JMP์šฉ ํ™•์žฅ ๊ฒ€์ƒ‰

์ถ”์ฒœ

์‹คํ—˜์ž์— ๋Œ€ํ•œ ๊ทธ๋ž˜ํ”„. ์Œ“์ธ ๋ฐ์ดํ„ฐ ํ˜•์‹์˜ ํ…œํ”Œ๋ฆฟ ํ…Œ์ด๋ธ”์ด ์žˆ๋Š” ์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๊ณตํ†ต ๊ทธ๋ž˜ํ”„ ๋ฐ ๊ด€๋ จ ๋ถ„์„. ๋ฒ”์ฃผ: Data Visualization, Data Analysis. JMP์—์„œ ์ œ์ž‘. 377 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์‹คํ—˜์ž์— ๋Œ€ํ•œ ๊ทธ๋ž˜ํ”„

์Œ“์ธ ๋ฐ์ดํ„ฐ ํ˜•์‹์˜ ํ…œํ”Œ๋ฆฟ ํ…Œ์ด๋ธ”์ด ์žˆ๋Š” ์ƒ˜ํ”Œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•œ ๊ณตํ†ต ๊ทธ๋ž˜ํ”„ ๋ฐ ๊ด€๋ จ ๋ถ„์„

๋ฐ์ดํ„ฐ ๋ถ„์„, ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

377 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro์šฉ Torch Deep Learning. ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” PyTorch ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ์ฝ”๋“œ ์—†๋Š” ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Machine Learning. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 4.83 / 5. 811 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro์šฉ Torch Deep Learning

์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” PyTorch ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ์ฝ”๋“œ ์—†๋Š” ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค.

๋จธ์‹  ๋Ÿฌ๋‹

811 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JSL-Hamcrest. JSL ๋กœ ํ‘œํ˜„ํ˜• ํ…Œ์ŠคํŠธ๋ฅผ ์ž‘์„ฑํ•˜๊ธฐ ์œ„ํ•œ ํ”„๋ ˆ์ž„์›Œํฌ. ๋ฒ”์ฃผ: Utility. JMP์—์„œ ์ œ์ž‘. 48 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JSL-Hamcrest

JSL ๋กœ ํ‘œํ˜„ํ˜• ํ…Œ์ŠคํŠธ๋ฅผ ์ž‘์„ฑํ•˜๊ธฐ ์œ„ํ•œ ํ”„๋ ˆ์ž„์›Œํฌ

์œ ํ‹ธ๋ฆฌํ‹ฐ

48 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

S3 ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ. Amazon S3์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ(JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ). ๋ฒ”์ฃผ: Data Connectors. JMP์—์„œ ์ œ์ž‘. 65 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

S3 ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

Amazon S3์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ(JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ)

๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

65 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ตœ๋‹ค ๋‹ค์šด๋กœ๋“œ

ํ•™์ƒ ๋ฉ”๋‰ด. ํ•ต์‹ฌ ํ†ต๊ณ„ ๊ฐœ๋…์„ ๊ต์œกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋Œ€ํ™”์‹ ์• ํ”Œ๋ฆฟ์ด ํฌํ•จ๋œ ํ•™์ƒ ๋ฉ”๋‰ด์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility. Industries: Academic. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 2817 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํ•™์ƒ ๋ฉ”๋‰ด

ํ•ต์‹ฌ ํ†ต๊ณ„ ๊ฐœ๋…์„ ๊ต์œกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋Œ€ํ™”์‹ ์• ํ”Œ๋ฆฟ์ด ํฌํ•จ๋œ ํ•™์ƒ ๋ฉ”๋‰ด์ž…๋‹ˆ๋‹ค

์œ ํ‹ธ๋ฆฌํ‹ฐ

2.8k ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋Ÿฐ๋ด‡. ์‚ฌ์šฉ์ž๊ฐ€ JMPยฎ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ด‡์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Documentation, Utility. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 1839 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋Ÿฐ๋ด‡

์‚ฌ์šฉ์ž๊ฐ€ JMPยฎ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ด‡์ž…๋‹ˆ๋‹ค

