Introduction to Bayesian Statistics (Coursera)

Introduction to Bayesian Statistics (Coursera)
Course Auditing
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S​ome experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. Knowledge of Jupyter Notebooks will be beneficial.
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Introduction to Bayesian Statistics (Coursera)
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.

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What You Will 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.


Course 1 of 3 in the Introduction to Computational Statistics for Data Scientists Specialization


Syllabus


WEEK 1

Environment Setup

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.


WEEK 2

Introduction to the Fundamentals of Probability

In this module, you will learn the foundations of probability and statistics. The focus is on gaining familiarity with terms and concepts.


WEEK 3

A Hands-On Introduction to Common Distributions

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.


WEEK 4

Sampling Algorithms

This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.



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Course Auditing
41.00 EUR/month
S​ome experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn. Knowledge of Jupyter Notebooks will be beneficial.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.