This course is part of the Introduction to Computational Statistics for Data Scientists Specialization

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Dr. Srijith Rajamohan

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Introduction to Computational Statistics for Data Scientists SpecializationDatabricks

About this Course

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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.
The instructor for this course will be Dr. Srijith Rajamohan.

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Course 2 of 3 in the

Introduction to Computational Statistics for Data Scientists Specialization

Beginner Level

1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.

2. Course 1 in this Specialization.

Approx. 14 hours to complete

English

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What you will learn

1. Markov Chain Monte Carlo algorithms
2. Implementing the above in Python
3. Assess the performance of Bayesian models

Skills you will gain

Bayesian
Scipy
Scikit-Learn
MCMC

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 2 of 3 in the

Introduction to Computational Statistics for Data Scientists Specialization

Beginner Level

1. Experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.

2. Course 1 in this Specialization.

Approx. 14 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

1.33/5 (6 Ratings)

Dr. Srijith Rajamohan

Sr. Developer Advocate (Data Science)

4,364 Learners

3 Courses

Offered by

Databricks

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Syllabus - What you will learn from this course

Week1

Week 1

5 hours to complete

Topics in Model Performance

This module gives an overview of topics related to assessing the quality of models. While some of these metrics may be familiar to those with a Machine Learning background, the goal is to bring awareness to the concepts rooted in Information Theory. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/BayesianInference.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

5 hours to complete

13 videos (Total 31 min), 5 readings, 1 quiz

13 videos

Welcome to Course 2!44sIntroduction1mUnderfitting and Overfitting2mExplained Variance2mCross Validation1mInformation Criteria1mLog-likelihood and Deviance3mPosterior Predictive Distribution1mAIC, BIC, DIC and WAIC4mA qualitative discussion of the various metrics1mEntropy3mKL Divergence2mModel Averaging3m

5 readings

What can you expect from this course/specialization?10mLikelihood and its use in Parameter Estimation and Model Comparison1hUnderstanding predictive information criteria for Bayesian models2hInformation Theory and Statistics1hModel Stacking10m

1 practice exercise

Topics in Model Performance30m

Week2

Week 2

5 hours to complete

The Metropolis Algorithms for MCMC

This module serves as a gentle introduction to Markov-Chain Monte Carlo methods. The general idea behind Markov chains are presented along with their role in sampling from distributions. The Metropolis and Metropolis-Hastings algorithms are introduced and implemented in Python to help illustrate their details. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

5 hours to complete

8 videos (Total 29 min), 1 reading, 1 quiz

Week3

Week 3

4 hours to complete

Gibbs Sampling and Hamiltonian Monte Carlo Algorithms

This module is a continuation of module 2 and introduces Gibbs sampling and the Hamiltonian Monte Carlo (HMC) algorithms for inferring distributions. The Gibbs sampler algorithm is illustrated in detail, while the HMC receives a more high-level treatment due to the complexity of the algorithm. Finally, some of the properties of MCMC algorithms are presented to set the stage for Course 3 which uses the popular probabilistic framework PyMC3. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/MonteCarlo.html#gibbs-sampling.

4 hours to complete

7 videos (Total 28 min), 2 readings, 1 quiz

About the Introduction to Computational Statistics for Data Scientists Specialization

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.

Introduction to Computational Statistics for Data Scientists

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Bayesian Inference with MCMC

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