analyticstool

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

(★ 37)

함수산책 - 캐글뽀개기 스터디 자료 보관소

analyticstool 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 함수산책 (이전 이름 캐글즐기기)

함수산책 (캐글뽀개기) 파트5 평일반 스터디 자료모음

* https://www.facebook.com/groups/kagglebreak/
* https://drive.google.com/drive/folders/0B2l0iH28o85xcHJRNWNUc1FvbEk
* 장소 : 토즈 강남점 
* 파트5는 격주 수요일 
* 스터디 KossLab(공개SW 개발자센터)에서 장소를 지원하고 있습니다.

## 교재

* 수리통계, 주교재 최신 수리통계학 출판사 경문사 저자 안승철,이재원,최원
* 선형대수학, 주교재 프로그래머를 위한 선형대수
* 네트워크 분석, 주교재 Python for Graph and Network Analysis

### preivos 교재 
```
* R for Data Science (http://r4ds.had.co.nz/)
* Data Science and Complex Networks Real Case Studies with Python (https://global.oup.com/academic/product/data-science-and-complex-networks-9780199639601?cc=kr&lang=en&)
* Shiny 튜토리얼 따라하기 (https://shiny.rstudio.com/tutorial/)
* 웹을 위한 머신러닝 (http://www.yes24.com/24/Goods/37151418?Acode=101)
* Bayesian Statistics : Techniques and Models (https://www.coursera.org/learn/mcmc-bayesian-statistics)
```

## Part5 커리큘럼
|요일   |주제   |발표자   |발표자료   |   |
|---|---|---|---|---|
|2017.09.13(수)|(선형대수학) 1장 벡터ㆍ행렬ㆍ행렬식 - ‘공간’에서 생각하자(1) |서범석|||
||(수리통계학) 제1장 확률의 소개 |김선화|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/statistics/ch1/1%EC%9E%A5_%ED%99%95%EB%A5%A0%EC%9D%98_%EC%86%8C%EA%B0%9C.html.docx)
||(네트워크) Ch1. Theoretical Concepts of Network Analysis (1)|조응태|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/graph/Ch01/Ch01%20Theoretical%20Concepts%20of%20Network%20Analysis.ipynb)
|2017.09.27(수)|(선형대수학) 1장 벡터ㆍ행렬ㆍ행렬식 - ‘공간’에서 생각하자(2)|서범석|||
||(수리통계학) 제2장 확률변수와 분포|방효석|||
||(네트워크) Ch1. Theoretical Concepts of Network Analysis (2) |조응태|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/graph/Ch01/Ch01%20Theoretical%20Concepts%20of%20Network%20Analysis.ipynb)
||(네트워크) Network Basic (1) |황중원|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/graph/Ch02/Ch02-Python%2Bfor%2BGraph%2Band%2BNetwork%2BAnalysis_Ch2-summary.ipynb)
|2017.10.11(수)|(선형대수학) 2장 랭크ㆍ역행렬ㆍ일차방정식 - 결과에서 원인을 구하다 (1) |손형우|||
||(수리통계학) 제3장 기댓값|김가경|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/statistics/ch3/ch03-20171025T124034Z-001.zip)||
||(네트워크) Ch3. Graph Theory |공민서|[발표자료](http://nbviewer.jupyter.org/format/slides/gist/rrbb014/61b202be2f754308d4fa2befbcb14738#/)
|2017.10.25(수)|(선형대수학) 3장 컴퓨터에서의 계산 (1) - LU 분해로 가자 (1) |김선화|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/linear_algebra/lu/LU%20%EB%B6%84%ED%95%B4_%EB%B6%80%EA%B0%80.pdf)||
||(수리통계학) 제4장 특수한 확률분포들|이상열|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/statistics/ch4/distribution.html)||
||(네트워크) Ch4. Social Networks |조응태|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part5/graph/ch04/Ch04%20-%20Social%20Networks%20(rev.%202).ipynb)||