์„ค๋ช…์„œ, ์œ ํ‹ธ๋ฆฌํ‹ฐ

1.8k ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๊ณต์ • ๋Šฅ๋ ฅ ํƒ์ƒ‰๊ธฐ. ํ”„๋กœ์„ธ์Šค ์—ญ๋Ÿ‰์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•œ ์ƒํ˜ธ ์ž‘์šฉ์ ์ด๊ณ  ๊ฐ„์†Œํ™”๋œ ์ ‘๊ทผ ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Visualization. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 1020 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๊ณต์ • ๋Šฅ๋ ฅ ํƒ์ƒ‰๊ธฐ

ํ”„๋กœ์„ธ์Šค ์—ญ๋Ÿ‰์„ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•œ ์ƒํ˜ธ ์ž‘์šฉ์ ์ด๊ณ  ๊ฐ„์†Œํ™”๋œ ์ ‘๊ทผ ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

1k ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro์šฉ Torch Deep Learning. ์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” PyTorch ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ์ฝ”๋“œ ์—†๋Š” ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Machine Learning. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 4.83 / 5. 811 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro์šฉ Torch Deep Learning

์‹ฌ์ธต ์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•œ ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” PyTorch ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ์ฝ”๋“œ ์—†๋Š” ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค.

๋จธ์‹  ๋Ÿฌ๋‹

811 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

ํ˜„๋Œ€ ์‹์Šค ์‹œ๊ทธ๋งˆ. JMPยฎ ๋‚ด์—์„œ Six Sigma DMAIC(์ •์˜, ์ธก์ •, ๋ถ„์„, ๊ฐœ์„ , ์ œ์–ด) ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐ„์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Six Sigma. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 4 / 5. 620 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํ˜„๋Œ€ ์‹์Šค ์‹œ๊ทธ๋งˆ

JMPยฎ ๋‚ด์—์„œ Six Sigma DMAIC(์ •์˜, ์ธก์ •, ๋ถ„์„, ๊ฐœ์„ , ์ œ์–ด) ํ”„๋กœ์„ธ์Šค๋ฅผ ๊ฐ„์†Œํ™”ํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค.

Six Sigma

620 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋ฐ์ดํ„ฐ ๋ถ„์„ ๋””๋ ‰ํ„ฐ. JMPยฎ์—์„œ ๋‹ค์–‘ํ•œ ๋ถ„์„์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•˜๋Š” ์ƒˆ๋กœ์šด ์‚ฌ์šฉ์ž๋ฅผ ์œ„ํ•œ ๊ฐ€์ด๋“œ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Six Sigma, Data Analysis, Documentation. Industries: Academic. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 603 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋ฐ์ดํ„ฐ ๋ถ„์„ ๋””๋ ‰ํ„ฐ

JMPยฎ์—์„œ ๋‹ค์–‘ํ•œ ๋ถ„์„์„ ์‚ฌ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•˜๋Š” ์ƒˆ๋กœ์šด ์‚ฌ์šฉ์ž๋ฅผ ์œ„ํ•œ ๊ฐ€์ด๋“œ์ž…๋‹ˆ๋‹ค

Six Sigma, ๋ฐ์ดํ„ฐ ๋ถ„์„, ์„ค๋ช…์„œ

603 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro์šฉ XGBoost. ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” XGBoost ์˜คํ”ˆ ์†Œ์Šค ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ํฌ์ธํ„ฐ ํด๋ฆญ ์ธํ„ฐํŽ˜์ด์Šค. ๋ฒ”์ฃผ: Machine Learning, Data Science. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 532 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro์šฉ XGBoost

์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” XGBoost ์˜คํ”ˆ ์†Œ์Šค ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ํฌ์ธํ„ฐ ํด๋ฆญ ์ธํ„ฐํŽ˜์ด์Šค

๋ฐ์ดํ„ฐ ๊ณผํ•™, ๋จธ์‹  ๋Ÿฌ๋‹

532 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ผ๋ณธ์–ด ์„ค๋ช…์„œ. ๋ชจ๋“  JMP ์„ค๋ช…์„œ ๋ฐ ์„ค๋ช…์„œ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์šฉ PDF ํŒŒ์ผ. ๋ฒ”์ฃผ: Documentation. JMP์—์„œ ์ œ์ž‘. 531 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์ผ๋ณธ์–ด ์„ค๋ช…์„œ

