whether structured, unstructured, or semi-structured.[11]
  37. There are no deadlines, so you can learn data science online at your own pace.[12]
  38. Data science provides meaningful information based on large amounts of complex data or big data.[13]
  39. The term data science has existed for the better part of the last 30 years and was originally used as a substitute for "computer science" in 1960.[13]
  40. In 2001, data science was introduced as an independent discipline.[13]
  41. Companies are applying big data and data science to everyday activities to bring value to consumers.[13]
  42. The U.S. Bureau of Labor Statistics reports that demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026.[14]
  43. This MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS).[14]
  44. It is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality.[14]
  45. Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies.[15]
  46. The main advantage of enlisting data science in an organization is the empowerment and facilitation of decision-making.[15]
  47. In customer-facing organizations, data science helps identify and refine target audiences.[15]
  48. The specific benefits of data science vary depending on the company's goal and the industry.[15]
  49. If this list doesn’t cover your needs, you can find more options in the Kotlin Data Science Resources digest from Thomas Nield.[16]
  50. Please consult the scope of the Data Science Journal and the descriptions of the categories of article.[17]
  51. The Problem Companies responded to the analytics boom by hiring the best data scientists they could find—but many of them haven’t gotten the value they expected from their data science initiatives.[18]
  52. The Solution A good data science team needs six talents: project management, data wrangling, data analysis, subject expertise, design, and storytelling.[18]
  53. Data science is growing up fast.[18]
  54. But despite the success stories, many companies aren’t getting the value they could from data science.[18]
  55. We develop our materials to help you take your interest in data science and develop it into a career opportunity, even without relevant background or prior experience.[19]
  56. Learn from a neatly structured, all-around program and acquire the key skills necessary to become a data science expert.[19]
  57. Data science combines computer science and statistics to solve exciting data-intensive problems in industry and many fields of science.[20]
  58. You can specialise either in the core areas of data science -- machine learning and algorithms, infrastructure and statistics -- or in its applications.[20]
  59. Curious about what you’d learn in UW Data Science courses?[21]
  60. This credential is valuable on its own – or you can combine it with Data Curation to qualify for a data science certification.[22]
  61. The process of data science starts with understanding the problem that the business user is trying to solve.[23]
  62. In addition to deploying models to dashboards and production systems, data scientists may also create sophisticated data science pipelines that can be invoked from a visualization or dashboard tool.[23]

소스

  1. ↑ 1.0 1.1 1.2 1.3 What is Data Science? A Complete Guide.
  2. ↑ Data Science Online Courses & Programs
  3. ↑ 3.0 3.1 3.2 3.3 Data science
  4. ↑ 4.0 4.1 4.2 4.3 What Is Data Science? A Beginner’s Guide To Data Science
  5. ↑ 5.0 5.1 5.2 What is Data Science?
  6. ↑ 6.0 6.1 6.2 6.3 What is a Data Scientist
  7. ↑ 7.0 7.1 7.2 7.3 What is Data Science: A Comprehensive Guide for Beginners
  8. ↑ 8.0 8.1 8.2 Learn Why Data Science Is Important
  9. ↑ 9.0 9.1 9.2 9.3 Science and data science
  10. ↑ Lambda School
  11. ↑ 11.0 11.1 11.2 11.3 What is data science? Transforming data into value
  12. ↑ Learn R, Python and SQL for Data Science
  13. ↑ 13.0 13.1 13.2 13.3 Data Science Definition
  14. ↑ 14.0 14.1 14.2 Statistics and Data Science MicroMasters
  15. ↑ 15.0 15.1 15.2 15.3 Definition from WhatIs.com
  16. ↑ Kotlin for Data Science
  17. ↑ Data Science Journal
  18. ↑ 18.0 18.1 18.2 18.3 Data Science and the Art of Persuasion
  19. ↑ 19.0 19.1 Learn Data Science with our Training Programs
  20. ↑ 20.0 20.1 Data Science │ Master’s programme
  21. ↑ What Do Data Scientists Do?
  22. ↑ SAS Academy for Data Science
  23. ↑ 23.0 23.1 What is Data Science?

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  • [{'LOWER': 'data'}, {'OP': '*'}, {'LOWER': 'driven'}, {'LEMMA': 'science'}]
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