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

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stars
16 ratings

Dr. Srijith Rajamohan

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Offered By

Introduction to Computational Statistics for Data Scientists SpecializationDatabricks

About this Course

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

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Course 3 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 & 2 in this Specialization.

Approx. 11 hours to complete

English

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What you will learn

1. The PyMC3/ArViz framework for Bayesian modeling and inference
2. Build real-world models using PyMC3 and assess the quality of your models

Skills you will gain

Bayesian Inference
Python Programming
Monte Carlo Method
PyMC3
Scipy

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.

Course 3 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 & 2 in this Specialization.

Approx. 11 hours to complete

English

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Instructor

Dr. Srijith Rajamohan

Sr. Developer Advocate (Data Science)

4,413 Learners

3 Courses

Offered by

Databricks

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Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Introduction to PyMC3 - Part 1

This module serves as an introduction to the PyMC3 framework for probabilistic programming. It introduces some of the concepts related to modeling and the PyMC3 syntax. The visualization library ArViz, that is integrated into PyMC3, will also be introduced. The course website is https://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

2 hours to complete

12 videos (Total 48 min), 3 readings, 1 quiz

12 videos

Welcome to Course 3!46sProbabilistic Programming with PyMC35mAn introduction to PyMC3 5mInference with PyMC34mComposition of Distributions1mHPD, HDI and ROPE4mCredible and Confidence Intervals3mModeling with a Gaussian Distribution6mPosterior Predictive Checks3mRobust Models3mHierarchical Models6mShrinkage in Hierarchical Models4m

3 readings

What can you expect from this course/specialization?10mProbabilistic Programming Frameworks30mPlate Notation30m

1 practice exercise

PyMC3 - I30m

Week2

Week 2

1 hour to complete

Introduction to PyMC3 - Part 2

This module will teach the basics of using PyMC3 to solve regression and classification problems using PyMC3. It will also show how to deal with outliers in your data and create hierarchical models. Finally, a case study is presented to help apply everything that was learned in Module 1 and 2. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#linear-regression-again. Instructions to download and run the notebooks are at https://sjster.github.io/introduction_to_computational_statistics/docs/Production/getting_started.html

1 hour to complete

14 videos (Total 47 min)

14 videos

Linear Regression3mMean-centering for Linear Regression2mRobust Linear Regression2mHierarchical Linear Regression5mPolynomial Linear Regression3mMultiple Linear Regression2mLogistic Regression2mLogistic Regression with PyMC36mDecision Boundary for Classification1mMultiple Logistic Regression2mMulticlass Logistic Regression2mCase Study with PyMC3 - I2mCase Study with PyMC3 - II5mCase Study with PyMC3 - III2m

1 practice exercise

PyMC3 - II30m

Week3

Week 3

5 hours to complete

Metrics in PyMC3

This module introduces various measures and metrics to assess the quality of the solutions inferred using PyMC3. Hands-on examples are used to illustrate how various methods and visualizations can be used in PyMC3. Finally, a brief overview of how to debug PyMC3 algorithms is provided. The course website ishttps://sjster.github.io/introduction_to_computational_statistics/docs/Production/PyMC3.html#mcmc-metrics. 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

11 videos (Total 28 min), 3 readings, 1 quiz

11 videos

Introduction to Metrics and Tuning2mMetropolis and HMC2mMixing and Potential Scale Reduction Factor1mCentered and Non-centered Parameterization5mAssess convergence in PyMC32mForest plots for visualization1mAutocorrelation and Effective Sample Size2mMonte Carlo error and Divergences3mDiagnosing issues in PyMC32mDiagnosing issues in PyMC3 with the multiclass classification problem3mDebugging in PyMC353s

3 readings

Visualization in Bayesian Workflow1h 30mTuning1hImproved Rhat1h 30m

1 practice exercise

PyMC3 - III30m

Week4

Week 4

3 hours to complete

Modeling of COVID-19 cases using PyMC3

3 hours to complete

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TOP REVIEWS FROM INTRODUCTION TO PYMC3 FOR BAYESIAN MODELING AND INFERENCE

by GMSep 14, 2022

Great capstone providing useful practice and tools for applying the concepts.

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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

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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Introduction to PyMC3 for Bayesian Modeling and Inference

About this Course

What you will learn

Skills you will gain