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  • Building A Data Science Team

Building A Data Science Team ๊ฐ•์ขŒ

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''building a data science team'์— ๋Œ€ํ•œ ์ด 219๊ฐœ์˜ ๊ฒฐ๊ณผ ํ‘œ์‹œ ์ค‘

  • Placeholder
    Building a Data Science Team
    Johns Hopkins University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.5์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 3118 ๋ฆฌ๋ทฐ
    4.5(3,118)
    40,000๋ช…์˜ ํ•™์ƒ
    Mixed LevelMixed
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    Mastering Software Development in R
    Johns Hopkins University
    ํŠนํ™” ๊ณผ์ •
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.3์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 1340 ๋ฆฌ๋ทฐ
    4.3(1,340)
    64,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Data Science
    Johns Hopkins University
    ํŠนํ™” ๊ณผ์ •
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.5์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 45638 ๋ฆฌ๋ทฐ
    4.5(45,638)
    1.1๋ถ„๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Data Science: Statistics and Machine Learning
    Johns Hopkins University
    ํŠนํ™” ๊ณผ์ •
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.4์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 6635 ๋ฆฌ๋ทฐ
    4.4(6,635)
    270,000๋ช…์˜ ํ•™์ƒ
    Intermediate LevelIntermediate
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    Building and analyzing linear regression model in R
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.6์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 21 ๋ฆฌ๋ทฐ
    4.6(21)
    Beginner LevelBeginner
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    Executive Data Science
    Johns Hopkins University
    ํŠนํ™” ๊ณผ์ •
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.5์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 9710 ๋ฆฌ๋ทฐ
    4.5(9,710)
    210,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Practical Machine Learning
    Johns Hopkins University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.5์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 3107 ๋ฆฌ๋ทฐ
    4.5(3,107)
    140,000๋ช…์˜ ํ•™์ƒ
    Mixed LevelMixed
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    Using Shiny to Plot Differential Gene Expression
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.4์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 35 ๋ฆฌ๋ทฐ
    4.4(35)
    1.5,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Structuring Machine Learning Projects
    DeepLearning.AI
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarFilled Star
    5์  ๋งŒ์ ์— 4.8์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 45685 ๋ฆฌ๋ทฐ
    4.8(45,685)
    320,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Introduction to Probability and Data with R
    Duke University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.7์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 4726 ๋ฆฌ๋ทฐ
    4.7(4,726)
    220,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Building Data Visualization Tools
    Johns Hopkins University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarStar
    5์  ๋งŒ์ ์— 3.9์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 146 ๋ฆฌ๋ทฐ
    3.9(146)
    11,000๋ช…์˜ ํ•™์ƒ
    Intermediate LevelIntermediate
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    Building R Packages
    Johns Hopkins University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarStar
    5์  ๋งŒ์ ์— 4.1์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 205 ๋ฆฌ๋ทฐ
    4.1(205)
    9.3,000๋ช…์˜ ํ•™์ƒ
    Intermediate LevelIntermediate
  • Placeholder
    Build Random Forests in R with Azure ML Studio
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Filled StarFilled StarFilled StarFilled StarFilled Star
    5์  ๋งŒ์ ์— 4.8์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 51 ๋ฆฌ๋ทฐ
    4.8(51)
    4.4,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
  • Placeholder
    Build Data Analysis and Transformation Skills in R using DPLYR
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.7์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 81 ๋ฆฌ๋ทฐ
    4.7(81)
    2.9,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
  • Placeholder
    IBM Data Analyst Capstone Project
    IBM
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.6์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 75 ๋ฆฌ๋ทฐ
    4.6(75)
    3.6,000๋ช…์˜ ํ•™์ƒ
    Intermediate LevelIntermediate
  • Placeholder
    Building Recommendation System Using MXNET on AWS Sagemaker
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Advanced LevelAdvanced
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    Econometrics: Methods and Applications
    Erasmus University Rotterdam
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.6์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 1023 ๋ฆฌ๋ทฐ
    4.6(1,023)
    140,000๋ช…์˜ ํ•™์ƒ
    Mixed LevelMixed
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    Managing Data Analysis
    Johns Hopkins University
    ๊ฐ•์ขŒ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.6์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 3052 ๋ฆฌ๋ทฐ
    4.6(3,052)
    58,000๋ช…์˜ ํ•™์ƒ
    Mixed LevelMixed
  • Placeholder
    Reverse and complement nucleic acid sequences (DNA, RNA) using R
    Coursera Project Network