๋ชจ๋“  JMP ์„ค๋ช…์„œ ๋ฐ ์„ค๋ช…์„œ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์šฉ PDF ํŒŒ์ผ

์„ค๋ช…์„œ

531 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๊ทธ๋ž˜ํ”„ ๋นŒ๋” ๋„๊ตฌ ๋ชจ์Œ. ๊ทธ๋ž˜ํ”„ ๋นŒ๋” ๋ฐ ๊ธฐํƒ€ ๋ณด๊ณ ์„œ์— ์œ ์šฉํ•œ ๋„์›€๋ง ํ•จ์ˆ˜๊ฐ€ ํฌํ•จ๋œ ๋„๊ตฌ ์ƒ์ž. ๋ฒ”์ฃผ: Data Visualization. ํ‰์ : 5 / 5. 477 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๊ทธ๋ž˜ํ”„ ๋นŒ๋” ๋„๊ตฌ ๋ชจ์Œ

๊ทธ๋ž˜ํ”„ ๋นŒ๋” ๋ฐ ๊ธฐํƒ€ ๋ณด๊ณ ์„œ์— ์œ ์šฉํ•œ ๋„์›€๋ง ํ•จ์ˆ˜๊ฐ€ ํฌํ•จ๋œ ๋„๊ตฌ ์ƒ์ž

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

477 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ถ”๊ฐ€๊ธฐ๋Šฅ ๊ด€๋ฆฌ์ž. ์ถ”๊ฐ€๊ธฐ๋Šฅ ํŒŒ์ผ์„ ์‰ฝ๊ฒŒ ๋นŒ๋“œ ๋ฐ ์‚ฌ์šฉ์ž ์ •์˜ํ•˜๊ณ , ์ถ”๊ฐ€๊ธฐ๋Šฅ ๋ฒ„์ „์„ ๊ด€๋ฆฌํ•˜๊ณ , ์†Œ์Šค ํŒŒ์ผ์— ์•ก์„ธ์Šคํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 404 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์ถ”๊ฐ€๊ธฐ๋Šฅ ๊ด€๋ฆฌ์ž

์ถ”๊ฐ€๊ธฐ๋Šฅ ํŒŒ์ผ์„ ์‰ฝ๊ฒŒ ๋นŒ๋“œ ๋ฐ ์‚ฌ์šฉ์ž ์ •์˜ํ•˜๊ณ , ์ถ”๊ฐ€๊ธฐ๋Šฅ ๋ฒ„์ „์„ ๊ด€๋ฆฌํ•˜๊ณ , ์†Œ์Šค ํŒŒ์ผ์— ์•ก์„ธ์Šคํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค

์œ ํ‹ธ๋ฆฌํ‹ฐ

404 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

ํฌ๋ ˆ์ŠคํŠธ ๊ทธ๋ฆผ. ๊ทธ๋ž˜ํ”„ ๋นŒ๋”๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํฌ๋ ˆ์ŠคํŠธ ๊ทธ๋ฆผ์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ. ๋ฒ”์ฃผ: Data Visualization. JMP์—์„œ ์ œ์ž‘. 403 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํฌ๋ ˆ์ŠคํŠธ ๊ทธ๋ฆผ

๊ทธ๋ž˜ํ”„ ๋นŒ๋”๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํฌ๋ ˆ์ŠคํŠธ ๊ทธ๋ฆผ์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

403 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

ํšจ๊ณผ ํฌ๊ธฐ ๊ณ„์‚ฐ. F ๋น„, p ๊ฐ’ ๋ฐ ํšจ๊ณผ ์ถ”์ •๊ฐ’ ํ…Œ์ด๋ธ”(์—ํƒ€ ์ œ๊ณฑ, ๋ถ€๋ถ„ ์—ํƒ€ ์ œ๊ณฑ, ์˜ค๋ฉ”๊ฐ€ ์ œ๊ณฑ)์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Statistical Modeling. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 402 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํšจ๊ณผ ํฌ๊ธฐ ๊ณ„์‚ฐ

F ๋น„, p ๊ฐ’ ๋ฐ ํšจ๊ณผ ์ถ”์ •๊ฐ’ ํ…Œ์ด๋ธ”(์—ํƒ€ ์ œ๊ณฑ, ๋ถ€๋ถ„ ์—ํƒ€ ์ œ๊ณฑ, ์˜ค๋ฉ”๊ฐ€ ์ œ๊ณฑ)์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค

ํ†ต๊ณ„ ๋ชจ๋ธ๋ง

402 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ตœ๊ณ  ํ‰์ 

์ค‘๊ตญ์–ด ์„ค๋ช…์„œ. ๋ชจ๋“  JMP ์„ค๋ช…์„œ ๋ฐ ์„ค๋ช…์„œ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์šฉ PDF ํŒŒ์ผ. ๋ฒ”์ฃผ: Documentation. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 312 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์ค‘๊ตญ์–ด ์„ค๋ช…์„œ

๋ชจ๋“  JMP ์„ค๋ช…์„œ ๋ฐ ์„ค๋ช…์„œ ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์šฉ PDF ํŒŒ์ผ

์„ค๋ช…์„œ

312 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋ฒค ๋‹ค์ด์–ด๊ทธ๋žจ. JMPยฎ๋กœ ๋ฒค ๋‹ค์ด์–ด๊ทธ๋žจ์„ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Visualization. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 398 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋ฒค ๋‹ค์ด์–ด๊ทธ๋žจ

JMPยฎ๋กœ ๋ฒค ๋‹ค์ด์–ด๊ทธ๋žจ์„ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

398 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro์šฉ XGBoost. ์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” XGBoost ์˜คํ”ˆ ์†Œ์Šค ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ํฌ์ธํ„ฐ ํด๋ฆญ ์ธํ„ฐํŽ˜์ด์Šค. ๋ฒ”์ฃผ: Machine Learning, Data Science. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 532 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro์šฉ XGBoost

์˜ˆ์ธก ๋ชจ๋ธ๋ง์„ ์œ„ํ•ด ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” XGBoost ์˜คํ”ˆ ์†Œ์Šค ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ๋Œ€ํ•œ ํฌ์ธํ„ฐ ํด๋ฆญ ์ธํ„ฐํŽ˜์ด์Šค

๋ฐ์ดํ„ฐ ๊ณผํ•™, ๋จธ์‹  ๋Ÿฌ๋‹

532 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

OFAT ๋Œ€ DOE ์‹œ์—ฐ. OFAT ์‹คํ—˜ ๊ฒฐ๊ณผ์™€ DOE ๋ฐฉ๋ฒ•์„ ๋Œ€์กฐํ•˜๋Š” ๊ฐ„๋‹จํ•œ ๋Œ€์‹œ๋ณด๋“œ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Experimental Design. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 219 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

OFAT ๋Œ€ DOE ์‹œ์—ฐ

OFAT ์‹คํ—˜ ๊ฒฐ๊ณผ์™€ DOE ๋ฐฉ๋ฒ•์„ ๋Œ€์กฐํ•˜๋Š” ๊ฐ„๋‹จํ•œ ๋Œ€์‹œ๋ณด๋“œ์ž…๋‹ˆ๋‹ค

์‹คํ—˜ ์„ค๊ณ„

219 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro ์šฉ Torch Companion. Torch Deep Learning ์ถ”๊ฐ€๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ์ดˆ๋ชจ์ˆ˜๋ฅผ ์ž๋™์œผ๋กœ ์„ ๋ณ„ํ•ฉ๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Experimental Design, Machine Learning, Data Analysis. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 139 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro ์šฉ Torch Companion

Torch Deep Learning ์ถ”๊ฐ€๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ์ดˆ๋ชจ์ˆ˜๋ฅผ ์ž๋™์œผ๋กœ ์„ ๋ณ„ํ•ฉ๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ๋ถ„์„, ๋จธ์‹  ๋Ÿฌ๋‹, ์‹คํ—˜ ์„ค๊ณ„

139 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

3D๋กœ ์œ ์ • ํŠธ๋ ˆ์ด์Šค ๊ทธ๋ฆผ. ๋ฐฉํ–ฅ ์กฐ์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ 3D๋กœ ์œ ์ • ํŠธ๋ ˆ์ด์Šค๋ฅผ ๊ทธ๋ฆผ์— ํ‘œ์‹œํ•ฉ๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Visualization. Industries: Clean Energy & Conservation. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 55 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

