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Computational Methods for Data Analysis 본문

Ph.D Course/Moocs

Computational Methods for Data Analysis

지식 연주가 Knowledge Designer 2015.01.13 23:17

Computational Methods for Data Analysis  <Coursera>


https://www.coursera.org/course/compmethods


Northwestern University 에서 응용수학으로 Ph.D 를 받은 Dr. J. Nathan Kutz 가 진행하는 강의. 데이터 분석을 위한 컴퓨터 툴 안내 정도 되겠네요.


강의소개에는 <In this course you will learn how to recognize and solve numerically practical problems which may arise in your research. We will solve some serious problems using the full power of MATLAB's built in functions and routines. This class is geared for those who need to get the basics in scientific computing methods for data analysis. Many of today's major research methods for exploring data analysis will be covered: signal processing, frequency filtering, time-frencency analysis, wavelets, principal component analysis, proper orthogonal decomposition, empirical mode decomposition etc. Applications will range from image processing to characterizing atmospheric dynamics.> 라고 나와있는데, 요약하자면 데이터 분석을 위한 주요 디지털 리서치 툴을 익히는 강의라고 합니다.


Course Outline 은 아래 보시는 바와 같습니다.


1. Review of Statistics: (1 week)
We will begin with a brief review of statistical methods. The principles of statistics will be largely applied in a computational context for extracting meaningful information from data.

    - mean, variance, moments
    - probability distributions
    - significance testing, hypothesis testing

2, Spectral and Time-Frequency Analysis: (4 weeks)
We will introduce the ideas of signal processing, filtering, time-frequency representations including wavelet expansions. Our application will be largely to problems in image processing, denoising and noise reduction.

    - digital signal processing
    - noise reduction and filtering
    - image processing and face recognition
    - time-frequency methods and wavelets

3. Objective Analysis Techniques: (5 weeks)
These methods are practical attempts to reduce the dimensionality of the data as well as infer statistically meaningful trends in what otherwise appears to be noisy data.

    - Principal Component Analysis (PCA)
    - Proper Orthogonal Decomposition (POD)
    - Emperical Mode Decomposition (EMD)
    - Singular Value Decomposition (SVD)


* 이 강의는 친절하게도 지나간 강의 영상을 모두 다운로드 할 수 있게 되어있으니, 스토리지에 저장해놓고 공부하는 것도 효율적일 수 있을 듯 보입니다.


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