- Bayesian Inference
- Python Programming
- MCMC
- PyMC3
- Scipy
- visualization
- Statistics
- Bayesian
- Scikit-Learn
- Monte Carlo Method
Introduction to Computational Statistics for Data Scientists 특화 과정
Practical Bayesian Inference. A conceptual understanding of the techniques and the tools used to perform scalable Bayesian inference in practice with PyMC3.
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배울 내용
The basics of Bayesian modeling and inference.
A conceptual understanding of the techniques used to perform Bayesian inference in practice.
Learn how to use PyMC3 to solve real-world problems.
The basics of Probability, Bayesian statistics, modeling and inference.
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The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:
The basics of Bayesian statistics and probability
Understanding Bayesian inference and how it works
The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly
A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3
With this goal in mind, the content is divided into the following three main sections (courses).
Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1.
Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2.
PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios.
The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.
응용 학습 프로젝트
Implement Distributions in Python and visualize it statically using Matplotlib or Seaborn and interactively using Plot.ly.
Implement Monte Carlo Sampling algorithms in Python.
Learn the basics of PyMC3 for various Bayesian modeling including Linear Regression, Hierarchical Regression, Classification, Robust models and assessing the quality of models.
Use PyMC3 to model the disease dynamics of and infer the parameters of an SIR model of COVID-19 from real-world data.
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완료 시 수료증 획득
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지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 일정
유연한 마감을 설정하고 유지 관리합니다.
초급 단계
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
완료하는 데 약 3개월이 걸립니다
권장 학습 속도: 주 4시간
영어
자막: 영어
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인 강좌
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
유연한 일정
유연한 마감을 설정하고 유지 관리합니다.
초급 단계
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
완료하는 데 약 3개월이 걸립니다
권장 학습 속도: 주 4시간
영어
자막: 영어
특화 과정 이용 방법
강좌 수강
Coursera 특화 과정은 한 가지 기술을 완벽하게 습득하는 데 도움이 되는 일련의 강좌입니다. 시작하려면 특화 과정에 직접 등록하거나 강좌를 둘러보고 원하는 강좌를 선택하세요. 특화 과정에 속하는 강좌에 등록하면 해당 특화 과정 전체에 자동으로 등록됩니다. 단 하나의 강좌만 수료할 수도 있으며, 학습을 일시 중지하거나 언제든 구독을 종료할 수 있습니다. 학습자 대시보드를 방문하여 강좌 등록 상태와 진도를 추적해 보세요.
실습 프로젝트
모든 특화 과정에는 실습 프로젝트가 포함되어 있습니다. 특화 과정을 완료하고 수료증을 받으려면 프로젝트를 성공적으로 마쳐야 합니다. 특화 과정에 별도의 실습 프로젝트 강좌가 포함되어 있는 경우, 다른 모든 강좌를 완료해야 프로젝트 강좌를 시작할 수 있습니다.
수료증 취득
모든 강좌를 마치고 실습 프로젝트를 완료하면 취업할 때나 전문가 네트워크에 진입할 때 제시할 수 있는 수료증을 취득할 수 있습니다.
이 전문 분야에는 3개의 강좌가 있습니다.
Introduction to Bayesian Statistics
The objective of this course is to introduce Computational Statistics to aspiring or new data scientists. The attendees will start off by learning the basics of probability, Bayesian modeling and inference. This will be the first course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Bayesian Inference with MCMC
The objective of this course is to introduce Markov Chain Monte Carlo Methods for Bayesian modeling and inference, The attendees will start off by learning the the basics of Monte Carlo methods. This will be augmented by hands-on examples in Python that will be used to illustrate how these algorithms work. This will be the second course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
Introduction to PyMC3 for Bayesian Modeling and Inference
The objective of this course is to introduce PyMC3 for Bayesian Modeling and Inference, The attendees will start off by learning the the basics of PyMC3 and learn how to perform scalable inference for a variety of problems. This will be the final course in a specialization of three courses .Python and Jupyter notebooks will be used throughout this course to illustrate and perform Bayesian modeling with PyMC3.. The course website is located at https://sjster.github.io/introduction_to_computational_statistics/docs/index.html. The course notebooks can be downloaded from this website by following the instructions on page https://sjster.github.io/introduction_to_computational_statistics/docs/getting_started.html.
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