- Bayesian Inference
- Python Programming
- MCMC
- PyMC3
- Scipy
- visualization
- Statistics
- Bayesian
- Scikit-Learn
- Monte Carlo Method
Introduction to Computational Statistics for Data Scientists専門講座
Practical Bayesian Inference. A conceptual understanding of the techniques and the tools used to perform scalable Bayesian inference in practice with PyMC3.
提供：
学習内容
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.
習得するスキル
この専門講座について
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.
応用学習プロジェクト
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.
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
初級レベル
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
約3ヶ月で完了
4時間/週の推奨ペース
英語
字幕：英語
共有できる証明書
修了時に証明書を取得
100%オンラインコース
自分のスケジュールですぐに学習を始めてください。
フレキシブルなスケジュール
柔軟性のある期限の設定および維持
初級レベル
- Some experience with Data Science using the PyData Stack of NumPy, Pandas, Scikit-learn
- Fundamentals of linear algebra and calculus
約3ヶ月で完了
4時間/週の推奨ペース
英語
字幕：英語
専門講座の仕組み
コースを受講しましょう。
Courseraの専門講座は、一連のコース群であり、技術を身に付ける手助けとなります。開始するには、専門講座に直接登録するか、コースを確認して受講したいコースを選択してください。専門講座の一部であるコースにサブスクライブすると、自動的にすべての専門講座にサブスクライブされます。1つのコースを修了するだけでも結構です。いつでも、学習を一時停止したり、サブスクリプションを終了することができます。コースの登録状況や進捗を追跡するには、受講生のダッシュボードにアクセスしてください。
実践型プロジェクト
すべての専門講座には、実践型プロジェクトが含まれています。専門講座を完了して修了証を獲得するには、成功裏にプロジェクトを終了させる必要があります。専門講座に実践型プロジェクトに関する別のコースが含まれている場合、専門講座を開始するには、それら他のコースをそれぞれ終了させる必要があります。
修了証を取得
すべてのコースを終了し、実践型プロジェクトを完了すると、修了証を獲得します。この修了証は、今後採用企業やあなたの職業ネットワークと共有できます。
この専門講座には3コースあります。
Introduction to Bayesian Statistics
22件の評価
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
10件の評価
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
10件の評価
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.
提供：
データプリックス
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よくある質問
返金ポリシーについて教えてください。
1つのコースだけに登録することは可能ですか？
学資援助はありますか？
無料でコースを受講できますか？
このコースは100%オンラインで提供されますか？実際に出席する必要のあるクラスはありますか？
専門講座を修了するのにどのくらいの期間かかりますか？
What background knowledge is necessary?
Do I need to take the courses in a specific order?
専門講座を修了することで大学の単位は付与されますか？
What will I be able to do upon completing the Specialization?
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