๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

๐“ก๐“ธ๐“ธ๐“ถ5: ๐’ฆ๐‘œ๐“‡๐‘’๐’ถ ๐’ฐ๐“ƒ๐’พ๐“‹

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[๊ฒฝ์˜์ „๋žต] 4. ์ง€์†๊ฐ€๋Šฅํ•œ ๊ฒฝ์Ÿ์šฐ์œ„์ฐฝ์ถœ์„ ์œ„ํ•œ ์ „๋žต - ๊ฒฝ์Ÿ์ „๋žต 0. ๊ฒฝ์Ÿ์ „๋žต์ด๋ž€ ์ „๋žต์˜ ๋‹จ๊ณ„๋Š” ํ™˜๊ฒฝ๋ถ„์„ > ์ „๋žต์ˆ˜๋ฆฝ > ์ „๋žต์‹คํ–‰ > ํ‰๊ฐ€์™€ ํ†ต์ œ ๋กœ ์ด๋ค„์ง„๋‹ค. ์ด ๋•Œ ์ „๋žต์—๋Š” ๊ธฐ์—…์ „๋žต (Corporate Strategy)์™€ ๊ฒฝ์Ÿ์ „๋žต (Competitive Strategy) ๋“ฑ์ด ์žˆ๋‹ค ์ „๋žต์„ ์ˆ˜๋ฆฝํ•  ๋•Œ์—๋Š” ๋‹ค์Œ ์‚ฌํ•ญ๋“ค์„ ๊ณ ๋ คํ•ด์•ผ ํ•œ๋‹ค. 1) Where to compete : ์ „์‚ฌ์ „๋žต๊ณผ ๊ด€๋ จ๋œ issue Industry Products Customers Channel Regions 2) How to compete : ๋ณดํ†ต ์‚ฌ์—… ์ „๋žต๊ณผ ๊ด€๋ จ๋œ issue Value proposition : ๊ณ ๊ฐ๋“ค์—๊ฒŒ ์–ด๋–ค ์ฐจ๋ณ„ํ™”๋œ ๊ณ ๊ฐ ๊ฐ€์น˜๋ฅผ ์–ด๋–ป๊ฒŒ ์ฐฝ์ถœํ•  ๊ฒƒ์ธ๊ฐ€ Business system : value chain ๊ด€๋ จ Core competence : ํ•ต์‹ฌ์—ญ๋Ÿ‰ 3) When to com..
[๊ฒฝ์Ÿ์ „๋žต] 3. ๊ฒฝ์Ÿ์ „๋žต์ˆ˜๋ฆฝ - ๋‚ด๋ถ€ํ™˜๊ฒฝ๊ณผ ํ•ต์‹ฌ์—ญ๋Ÿ‰ 0. ๋‚ด๋ถ€ํ™˜๊ฒฝ์˜ ์ดํ•ด 1. ์ˆ˜์ต์„ฑ ์‚ฐ์—… ํ‰๊ท ๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ฒฝ์Ÿ์ž/์‚ฐ์—… ํ‰๊ท  ๋Œ€๋น„ ์šฐ๋ฆฌ๊ฐ€ ์–ผ๋งˆ๋‚˜ ์ž˜ํ•˜๋Š”์ง€ ๋ถ„์„ ๋‹จ, ์‹ ์ƒ ๊ธฐ์—…์ผ ๊ฒฝ์šฐ Break-even point ๋ณด๊ธฐ [๋ฌธ์ œํ•ด๊ฒฐ๋Šฅ๋ ฅ] MECE principle : ๋ฌธ์ œ๋ฅผ ์ž˜ ์ชผ๊ฐœ์–ด์„œ ๋ณด๊ธฐ (Mutually Exclusive and Collectively Exhaustive) ์›์ธ์ด ์ž‘์€ ๋ถ€๋ถ„์€ ๋ฌด์‹œํ•˜๊ณ  ์ค‘์š”ํ•œ ๋ถ€๋ถ„์— ์ง‘์ค‘ํ•˜๊ธฐ Revenue - cost์—์„œ ์–ด๋–ค ๋ถ€๋ถ„์ด ๋ฌธ์ œ์ธ๊ฐ€ -> ๋” ํฐ ๋ฌธ์ œ๋งŒ ์ง‘์ค‘ํ•ด์„œ ๋ด๋ผ revenue = p*q = p๊ฐ€ ์ ๊ฑฐ๋‚˜ q๊ฐ€ ์ ๊ฑฐ๋‚˜ cost ๊ฐ€ ๋„ˆ๋ฌด ํฌ๊ฑฐ๋‚˜ 1) ํˆฌ์ž์ž๋ณธ ์ˆ˜์ต๋ฅ  : ROIC (Return on Invested Capital) ์ž๋ณธ์„ ํ†ตํ•ด ๋‚˜์˜จ ์ˆ˜์ต ROIC๊ฐ€ ๋†’์•„์•ผ์ง€ ์ˆ˜์ต์„ฑ์ด ๋†’์€ ๊ฒƒ!! ๋‚ฎ๋‹ค๋ฉด ๊ทธ ์ด์œ ๋ฅผ ๋ถ„..
