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

  • 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.
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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