About this Course

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Beginner Level

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

2. Course 1 in this Specialization.

Approx. 15 hours to complete
English

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

BayesianScipyScikit-LearnMCMC
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Beginner Level

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

2. Course 1 in this Specialization.

Approx. 15 hours to complete
English

Offered by

Placeholder

Databricks

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Topics in Model Performance

5 hours to complete
13 videos (Total 31 min), 5 readings, 1 quiz
Week
2

Week 2

5 hours to complete

The Metropolis Algorithms for MCMC

5 hours to complete
8 videos (Total 29 min), 1 reading, 1 quiz
Week
3

Week 3

4 hours to complete

Gibbs Sampling and Hamiltonian Monte Carlo Algorithms

4 hours to complete
7 videos (Total 28 min), 2 readings, 1 quiz

About the Introduction to Computational Statistics for Data Scientists Specialization

Introduction to Computational Statistics for Data Scientists

Frequently Asked Questions

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