[๊ฒฝ์˜์ „๋žต] 2. ๊ฒฝ์Ÿ์ „๋žต์ˆ˜๋ฆฝ - ์™ธ๋ถ€ํ™˜๊ฒฝ๋ถ„์„ 0. ์™ธ๋ถ€ํ™˜๊ฒฝ์ด๋ž€ ์™ธ๋ถ€ ํ™˜๊ฒฝ = PEST + Industry ๋™ํƒœ์  ๋ถ„์„ : ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ฅธ ๋ณ€ํ™” (Dynamic state) ์ •ํƒœ์  ๋ถ„์„ : ํ•œ ์‹œ์ ์— ๋Š์–ด์„œ ๋ถ„์„ ex. ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ -> ์ •ํƒœ์  ๋ถ„์„ Learning curve -> ๋™ํƒœ์  ๋ถ„์„ KSF : ์ด ์‚ฐ์—…์—์„œ ์„ฑ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ์š”์†Œ (์ด ํšŒ์‚ฌ๊ฐ€ ์„ฑ๊ณตํ•˜๊ธฐ ์œ„ํ•œ ์š”์†Œ๊ฐ€ ์•„๋‹˜) ์ž์„ธํ•œ ์„ค๋ช…์€ ์•„๋ž˜์—์„œ ํ•ด๋ด…์‹œ๋‹ค 1. Macro-Environment : ๊ฑฐ์‹œํ™˜๊ฒฝ ๋ถ„์„ (=PEST) ์•„๋ฌด๋ฆฌ ์‚ฐ์—…(micro ๋ถ„์„)์ด ์ข‹์•„๋„, ์™ธ๋ถ€ ํ™˜๊ฒฝ์ด ์ข‹์•„์•ผ ํ•จ. P : ๋ฒ•, ์ •์น˜, ๊ทœ์ œ ํ™˜๊ฒฝ (Regulation, Legal, Political) E : ๊ฑฐ์‹œ๊ฒฝ์ œ์  ํ™˜๊ฒฝ (ex. ๋ฉ”๊ฐ€๋“œ๋ Œ๋“œ - ์ด๋จธ์ง• ๋งˆ์ผ“์˜ ๋ถ€์ƒ, ๊ณ ์œ ๊ฐ€) S : ์‚ฌํšŒ๋ฌธํ™”์  ํ™˜๊ฒฝ (ex. SNS, ๊ฐœ๋ฐฉ..
