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.
