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.
Introduction to Bayesian Statistics
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Introduction to Bayesian Statistics
This course is part of Introduction to Computational Statistics for Data Scientists Specialization
Instructor: Dr. Srijith Rajamohan
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4.2
(88 reviews)
Beginner level
Recommended experience
12 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
The basics of Probability, Bayesian statistics, modeling and inference.
You will also get a hands-on introduction to using Python for computational statistics using Scikit-learn, SciPy and Numpy.
Skills you'll gain
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Assessments
17 assignments
Taught in English
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This course is part of the Introduction to Computational Statistics for Data Scientists Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
Introduction to the compute environment for the Specialization. The users will be introduced to the Databricks Ecosystem for Data Science. The users can also deploy the notebooks to Binder for setup-free access.
What's included
4 videos1 reading
In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.
What's included
17 videos7 readings12 assignments
Tis module will be an introduction to common distributions along with the Python code to generate, plot and interact with these distributions. You will also learn how to perform Maximum Likelihood Estimation (MLE) for various distributions and Kernel Density Estimation (KDE) for non-parametric distributions.
What's included
12 videos2 readings2 assignments
This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.
What's included
6 videos2 readings3 assignments
Instructor
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4.1 (39 ratings)Offered by
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