[๊ฒฝ์˜์ „๋žต] 1. ์ „๋žต ํ”„๋ ˆ์ž„์›Œํฌ ๋ฐ ๋น„์ „ 0. ์ „๋žต์ด๋ž€? ์ „๋žต์˜ ์ •์˜ = How to achieve , ๋ชฉํ‘œ๋ฅผ ์„ฑ๊ณต์ ์œผ๋กœ ์ด๋ฃจ๊ธฐ ์œ„ํ•œ ๋ฐฉ์‹ = ์„ ํƒ๊ณผ ํฌ๊ธฐ ์‹œ๊ฐ„, ๋ˆ, ๋ฆฌ์†Œ์Šค๋Š” ์œ ํ•œํ•จ Aspiration : ์—ด์ •, ์—ด๋ง, ๋ชฉํ‘œ --> ์ด๊ฑธ ๊ธฐ๋ฐ˜์œผ๋กœ ์ „๋žต ์ˆ˜๋ฆฝ ๊ธฐ์—…์˜ ๋น„์ „ ๋ฐ ๋ชฉํ‘œ, ์™ธ๋ถ€ ํ™˜๊ฒฝ ๋ฐ ๋‚ด๋ถ€์—ญ๋Ÿ‰์˜ ์ ํ•ฉ์„ฑ์„ ์ถ”๊ตฌํ•˜์—ฌ '์ œํ•œ๋œ ์ž์›'์œผ๋กœ ์ง€์† ๊ฐ€๋Šฅํ•œ ๊ฒฝ์Ÿ์šฐ์œ„๋ฅผ ์ฐฝ์ถœํ•˜๊ธฐ์œ„ํ•œ ์˜์‚ฌ๊ฒฐ์ • ์ด ๋•Œ ์ž์›๋ฐฐ๋ถ„์˜ ์šฐ์„  ์ˆœ์œ„ ๊ฒฐ์ •์€ "์„ ํƒ๊ณผ ์ง‘์ค‘"!!! ์ „๋žต์˜ ๊ฐ ๊ตฌ์„ฑ ์š”์†Œ๊ฐ„์˜ ์ผ๊ด€์„ฑ ๋ฐ ์ ํ•ฉ์„ฑ์ด ์ค‘์š”ํ•จ ์ „๋žต ํ”ผ๋ผ๋ฏธ๋“œ ๋น„์ฆˆ๋‹ˆ์Šค์˜ ํ†ต์ œ ํญ (์‹œ๊ฐ„) : ๊ธฐ์—…์ „๋žต(๊ธบ) ----------- ์‹คํ–‰ ๋‹จ์œ„๋ณ„ ์ „๋žต (์งง์Œ) ์ž์›์˜ ๊ทœ๋ชจ : ๊ธฐ์—…์ „๋žต (ํผ) ------------- ์‹คํ–‰ ๋‹จ์œ„๋ณ„ ์ „๋žต (์ž‘์Œ) ์กฐ์ง ๊ด€๋ฆฌ ํญ : ๊ธฐ์—…์ „๋žต (๋„“์Œ) --------------..
[๊ฒฝ์˜์ •๋ณด์‹œ์Šคํ…œ] 3. Marketing Research Marketing Research Formal Communication link with the environment for providing accurate and useful information for planning, problem solving, and control which leads to better decision making. specifying, collecting, analyzing, interpreting information์„ ์ˆ˜๋ฐ˜ํ•จ! Primary Data : Expensive Secondary Data : not always suitable research process ๋ฌธ์ œ์ธ์‹์„ ํ•œ๋‹ค ์ด์ „์— ์ฐพ์€ ๊ฒƒ์„ ๊ฒ€ํ† ํ•œ๋‹ค solution์„ ์„ค๊ณ„ํ•˜๊ณ  variables๋ฅผ ์„ ํƒํ•œ๋‹ค ๋ฐ์ดํ„ฐ๋ฅผ..