    ์‹ ๊ทœ

    ์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.5์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 44 ๋ฆฌ๋ทฐ
    4.5(44)
    1.7,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
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    Statistical Analysis with R for Public Health
    Imperial College London
    ํŠนํ™” ๊ณผ์ •
    Filled StarFilled StarFilled StarFilled StarHalf Filled Star
    5์  ๋งŒ์ ์— 4.7์ ์„ ๋ฐ›์•˜์Šต๋‹ˆ๋‹ค. 1188 ๋ฆฌ๋ทฐ
    4.7(1,188)
    32,000๋ช…์˜ ํ•™์ƒ
    Beginner LevelBeginner
1234โ€ฆ11Chevron Right

์š”์•ฝํ•˜์ž๋ฉด, ์—ฌ๊ธฐ์— ๊ฐ€์žฅ ์ธ๊ธฐ ์žˆ๋Š” building a data science team ๊ฐ•์ขŒ 10๊ฐœ๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค.

  • Building a Data Science Team:ย Johns Hopkins University
  • Mastering Software Development in R:ย Johns Hopkins University
  • Data Science:ย Johns Hopkins University
  • Data Science: Statistics and Machine Learning:ย Johns Hopkins University
  • Building and analyzing linear regression model in R:ย Coursera Project Network
  • Executive Data Science:ย Johns Hopkins University
  • Practical Machine Learning:ย Johns Hopkins University
  • Using Shiny to Plot Differential Gene Expression:ย Coursera Project Network
  • Structuring Machine Learning Projects:ย DeepLearning.AI
  • Introduction to Probability and Data with R:ย Duke University
์‚ดํŽด๋ณผ ๋งŒํ•œ ๋‹ค๋ฅธ ์ฃผ์ œ
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์˜ˆ์ˆ  & ์ธ๋ฌธํ•™
338๊ฐœ์˜ ๊ฐ•์ขŒ
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๋น„์ฆˆ๋‹ˆ์Šค
1095๊ฐœ์˜ ๊ฐ•์ขŒ
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์ปดํ“จํ„ฐ ๊ณตํ•™
668๊ฐœ์˜ ๊ฐ•์ขŒ
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๋ฐ์ดํ„ฐ ๊ณผํ•™
425๊ฐœ์˜ ๊ฐ•์ขŒ
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์ •๋ณด ๊ธฐ์ˆ 
145๊ฐœ์˜ ๊ฐ•์ขŒ
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๊ฑด๊ฐ•
471๊ฐœ์˜ ๊ฐ•์ขŒ
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์ˆ˜ํ•™ ๋ฐ ๋…ผ๋ฆฌ
70๊ฐœ์˜ ๊ฐ•์ขŒ
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์ž๊ธฐ๊ฐœ๋ฐœ
137๊ฐœ์˜ ๊ฐ•์ขŒ
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๋ฌผ๋ฆฌ ๊ณผํ•™ ๋ฐ ๊ณตํ•™
413๊ฐœ์˜ ๊ฐ•์ขŒ
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์‚ฌํšŒ ๊ณผํ•™
401๊ฐœ์˜ ๊ฐ•์ขŒ
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์–ธ์–ด ํ•™์Šต
150๊ฐœ์˜ ๊ฐ•์ขŒ

Coursera๊ฐ€ ์ œ๊ณตํ•˜๋Š” ํ˜œํƒ

ํ•™์Šต ํ”„๋กœ๊ทธ๋žจ์„ค๋ช…
์•ˆ๋‚ด ํ”„๋กœ์ ํŠธ

์ฃผ์ œ ์ „๋ฌธ๊ฐ€์˜ ์•ˆ๋‚ด์— ๋”ฐ๋ผ ๋Œ€ํ™”์‹ ๊ฒฝํ—˜์„ ํ†ตํ•ด 2์‹œ๊ฐ„ ์•ˆ์— ์Šต๋“ํ•˜์—ฌ ์˜ค๋Š˜ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์ง๋ฌด ๊ด€๋ จ ๊ธฐ์ˆ ์„ ๋ฐฐ์›Œ๋ณด์„ธ์š”. ๋ธŒ๋ผ์šฐ์ €์—์„œ ๋ฐ”๋กœ ํ•„์š”ํ•œ ๋ชจ๋“  ๊ฒƒ์— ์•ก์„ธ์Šคํ•˜๊ณ  ๋‹จ๊ณ„๋ณ„ ์ง€์นจ์œผ๋กœ ํ”„๋กœ์ ํŠธ๋ฅผ ์ž์‹  ์žˆ๊ฒŒ ์™„๋ฃŒํ•˜์„ธ์š”.