3D๋กœ ์œ ์ • ํŠธ๋ ˆ์ด์Šค ๊ทธ๋ฆผ

๋ฐฉํ–ฅ ์กฐ์‚ฌ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ 3D๋กœ ์œ ์ • ํŠธ๋ ˆ์ด์Šค๋ฅผ ๊ทธ๋ฆผ์— ํ‘œ์‹œํ•ฉ๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

55 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ถ”๊ฐ€๊ธฐ๋Šฅ ๊ด€๋ฆฌ์ž. ์ถ”๊ฐ€๊ธฐ๋Šฅ ํŒŒ์ผ์„ ์‰ฝ๊ฒŒ ๋นŒ๋“œ ๋ฐ ์‚ฌ์šฉ์ž ์ •์˜ํ•˜๊ณ , ์ถ”๊ฐ€๊ธฐ๋Šฅ ๋ฒ„์ „์„ ๊ด€๋ฆฌํ•˜๊ณ , ์†Œ์Šค ํŒŒ์ผ์— ์•ก์„ธ์Šคํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 404 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์ถ”๊ฐ€๊ธฐ๋Šฅ ๊ด€๋ฆฌ์ž

์ถ”๊ฐ€๊ธฐ๋Šฅ ํŒŒ์ผ์„ ์‰ฝ๊ฒŒ ๋นŒ๋“œ ๋ฐ ์‚ฌ์šฉ์ž ์ •์˜ํ•˜๊ณ , ์ถ”๊ฐ€๊ธฐ๋Šฅ ๋ฒ„์ „์„ ๊ด€๋ฆฌํ•˜๊ณ , ์†Œ์Šค ํŒŒ์ผ์— ์•ก์„ธ์Šคํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค

์œ ํ‹ธ๋ฆฌํ‹ฐ

404 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

ํ•™์ƒ ๋ฉ”๋‰ด. ํ•ต์‹ฌ ํ†ต๊ณ„ ๊ฐœ๋…์„ ๊ต์œกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋Œ€ํ™”์‹ ์• ํ”Œ๋ฆฟ์ด ํฌํ•จ๋œ ํ•™์ƒ ๋ฉ”๋‰ด์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility. Industries: Academic. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 2817 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํ•™์ƒ ๋ฉ”๋‰ด

ํ•ต์‹ฌ ํ†ต๊ณ„ ๊ฐœ๋…์„ ๊ต์œกํ•˜๊ธฐ ์œ„ํ•œ ๋‹ค์–‘ํ•œ ๋Œ€ํ™”์‹ ์• ํ”Œ๋ฆฟ์ด ํฌํ•จ๋œ ํ•™์ƒ ๋ฉ”๋‰ด์ž…๋‹ˆ๋‹ค

์œ ํ‹ธ๋ฆฌํ‹ฐ

2.8k ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Pro๋ฅผ ์œ„ํ•œ ์‹ ๊ฒฝ๋ง ์กฐ์ •. ์€๋‹‰์ธต, ๋ถ€์ŠคํŠธ ๋ฐ ์ ํ•ฉ ์˜ต์…˜์— ๋Œ€ํ•œ ์ตœ์  ๋ชจ์ˆ˜๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด ๋‹ค์ค‘ ์‹ ๊ฒฝ๋ง์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Science. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 287 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Pro๋ฅผ ์œ„ํ•œ ์‹ ๊ฒฝ๋ง ์กฐ์ •

์€๋‹‰์ธต, ๋ถ€์ŠคํŠธ ๋ฐ ์ ํ•ฉ ์˜ต์…˜์— ๋Œ€ํ•œ ์ตœ์  ๋ชจ์ˆ˜๋ฅผ ์ฐพ๊ธฐ ์œ„ํ•ด ๋‹ค์ค‘ ์‹ ๊ฒฝ๋ง์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ธํ„ฐํŽ˜์ด์Šค์ž…๋‹ˆ๋‹ค

๋ฐ์ดํ„ฐ ๊ณผํ•™

287 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋Ÿฐ๋ด‡. ์‚ฌ์šฉ์ž๊ฐ€ JMPยฎ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ด‡์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility, Documentation. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 1839 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋Ÿฐ๋ด‡

์‚ฌ์šฉ์ž๊ฐ€ JMPยฎ ์†Œํ”„ํŠธ์›จ์–ด๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ƒ‰ํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋˜๋Š” ๋ด‡์ž…๋‹ˆ๋‹ค