## Part4 커리큘럼
|요일   |주제   |발표자   |발표자료   |   |
|---|---|---|---|---|
|2017.05.17(수)|(R 중급) 8/9/10 Workflow : projects ~ Tibbles |김승욱|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170517/R4DS_8_9_10_김승욱_170518.pptx)
||(Python) Data Wrangling & Aggreagtion |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Python/etc.04-Data%20Wrangling.ipynb)
||(Network) 1 Food Webs (2)|이상열   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170517/1.%20Food%20Webs.ipynb)
||(Shiny) Use reactive expressions |김승욱   |[발표자료](https://github.com/KaggleBreak/analyticstool/tree/master/part4/R/shiny/20170517)
||~~(웹 머신러닝) 4장 웹 마이닝 기법~~ |~~정광윤~~ |
||(베이지안) Course Overview * Statistical modeling and Monte Carlo estimation |이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/review/Bayesian%20Statistics%20Course%20Overview.ipynb)
|2017.05.24(수)|(R 중급) 11/12 Data Import ~ Tidy Data |정용환|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170524/Study_Part4_Import_Tidy.html)
||(Network) 2 International Trade Networks and World Trade Web (1) |김가경 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170524/2.%20International%20Trade%20Networks%20and%20World%20Trade%20Web.ipynb)
||(베이지안) Statistical modeling and Monte Carlo estimation (1)|이상열|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/review/Bayesian%20Statistics%20Course%20Overview.ipynb)
||(웹 머신러닝) 4장 웹 마이닝 기법(1) |정광윤|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/WebML/20170524/chapter04_web_mining.ipynb)
|2017.05.31(수)|(R 중급) 11/12 Data Import ~ Tidy Data |정용환|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170524/Study_Part4_Import_Tidy.html)
||(Network) 2 International Trade Networks and World Trade Web (2) |김가경|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170524/2.%20International%20Trade%20Networks%20and%20World%20Trade%20Web.ipynb)
||(베이지안) 2. Markov chain Monte Carlo (MCMC)|이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/week2/week2_mcmc.ipynb)
||(웹 머신러닝) 4장 웹 마이닝 기법(2) |정광윤|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/WebML/20170524/chapter04_web_mining.ipynb)
|2017.06.14(수)|(R 중급) 13/14 Relational data & Strings|권미현 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170614/170614_relational_data_strings.html)
||(Network) (Network) 3. The Internet Network (1) |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170614/III%20Internet/03%20-%20The%20Internet%20Network.ipynb)
||(웹 머신러닝) 5장 추천시스템 (1)|김가경 |
|2017.06.21(수)|(R 중급) 15/16 Factors/Dates and times|이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170621/15_16_Factors_Dates.ipynb)
||(Network) (Network) 3. The Internet Network (1) |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170614/III%20Internet/03%20-%20The%20Internet%20Network.ipynb)
||(베이지안) 3. Common statistical models (1)|이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/week3/week3_common.ipynb)
||(웹 머신러닝) 5장 추천시스템 (1)|김가경 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/WebML/20170621/5.%20%EC%B6%94%EC%B2%9C%20%EC%8B%9C%EC%8A%A4%ED%85%9C%20(Recommendation%20system).ipynb)
|2017.06.28(수)|(R 중급) 17/18/19 Introductions/pipe/Functions|이규영 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170628/kaggle%20Study.pptx)
||(Network) 4, World Wide Web, Wikipedia, and Social Networks(1) |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170628/IV%20WWW%2C%20Wikipedia%2C%20and%20Social%20Networks/4.%20WWW%2C%20Wikipedia%2C%20and%20Social%20Networks.ipynb)
||(베이지안) 3. Common statistical models (2)|이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/week3/week3_common.ipynb)
|2017.07.05(수)|(R 중급) 20 Vectors |심상진 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170705/VectorsAndIteratorsI.pptx)
||(웹 머신러닝) 6장 장고 시작 |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/tree/master/part4/WebML/20170705)
||(베이지안) 3. Common statistical models (3)|이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/week3/week3_common.ipynb)
|2017.07.12(수)|(R 중급) 21 Iterations |심상진 |
||(R 중급) 22/23 Intro/Model Basics |정용환 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/R/analytics/20170712/ModelBasics.pptx)
||(Network) 5. Financial Networks |조응태 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170712/V%20Financial%20Networks.ipynb)
||(놀러온 특강) 최적화 |심상진 |
|2017.07.19(수)|(R 중급) 24/25 Model building/Many Models |이상열|[발표자료](https://github.com/KaggleBreak/analyticstool/tree/master/part4/R/analytics/20170719)
||(웹 머신러닝) 영화 추천 시스템 웹 애플리케이션 |이상열 |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/WebML/20170719/myvenv/movie-time/movie_time_investigation.ipynb)
|2017.07.26(수)|(Network) 6. Modeling |조응태|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Network/20170726/VI%20Modelling/6.%20Modelling.ipynb)
||(베이지안) 4. Count data and hierarchical modeling |심상진|[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part4/Bayesian/week4/week4_Techniques%20and%20Models.ipynb)