๊ฐ•์ขŒ

์ „ ์„ธ๊ณ„ ์ตœ๊ณ ์˜ ๊ฐ•์‚ฌ์™€ ๋Œ€ํ•™์˜ ๊ฐ•์ขŒ๋ฅผ ์ˆ˜๊ฐ•ํ•˜์‹ญ์‹œ์˜ค. ๊ฐ•์ขŒ์—๋Š” ์ž๋™ ๋“ฑ๊ธ‰ ๊ธฐ๋ก ๋ฐ ๋™๋ฃŒ ํ‰๊ฐ€ ๊ณผ์ œ, ๋™์˜์ƒ ๊ฐ•์˜ ๋ฐ ์ปค๋ฎค๋‹ˆํ‹ฐ ํ† ๋ก  ํฌ๋Ÿผ์ด ํฌํ•จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. ๊ฐ•์ขŒ๋ฅผ ์™„๋ฃŒํ•˜๋ฉด ์•ฝ๊ฐ„์˜ ์ˆ˜์ˆ˜๋ฃŒ๋ฅผ ์ง€๋ถˆํ•˜๊ณ  ๊ณต์œ ๊ฐ€ ๊ฐ€๋Šฅํ•œ ์ „์ž ๊ฐ•์ขŒ ์ˆ˜๋ฃŒ์ฆ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

ํŠนํ™” ๊ณผ์ •

ํŠนํ™” ๊ณผ์ •์— ๋“ฑ๋กํ•˜์—ฌ ํŠน์ • ๊ฒฝ๋ ฅ ๊ธฐ์ˆ ์„ ์™„๋ฒฝํ•˜๊ฒŒ ์ตํ˜€ ๋ณด์„ธ์š”. ์ˆ˜์ค€ ๋†’์€ ์ผ๋ จ์˜ ๊ฐ•์ขŒ๋ฅผ ์ˆ˜๋ฃŒํ•˜๊ณ  ์‹ค์Šต ํ”„๋กœ์ ํŠธ๋ฅผ ์ง„ํ–‰ํ•˜์—ฌ ํŠนํ™” ๊ณผ์ • ์ˆ˜๋ฃŒ์ฆ์„ ์ทจ๋“ํ•˜๋ฉด ์—…๊ณ„ ๋„คํŠธ์›Œํฌ ๋ฐ ์ง€์›ํ•˜๋Š” ํšŒ์‚ฌ์™€ ๊ณต์œ ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค.

์ „๋ฌธ๊ฐ€ ์ˆ˜๋ฃŒ์ฆ

์ƒˆ๋กœ์šด ๋ถ„์•ผ์—์„œ ๊ฒฝ๋ ฅ์„ ์ฐพ๊ฑฐ๋‚˜ ํ˜„์žฌ ๊ฒฝ๋ ฅ์— ๋ณ€ํ™”๋ฅผ ์ฃผ๊ณ  ์‹ถ๋‹ค๋ฉด, Coursera์˜ ์ „๋ฌธ ์ž๊ฒฉ์ฆ์„ ์ทจ๋“ํ•˜์—ฌ ์ค€๋น„๋œ ์ธ์žฌ๋กœ ๊ฑฐ๋“ญ๋‚  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ตœ๊ณ ์˜ ๊ธฐ์—…๊ณผ ๋Œ€ํ•™์—์„œ ์ž์‹ ์˜ ์†๋„์— ๋งž๊ฒŒ ํ•™์Šตํ•˜๊ณ , ์ž ์žฌ์  ๊ณ ์šฉ ํšŒ์‚ฌ์— ์—ฌ๋Ÿฌ๋ถ„์ด ์ „๋ฌธ๊ฐ€์ž„์„ ์ž…์ฆํ•ด์ฃผ๋Š” ์‹ค์Šต ํ”„๋กœ์ ํŠธ์—์„œ ์ƒˆ๋กœ ์Šต๋“ํ•œ ๋Šฅ๋ ฅ์„ ๋ฐœํœ˜ํ•˜๋ฉฐ, ๊ฒฝ๋ ฅ ์ฆ๋ช…์„ ์ทจ๋“ํ•˜์—ฌ ์ƒˆ๋กœ์šด ์ปค๋ฆฌ์–ด๋ฅผ ์Œ“์œผ์„ธ์š”.