์„ค๋ช…์„œ, ์œ ํ‹ธ๋ฆฌํ‹ฐ

1.8k ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋‹จ์œ„ ๋ถ€์ ํ•ฉ. ์—ฌ๋Ÿฌ ๊ทœ๊ฒฉ ํ•œ๊ณ„์— ๋Œ€ํ•œ ๋‹จ์œ„๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ๊ณ ์žฅ ํ†ต๊ณ„๋Ÿ‰์„ ๋ณด๊ณ ํ•˜๋Š” ๋„๊ตฌ. ๋ฒ”์ฃผ: Data Analysis. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 65 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋‹จ์œ„ ๋ถ€์ ํ•ฉ

์—ฌ๋Ÿฌ ๊ทœ๊ฒฉ ํ•œ๊ณ„์— ๋Œ€ํ•œ ๋‹จ์œ„๋ฅผ ํ‰๊ฐ€ํ•˜๊ณ  ๊ณ ์žฅ ํ†ต๊ณ„๋Ÿ‰์„ ๋ณด๊ณ ํ•˜๋Š” ๋„๊ตฌ

๋ฐ์ดํ„ฐ ๋ถ„์„

65 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Live ํ™œ๋™ ๋Œ€์‹œ๋ณด๋“œ. JMPยฎ Live ์‚ฌ์šฉ์ž ํ™œ๋™์„ ๋ณด๊ธฐ ์œ„ํ•ด JMPยฎ Live ๊ฐ์‚ฌ ๋กœ๊ทธ์—์„œ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Visualization. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 43 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Live ํ™œ๋™ ๋Œ€์‹œ๋ณด๋“œ

JMPยฎ Live ์‚ฌ์šฉ์ž ํ™œ๋™์„ ๋ณด๊ธฐ ์œ„ํ•ด JMPยฎ Live ๊ฐ์‚ฌ ๋กœ๊ทธ์—์„œ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

43 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ตœ๊ทผ ๊ฒŒ์‹œ

๋„คํŠธ์›Œํฌ ๊ทธ๋ฆผ. ๊ทธ๋ž˜ํ”„ ๋นŒ๋”์—์„œ ์ด๋ฒคํŠธ ๋˜๋Š” ๋ฐœ์ƒ์— ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ ํ…Œ์ด๋ธ”์„ ์‚ฌ์šฉํ•˜์—ฌ ๋„คํŠธ์›Œํฌ ๊ทธ๋ฆผ์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ. ๋ฒ”์ฃผ: Data Visualization, Data Science. Industries: Academic. JMP์—์„œ ์ œ์ž‘. 6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋„คํŠธ์›Œํฌ ๊ทธ๋ฆผ

๊ทธ๋ž˜ํ”„ ๋นŒ๋”์—์„œ ์ด๋ฒคํŠธ ๋˜๋Š” ๋ฐœ์ƒ์— ๋Œ€ํ•œ ๋ฐ์ดํ„ฐ ํ…Œ์ด๋ธ”์„ ์‚ฌ์šฉํ•˜์—ฌ ๋„คํŠธ์›Œํฌ ๊ทธ๋ฆผ์„ ์ƒ์„ฑํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ

๋ฐ์ดํ„ฐ ๊ณผํ•™, ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๊ธฐ์ค€ ๋ถ„ํฌ. Plant ์‹œํ—˜ ๋ถ„์„์„ ์œ„ํ•œ ๋Œ€์กฐ ๋ถ„ํฌ ๊ณ„์‚ฐ ๋„๊ตฌ. ๋ฒ”์ฃผ: Experimental Design, Six Sigma, Data Analysis. Industries: Industrial Manufacturing. JMP์—์„œ ์ œ์ž‘. 3 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๊ธฐ์ค€ ๋ถ„ํฌ

Plant ์‹œํ—˜ ๋ถ„์„์„ ์œ„ํ•œ ๋Œ€์กฐ ๋ถ„ํฌ ๊ณ„์‚ฐ ๋„๊ตฌ

Six Sigma, ๋ฐ์ดํ„ฐ ๋ถ„์„, ์‹คํ—˜ ์„ค๊ณ„

3 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

ํ™˜๊ฒฝ ์„ค์ • ๊ด€๋ฆฌ์ž. ํ™˜๊ฒฝ ์„ค์ • ํ”„๋กœํŒŒ์ผ ์ €์žฅ, ๋‚ด๋ณด๋‚ด๊ธฐ, ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ „ํ™˜. ๋ฒ”์ฃผ: Utility. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 5 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