## Part3 커리큘럼
|요일   |주제   |발표자   |발표자료   |   |
|---|---|---|---|---|
|2017.03.27(월)|(R 기초) 3 Data visualisation   |송영숙   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0327/3%20visualisation-20170328T065654Z-001/3%20visualisation/R%20for%20data%20science%203%20Data%20visualisation.ipynb)   |   |
||(Python 기초) A Byte of Python 1~9장 기초 설명   |이상열   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/byteofpython/A%20Byte%20of%20Python%201_9.ipynb)  |   |
||(R 중급) LESSON1 Welcome to Shiny   |김승욱   |[발표자료](https://encaion.shinyapps.io/shiny_color_picker_170327/)    |   |
||(Python 중급) 장고걸스 튜토리얼 1   |이상열   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/Django/Django%20Girls%20Tutorial%201.ipynb)   |   |
|2017.04.03(월)|(R 기초) 4 Workflow : basics   |김태영   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0403_basics/basics.pptx)   |   |
||(Python 기초) A Byte of Python 1~9장 기초 설명   |이상열   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/byteofpython/A%20Byte%20of%20Python%201_9.ipynb)  |   |
||(R 중급) Layout the user interface   |이상열  |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0403/LESSON%202_Build%20a%20user-interface.ipynb) ||
||(Python 중급) 장고걸스 튜토리얼 2 (Django 모델, 관리자, 배포하기)  |정원석   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/Django/Django%20Girls%20Tutorial2.pdf)   |   |
|2017.04.10(월)|(R 기초) 5. Data transformation   |김지현   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0410_transformation/5_data_transformation.pdf)   |   |
||(Python 기초) A Byte of Python 10~14장 요약   |송영숙   |[발표자료](ithub.com/KaggleBreak/analyticstool/blob/master/part3/Python/byteofpython/byteofpython1014.ipynb) |   |
||(R 중급) Add control widgets   |김승욱  |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0410_widgets/all_buttons_in_shiny.R) ||
||(Python 중급) 장고걸스 튜토리얼 3 (Django URL, 뷰만들기, HTML 시작하기)  |신민정   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/Django/django_mtv.pdf)  |   |
|2017.04.17(월)|(R 기초) 6. Workflow : scripts (R 기초) 7. Exploratory Data Analysis   |조희주   |   |   |
||(Python) numpy / pandas 기초   |조응태   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/nppd/etc%20-%201.%20NumPy%20Basics.ipynb)   |   |
||(Network) 1 Food Webs  |이상열   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Network/book_1/1.%20Food%20Webs.ipynb)   |   |
||(R 중급) Display reactive output   |김승욱   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0417_Reactive/ui.R)   |   |
||(Python 중급) 장고걸스 튜토리얼 4 (Django ORM과 QuerySets, 템플릿의 동적 데이터, Django 템플릿)   |유민규   |   |   |
|2017.04.24(월)|(R 기초) 6. Workflow : scripts (R 기초) 7. Exploratory Data Analysis |조희주   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/R/0424_EDA/6-7.Workflow-EDA.pptx)   |   |
||(Python) pandas 기초  |조응태   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/nppd/etc%20-%202.%20Getting%20Started%20with%20pandas.ipynb)  |   |
||(R 중급) Use R scripts and data  |김승욱   |[발표자료](https://github.com/KaggleBreak/analyticstool/tree/master/part3/R/0424_census)   |   |
||(Python 중급)  장고걸스 튜토리얼 5 (CSS 예쁘게 만들기, 템플릿 확장하기, 어플리케이션 확장하기, Django 폼 |정광윤   |[발표자료](https://github.com/KaggleBreak/analyticstool/blob/master/part3/Python/Django/template_and_django_form.pptx)   |   |

analyticstool

analyticstool 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

analyticstool

analyticstool 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README