MASTERTRACKโ„ข ์ˆ˜๋ฃŒ์ฆ

MasterTrackโ„ข ์ˆ˜๋ฃŒ์ฆ๊ณผ ๋”๋ถˆ์–ด ์„์‚ฌ ํ”„๋กœ๊ทธ๋žจ์˜ ์ผ๋ถ€๊ฐ€ ์˜จ๋ผ์ธ ๋ชจ๋“ˆ๋กœ ๋ถ„๋ฆฌ๋˜์—ˆ์œผ๋ฏ€๋กœ, ์œ ์—ฐํ•œ ๋Œ€ํ™”ํ˜• ํฌ๋งท์„ ํ†ตํ•ด ํ˜์‹ ์ ์ธ ๊ฐ€๊ฒฉ์œผ๋กœ ๋Œ€ํ•™์—์„œ ๋ฐœ๊ธ‰ํ•˜๋Š” ์ˆ˜์ค€ ๋†’์€ ๊ฒฝ๋ ฅ ์ž๊ฒฉ ์ฆ๋ช…์„ ์ทจ๋“ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์‹ค์ œ ํ”„๋กœ์ ํŠธ์™€ ์‹ค๋ฌด ์ „๋ฌธ๊ฐ€์˜ ๊ฐ•์˜๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ๋ชฐ์ž…๋„ ๋†’์€ ํ•™์Šต ํ™˜๊ฒฝ์˜ ์ด์ ์„ ๋ˆ„๋ฆฌ์„ธ์š”. ์ „์ฒด ์„์‚ฌ ๊ณผ์ •์„ ๋ฐŸ๋Š” ๊ฒฝ์šฐ ์ทจ๋“ํ•œ ํ•™์œ„์— ๋Œ€ํ•œ MasterTrack ์ˆ˜๊ฐ• ํ™œ๋™์ด ์ธ์ •๋ฉ๋‹ˆ๋‹ค.

ํ•™์œ„

ํ•ฉ๋ฆฌ์ ์ธ ๊ฐ€๊ฒฉ์œผ๋กœ ์ƒ์œ„๊ถŒ ๋Œ€ํ•™ ํ•™์œ„๋ฅผ ๋ณด์œ ํ•œ ์ด๋ ฅ์„œ๋ฅผ ์ž‘์„ฑํ•ด ๋ณด์„ธ์š”. ๋‹น์‚ฌ์˜ ๋ชจ๋“ˆ์‹ ํ•™์œ„ ํ•™์Šต ํ™˜๊ฒฝ์„ ํ†ตํ•ด ์–ธ์ œ๋“ ์ง€ ์˜จ๋ผ์ธ์—์„œ ํ•™์Šตํ•˜๊ณ  ๊ฐ•์ขŒ ๊ณผ์ œ๋ฅผ ์™„๋ฃŒํ•˜๋ฉด ํ•™์ ์„ ๋ฐ›์„ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ต์‹ค์—์„œ ์ˆ˜์—…์„ ๋ฐ›์€ ํ•™์ƒ๊ณผ ๋™์ผํ•œ ์ž๊ฒฉ ์ฆ๋ช…์„ ๋ฐ›๊ฒŒ ๋ฉ๋‹ˆ๋‹ค. Coursera ํ•™์  ์ทจ๋“ ๋น„์šฉ์€ ์บ ํผ์Šค ํ”„๋กœ๊ทธ๋žจ๊ณผ ๋น„๊ตํ•ด ๋งค์šฐ ์ €๋ ดํ•ฉ๋‹ˆ๋‹ค.