[๊ฒฝ์˜์ •๋ณด์‹œ์Šคํ…œ] 2. Regression Regression Regression X์™€ Y ์‚ฌ์ด์˜ relationship์„ ๋ถ„์„ํ•˜๋Š” statistical model X = ํ•˜๋‚˜ ์ด์ƒ์˜ independent/explanatory variables Y = dependent, target, explanatory variables, ์ฆ‰ ์šฐ๋ฆฌ๊ฐ€ ์˜ˆ์ธกํ•˜๊ณ  ์‹ถ์€ ๊ฒƒ x๊ฐ’์˜ ๋ณ€ํ™”์— ๋”ฐ๋ฅธ Y๊ฐ’์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด, ๋” ๋‚˜์•„๊ฐ€ X์™€ Y ๊ฐ„์˜ relationship์„ explanationํ•˜๊ธฐ ์œ„ํ•ด! ๋ฐ์ดํ„ฐ์…‹์„ ํ•™์Šตํ•˜์—ฌ ์•„์ง๋ชจ๋ฅด๋Š” parameter a, b๊ฐ’์„ ๊ตฌํ•ด๋‚ธ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด Height = a + b(Age) ๋ผ๊ณ  ํ•ด๋ณด์ž. Age๊ฐ€ ์ปค์งˆ ์ˆ˜๋ก Height๋„ ์–ด๋Š์ •๋„ ๋น„๋ก€ํ•ด์„œ ์ปค์งˆ ๊ฒƒ์ด๋‹ค. ์‹ค์ œ๋กœ ๋ฐ์ดํ„ฐ๋ฅผ ์‚ฌ๋ถ„๋ฉด ์œ„์— ํ‘œ์‹œํ•ด๋ณด๋ฉด ๊ทธ๋ž˜ํ”„์˜ ๊ธฐ์šธ๊ธฐ๊ฐ€ ์–‘์ˆ˜์ธ ํ˜•ํƒœ์˜ ๊ทธ๋ž˜..
[๊ฒฝ์˜์ •๋ณด์‹œ์Šคํ…œ] 1. Artificial Intelligence * Exemplary System์€ ์‹œํ—˜๋ฒ”์œ„์— ํฌํ•จ๋˜์ง€ ์•Š์œผ๋‹ˆ ์ƒ๋žตํ•œ๋‹ค Deductive : ์—ฐ์—ญ๋ฒ• (Idea -> Observations -> Conclusion) -> Rule-Based Inductive : ๊ท€๋‚ฉ๋ฒ• -> Case-Based (Observations -> Analysis -> Theory) Artificial Intelligence (AI) A huge set of tools for making computers behave intelligently Expert system : ์‚ฌ๋žŒ์ด ์•„๋Š” ๊ฒƒ์„ ๊ธฐ๊ณ„์—๊ฒŒ ๊ทธ๋Œ€๋กœ ์ฃผ์ž…์‹œ์ผœ ์˜์‚ฌ๋‚˜ ๋ณ€ํ˜ธ์‚ฌ์ฒ˜๋Ÿผ ์ถ”๋ก ํ•˜๋Š” ๊ธฐ๊ณ„๋ฅผ ๋งŒ๋“ค ์ˆ˜ ์žˆ๋‹ค๋Š” ์ƒ๊ฐ์—์„œ ๋‚˜์˜จ ๊ฒƒ์œผ๋กœ Rule based์ธ AI ์ข…๋ฅ˜ ์ค‘ ํ•˜๋‚˜. ํ•˜์ง€๋งŒ ์ด๊ฒƒ์€ ์ธ๊ฐ„์˜ ๋Šฅ๋ ฅ๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ํ•œ์ฐธ ๋ถ€์กฑํ•จ..
[๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค] CH7. Relational Database Design(Normalization) 0. Intro 1. Features of Good Relational Design ์šฐ๋ฆฌ๊ฐ€ instructor ๊ณผ department๋ฅผ in_dep์ด๋ผ๋Š” table๋กœ ํ•ฉ์ณค๋‹ค๊ณ  ์ƒ๊ฐํ•ด๋ณด์ž. ์ด๋Ÿฌ๋ฉด ๊ฐ™์€ dept ์ •๋ณด๊ฐ€ ๊ณ„์† ๋ฐ˜๋ณตํ•ด์„œ ๋“ค์–ด๊ฐ€๊ณ , ๋งŒ์•ฝ ํ•™๊ณผ๊ฐ€ ์ƒˆ๋กœ ๋งŒ๋“ค์–ด ์กŒ์„ ๋•Œ ๊ต์ˆ˜์ž๊ฐ€ ์•„์ง ์—†๋‹ค๋ฉด null๊ฐ’์œผ๋กœ ๋„ฃ์–ด ์ถ”๊ฐ€ํ•ด์•ผ ํ•˜๋Š” ๋ฌธ์ œ๋„ ๋ฐœ์ƒํ•œ๋‹ค. ์‹ฌ์ง€์–ด id๊ฐ€ not null์ด๋ฉด ์ด๊ฒƒ๋„ ๋ถˆ๊ฐ€๋Šฅ ํ•˜๋‹ค. ์ฆ‰, ๋ฌด์กฐ๊ฑด table์„ ํ•ฉ์นœ๋‹ค๊ณ  ์ข‹์€ ๊ฒƒ์ด ์•„๋‹ˆ๋‹ค. Combined schema without repetition ๊ทธ๋Ÿฌ๋‚˜ ํ•ฉ์นœ๋‹ค๊ณ  ๋˜ ๋ฌด์กฐ๊ฑด ์ค‘๋ณต์ด ์ƒ๊ธฐ๋Š” ๊ฑด ์•„๋‹ˆ๋‹ค. sec_class์™€ section์„ ํ•ฉ์น  ๊ฒฝ์šฐ ์ค‘๋ณต๋˜๋Š” ์ •๋ณด๊ฐ€ ์—†๊ฒŒ ๋œ๋‹ค. 2. Decomposition : table ์ชผ๊ฐœ๊ธฐ. ์šฐ๋ฆฌ๊ฐ€ ์•ž..
[๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค] CH6. Database Design Using the E-R Model 0. INTRO 1. Design Phases Initial phase : ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค ์‚ฌ์šฉ์ž๊ฐ€ ํ•„์š”๋กœ ํ•˜๋Š” ๋ฐ์ดํ„ฐ๋ฅผ ํŒŒ์•…ํ•˜๊ธฐ Second phase : conceptual design data model์„ ์„ ํƒํ•ด์„œ ๊ฐœ๋…์„ ์ ์šฉํ•œ๋‹ค. requirements๋ฅผ ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ conceptual schema๋กœ ๋ณ€ํ™˜ํ•œ๋‹ค. (ex. ER Model) ๊ฐœ๋ฐœ์ด ์™„๋ฃŒ๋œ conceptual schema๋Š” ๊ธฐ์—…์˜ ๊ธฐ๋Šฅ ์š”๊ตฌ ์‚ฌํ•ญ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. Final Phase : ์ถ”์ƒ์ ์ธ data model์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๊ตฌํ˜„ํ•˜๊ธฐ Logical Design : ๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค์˜ ์Šคํ‚ค๋งˆ๋ฅผ ๊ฒฐ์ •ํ•œ๋‹ค. (conceptual schema(er diagram)์„ logical schema(relational schema)์— ๋Œ€์‘์‹œํ‚จ๋‹ค) Physica..
[๋ฐ์ดํ„ฐ๋ฒ ์ด์Šค] CH4. Intermediate SQL Join Expressions ๋‘ ๊ฐœ์˜ relation์„ join ํ•ด์„œ ์ƒˆ๋กœ์šด relation์„ ๋ฐ˜ํ™˜ํ•˜๋Š” ๊ฒƒ Cartesian product!! ๋ณดํ†ต from clause์—์„œ ์‚ฌ์šฉ๋จ Join conditions Using Clause -> attribution์„ ์ž˜๋ชป ํ•ฉ์น˜๋Š” ๊ฒฝ์šฐ๋ฅผ ๋ง‰๊ธฐ ์œ„ํ•ด ์”€! select name, title from (student natural join takes) join course using (course_id)์ด๋ ‡๊ฒŒ join ์–ด์ฉŒ๊ตฌ using (์ €์ฉŒ๊ตฌ) ๋ผ๊ณ  ๋ช…์‹œํ•ด์„œ ๊ทธ๋ƒฅ ๊ฒน์น˜๋Š” ๊ฑธ ๋‹ค ํ•ฉ์น˜๋Š” ๊ฒƒ์ด ์•„๋‹ˆ๋ผ, ํŠน์ • attribute๋ฅผ ๊ธฐ์ค€์œผ๋กœ join ํ•˜๊ฒ ๋‹ค๊ณ  ํ•ด์ฃผ๋Š” ๊ฒƒ! ์ด ๊ฒฝ์šฐ Duplication columns์€ ์ œ๊ฑฐ๋œ๋‹ค. Join Condition (on) ์ด pre..