ํ™˜๊ฒฝ ์„ค์ • ๊ด€๋ฆฌ์ž

ํ™˜๊ฒฝ ์„ค์ • ํ”„๋กœํŒŒ์ผ ์ €์žฅ, ๋‚ด๋ณด๋‚ด๊ธฐ, ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ „ํ™˜

์œ ํ‹ธ๋ฆฌํ‹ฐ

5 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋„คํŠธ์›Œํฌ ๋ถ„์„. Python NetworkX๋ฅผ ์‚ฌ์šฉํ•œ JMPยฎ ์˜ ๋„คํŠธ์›Œํฌ ๋ถ„์„. ๋ฒ”์ฃผ: Data Analysis, Data Visualization, Data Science. JMP์—์„œ ์ œ์ž‘. 19 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋„คํŠธ์›Œํฌ ๋ถ„์„

Python NetworkX๋ฅผ ์‚ฌ์šฉํ•œ JMPยฎ ์˜ ๋„คํŠธ์›Œํฌ ๋ถ„์„

๋ฐ์ดํ„ฐ ๊ณผํ•™, ๋ฐ์ดํ„ฐ ๋ถ„์„, ๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

19 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

MySQL ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ. MySQL์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ. ๋ฒ”์ฃผ: Data Connectors. JMP์—์„œ ์ œ์ž‘. 6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

MySQL ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

MySQL์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ

๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

PostgreSQL ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ. PostgreSQL์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ. ๋ฒ”์ฃผ: Data Connectors. JMP์—์„œ ์ œ์ž‘. 3 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

PostgreSQL ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

PostgreSQL์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ

๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

3 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋กœ๊ฑฐ ์‹œ์ž‘. ์ปดํ“จํ„ฐ์—์„œ JMP ์‹œ์ž‘์„ ๊ธฐ๋กํ•˜๋Š” ๋„๊ตฌ. ๋ฒ”์ฃผ: Utility. JMP์—์„œ ์ œ์ž‘. 6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋กœ๊ฑฐ ์‹œ์ž‘

์ปดํ“จํ„ฐ์—์„œ JMP ์‹œ์ž‘์„ ๊ธฐ๋กํ•˜๋Š” ๋„๊ตฌ

์œ ํ‹ธ๋ฆฌํ‹ฐ

6 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

์ด์ค‘ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ฒ€์ฆ ๋„๊ตฌ. JMP Clinical ๋ณด๊ณ ์„œ ์ž‘์„ฑ์ž์™€ ๋…๋ฆฝ์ ์œผ๋กœ ์ž‘์„ฑํ•œ ์ฝ”๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ JMP Clinical Clinical ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility, Documentation, Data Analysis. JMP์—์„œ ์ œ์ž‘. 5 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

์ด์ค‘ ํ”„๋กœ๊ทธ๋ž˜๋ฐ ๊ฒ€์ฆ ๋„๊ตฌ

JMP Clinical ๋ณด๊ณ ์„œ ์ž‘์„ฑ์ž์™€ ๋…๋ฆฝ์ ์œผ๋กœ ์ž‘์„ฑํ•œ ์ฝ”๋“œ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ JMP Clinical Clinical ๊ฒฐ๊ณผ๋ฅผ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ๋ถ„์„, ์„ค๋ช…์„œ, ์œ ํ‹ธ๋ฆฌํ‹ฐ

5 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

TDMS ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ. ๋‚ด์šฉ์„ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ๋ชจ๋“œ์˜ TDMS(Labview 4 (tm)) ๋ฐ์ดํ„ฐ ํŒŒ์ผ์„ ์—ฌ๋Š” ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ (JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ). ๋ฒ”์ฃผ: Data Connectors. JMP์—์„œ ์ œ์ž‘. 10 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