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์ธ๊ธฐ ์˜จ๋ผ์ธ ๊ฐ•์ขŒ

  • ์ธ์ƒ์˜ ๋ชฉ์ ๊ณผ ์˜๋ฏธ ์ฐพ๊ธฐ
  • ์˜๋ฃŒ ์—ฐ๊ตฌ์˜ ์ดํ•ด
  • ์ดˆ๊ธ‰์ž๋ฅผ ์œ„ํ•œ ์ผ๋ณธ์–ด
  • ํด๋ผ์šฐ๋“œ ์ปดํ“จํŒ… ์ž…๋ฌธ
  • ๋ช…์ƒ์˜ ๊ธฐ์ดˆ
  • ์žฌ๋ฌด์˜ ๊ธฐ๋ณธ
  • ๊ธฐ๊ณ„ ํ•™์Šต
  • Sas Viya๋ฅผ ์‚ฌ์šฉํ•˜๋Š” ๋จธ์‹  ๋Ÿฌ๋‹
  • ์›ฐ๋น™์˜ ๊ณผํ•™
  • ์ฝ”๋กœ๋‚˜19 ์ ‘์ด‰์ž ์ถ”์ 
  • ๋ชจ๋‘๋ฅผ ์œ„ํ•œ AI
  • ๊ธˆ์œต ์‹œ์žฅ
  • ์‹ฌ๋ฆฌํ•™ ์ž…๋ฌธ
  • AWS ์‹œ์ž‘ํ•˜๊ธฐ
  • ๊ตญ์ œ ๋งˆ์ผ€ํŒ…
  • C++
  • ์˜ˆ์ธก ๋ถ„์„ ๋ฐ ๋ฐ์ดํ„ฐ ๋งˆ์ด๋‹
  • UCSD ํ•™์Šต์˜ ๋ฐฉ๋ฒ• ๋ฐฐ์šฐ๊ธฐ
  • ๋ฏธ์‹œ๊ฐ„ ๋Œ€ํ•™ ๋ชจ๋‘๋ฅผ ์œ„ํ•œ ํ”„๋กœ๊ทธ๋ž˜๋ฐ
  • JHU R ํ”„๋กœ๊ทธ๋ž˜๋ฐ
  • Google CBRS CPI ๊ต์œก

์ธ๊ธฐ ์˜จ๋ผ์ธ ์ „๋ฌธ ๋ถ„์•ผ

  • ์ž์—ฐ ์–ธ์–ด ์ฒ˜๋ฆฌ(NLP)
  • ์˜ํ•™ ๋ถ„์•ผ AI
  • ์–ธ์–ด ๊ธฐ์ˆ  ํ–ฅ์ƒํ•˜๊ธฐ: ๊ธ€์“ฐ๊ธฐ ๋ฐ ํŽธ์ง‘
  • ์ „์—ผ๋ณ‘ ๋ชจ๋ธ๋ง
  • ๋ฏธ๊ตญ์‹ ์˜์–ด์˜ ๋ฐœ์Œ
  • ์†Œํ”„ํŠธ์›จ์–ด ํ…Œ์ŠคํŠธ ์ž๋™ํ™”
  • ์‹ฌ์ธต ํ•™์Šต
  • ๋ชจ๋‘๋ฅผ ์œ„ํ•œ Python
  • ๋ฐ์ดํ„ฐ ๊ณผํ•™
  • ๋น„์ฆˆ๋‹ˆ์Šค ๊ธฐ๋ณธ
  • ๋น„์ฆˆ๋‹ˆ์Šค์šฉ Excel ๊ธฐ์ˆ 
  • Python๊ณผ ํ•จ๊ป˜ํ•˜๋Š” ๋ฐ์ดํ„ฐ ๊ณผํ•™
  • ๋ชจ๋‘๋ฅผ ์œ„ํ•œ ์žฌ๋ฌด
  • ์—”์ง€๋‹ˆ์–ด๋ฅผ ์œ„ํ•œ ์ปค๋ฎค๋‹ˆ์ผ€์ด์…˜ ๊ธฐ์ˆ 
  • ์˜์—… ๊ต์œก
  • ๊ฒฝ๋ ฅ ๋ธŒ๋žœ๋“œ ๊ด€๋ฆฌ
  • ์™€ํŠผ ์Šค์ฟจ ๋น„์ฆˆ๋‹ˆ์Šค ๋ถ„์„
  • ํŽœ์‹ค๋ฒ ์ด๋‹ˆ์•„ ๋Œ€ํ•™ ๊ธ์ •์˜ ์‹ฌ๋ฆฌํ•™
  • ์›Œ์‹ฑํ„ด ๋Œ€ํ•™ ๊ธฐ๊ณ„ ํ•™์Šต
  • CalArts ๊ทธ๋ž˜ํ”ฝ ๋””์ž์ธ

์˜จ๋ผ์ธ ์ˆ˜๋ฃŒ์ฆ

  • ์ „๋ฌธ ์ž๊ฒฉ์ฆ
  • MasterTrack ์ˆ˜๋ฃŒ์ฆ
  • Google IT ์ง€์›
  • IBM ๋ฐ์ดํ„ฐ ๊ณผํ•™
  • Google ํด๋ผ์šฐ๋“œ ๋ฐ์ดํ„ฐ ์—”์ง€๋‹ˆ์–ด๋ง
  • IBM ์‘์šฉ AI
  • Google ํด๋ผ์šฐ๋“œ ์•„ํ‚คํ…์ฒ˜
  • IBM ์‚ฌ์ด๋ฒ„ ๋ณด์•ˆ ๋ถ„์„๊ฐ€
  • Python๊ณผ ํ•จ๊ป˜ํ•˜๋Š” Google IT ์ž๋™ํ™”
  • IBM z/OS ๋ฉ”์ธํ”„๋ ˆ์ž„ ์‹ค๋ฌด์ž
  • UCI ์‘์šฉ ํ”„๋กœ์ ํŠธ ๊ด€๋ฆฌ
  • ๊ต์ˆ˜ ์„ค๊ณ„ ์ˆ˜๋ฃŒ์ฆ
  • ๊ฑด์„ค ๊ณตํ•™ ๋ฐ ๊ด€๋ฆฌ ์ˆ˜๋ฃŒ์ฆ
  • ๋น… ๋ฐ์ดํ„ฐ ์ˆ˜๋ฃŒ์ฆ
  • ๋ถ„์„์šฉ ๊ธฐ๊ณ„ ํ•™์Šต ์ˆ˜๋ฃŒ์ฆ
  • ํ˜์‹  ๊ด€๋ฆฌ ๋ฐ ๊ธฐ์—…๊ฐ€ ์ •์‹  ์ˆ˜๋ฃŒ์ฆ
  • ์ง€์† ๊ฐ€๋Šฅ์„ฑ ๋ฐ ๊ฐœ๋ฐœ ์ˆ˜๋ฃŒ์ฆ
  • ์‚ฌํšŒ ์‚ฌ์—… ์ˆ˜๋ฃŒ์ฆ
  • AI ๋ฐ ๊ธฐ๊ณ„ ํ•™์Šต ์ˆ˜๋ฃŒ์ฆ
  • ๊ณต๊ฐ„ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๋ฐ ์‹œ๊ฐํ™” ์ˆ˜๋ฃŒ์ฆ

์˜จ๋ผ์ธ ํ•™์œ„ ํ”„๋กœ๊ทธ๋žจ

  • ์ปดํ“จํ„ฐ ๊ณตํ•™ ํ•™์œ„
  • ๊ฒฝ์˜ํ•™ ํ•™์œ„
  • ๊ณต์ค‘ ๋ณด๊ฑด ํ•™์œ„
  • ๋ฐ์ดํ„ฐ ๊ณผํ•™ ํ•™์œ„
  • ํ•™์‚ฌ ํ•™์œ„
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  • MS ์ „๊ธฐ ๊ณตํ•™
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  • MPH
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  • MCIT
  • MBA ์˜จ๋ผ์ธ
  • ์‘์šฉ ๋ฐ์ดํ„ฐ ๊ณผํ•™ ์„์‚ฌ
  • ๊ตญ์ œ MBA
  • ๊ธฐ์ˆ  ํ˜์‹ ๊ณผ ๊ธฐ์—…๊ฐ€ ์ •์‹  ์„์‚ฌ
  • MCS ๋ฐ์ดํ„ฐ ๊ณผํ•™
  • ์ปดํ“จํ„ฐ ๊ณตํ•™ ์„์‚ฌ
  • ๊ณต์ค‘ ๋ณด๊ฑดํ•™ ์„์‚ฌ

Coursera

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  • ๋ฆฌ๋”์‹ญ
  • ์ง์—…
  • ์นดํƒˆ๋กœ๊ทธ
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  • MasterTrackโ„ข ์ˆ˜๋ฃŒ์ฆ
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์ปค๋ฎค๋‹ˆํ‹ฐ

  • ํ•™์Šต์ž
  • ํŒŒํŠธ๋„ˆ
  • ๊ฐœ๋ฐœ์ž
  • ๋ฒ ํƒ€ ํ…Œ์Šคํ„ฐ
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  • ๋ธ”๋กœ๊ทธ
  • ๊ธฐ์ˆ  ๋ธ”๋กœ๊ทธ
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๊ธฐํƒ€

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  • ๋„์›€๋ง
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  • ๋ณด๋„ ์ž๋ฃŒ
  • ๋ฌธ์˜ํ•˜๊ธฐ
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  • ๋””๋ ‰ํ† ๋ฆฌ
  • ๊ณ„์—ด์‚ฌ
์–ด๋””์—์„œ๋‚˜ ํ•™์Šต
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