TDMS ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

๋‚ด์šฉ์„ ํƒ์ƒ‰ํ•˜๊ธฐ ์œ„ํ•ด ์„œ๋กœ ๋‹ค๋ฅธ ๋ชจ๋“œ์˜ TDMS(Labview 4 (tm)) ๋ฐ์ดํ„ฐ ํŒŒ์ผ์„ ์—ฌ๋Š” ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ (JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ)

๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

10 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

๋ฐ์ดํ„ฐ ์ƒ์„ฑ๊ธฐ. ๋ฐ์ดํ„ฐ ์ƒ์„ฑ ๋ฐ ํŽธ์ง‘, ๊ต์œก์šฉ ๋ฐ์ดํ„ฐ ์ง‘ํ•ฉ ์ƒ์„ฑ, ์˜ˆ์ธก ๋ชจํ˜• ํ…Œ์ŠคํŠธ ๋ฐ FDE ๋ชฉํ‘œ๊ฐ’ ์ƒ์„ฑ์„ ์œ„ํ•œ ๊ฐ„๋‹จํ•œ ๊ทธ๋ฆฌ๊ธฐ ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ ํฌํ•จ๋œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Utility, Statistical Modeling, Data Visualization. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 68 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

๋ฐ์ดํ„ฐ ์ƒ์„ฑ๊ธฐ

๋ฐ์ดํ„ฐ ์ƒ์„ฑ ๋ฐ ํŽธ์ง‘, ๊ต์œก์šฉ ๋ฐ์ดํ„ฐ ์ง‘ํ•ฉ ์ƒ์„ฑ, ์˜ˆ์ธก ๋ชจํ˜• ํ…Œ์ŠคํŠธ ๋ฐ FDE ๋ชฉํ‘œ๊ฐ’ ์ƒ์„ฑ์„ ์œ„ํ•œ ๊ฐ„๋‹จํ•œ ๊ทธ๋ฆฌ๊ธฐ ์ธํ„ฐํŽ˜์ด์Šค๊ฐ€ ํฌํ•จ๋œ ๋„๊ตฌ์ž…๋‹ˆ๋‹ค.

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”, ์œ ํ‹ธ๋ฆฌํ‹ฐ, ํ†ต๊ณ„ ๋ชจ๋ธ๋ง

68 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

SalesForce ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ. SalesForce์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ( JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ). ๋ฒ”์ฃผ: Data Connectors. JMP์—์„œ ์ œ์ž‘. 21 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

SalesForce ๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

SalesForce์—์„œ ๋ฐ์ดํ„ฐ ๊ฐ€์ ธ์˜ค๊ธฐ ๋ฐ ์ฟผ๋ฆฌ( JMPยฎ 19 EA์—์„œ๋งŒ ์‚ฌ์šฉ ๊ฐ€๋Šฅ)

๋ฐ์ดํ„ฐ ์ปค๋„ฅํ„ฐ

21 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMPยฎ Live ํ™œ๋™ ๋Œ€์‹œ๋ณด๋“œ. JMPยฎ Live ์‚ฌ์šฉ์ž ํ™œ๋™์„ ๋ณด๊ธฐ ์œ„ํ•ด JMPยฎ Live ๊ฐ์‚ฌ ๋กœ๊ทธ์—์„œ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค. ๋ฒ”์ฃผ: Data Visualization. JMP์—์„œ ์ œ์ž‘. ํ‰์ : 5 / 5. 43 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜.

JMPยฎ Live ํ™œ๋™ ๋Œ€์‹œ๋ณด๋“œ

JMPยฎ Live ์‚ฌ์šฉ์ž ํ™œ๋™์„ ๋ณด๊ธฐ ์œ„ํ•ด JMPยฎ Live ๊ฐ์‚ฌ ๋กœ๊ทธ์—์„œ ๋Œ€์‹œ๋ณด๋“œ๋ฅผ ์ž๋™์œผ๋กœ ์ƒ์„ฑํ•˜๋Š” ๋„๊ตฌ์ž…๋‹ˆ๋‹ค

๋ฐ์ดํ„ฐ ์‹œ๊ฐํ™”

43 ๋‹ค์šด๋กœ๋“œ ํšŸ์ˆ˜

JMP๋ฅผ ์ฒ˜์Œ ์‚ฌ์šฉํ•˜์‹œ๋‚˜์š”? ๋ฌด๋ฃŒ ํ‰๊ฐ€ํŒ ๋‹ค์šด๋กœ๋“œ