Introduction to Bayesian Statistics

Go to Course: https://www.coursera.org/learn/compstatsintro

Introduction

**Course Review: Introduction to Bayesian Statistics on Coursera** If you're an aspiring data scientist looking to deepen your understanding of statistics, particularly Bayesian statistics, the **Introduction to Bayesian Statistics** course on Coursera is an excellent starting point. This course serves as the first step in a comprehensive three-course specialization, designed to provide learners with the foundational knowledge and skills needed to apply Bayesian methods in data analysis and modeling. ### Overview The main objective of this course is to introduce students to the essential concepts of Computational Statistics, with a strong emphasis on Bayesian modeling and inference. What sets this course apart is its hands-on approach, utilizing Python and Jupyter notebooks for practical exercises. Whether you are new to data science or hoping to refresh your knowledge, this course is structured to cater to your learning needs. You can find more details on the course website: [Introduction to Computational Statistics](https://sjster.github.io/introduction_to_computational_statistics/docs/index.html). ### Course Structure The course is organized into several engaging modules that cover crucial aspects of Bayesian statistics and provide practical coding experience: 1. **Environment Setup**: - The course begins by familiarizing students with the computational environment used throughout the specialization. You will learn to navigate the Databricks Ecosystem for Data Science, which is a powerful platform for working on data. Additionally, students have the option to use Binder for setup-free access to the notebooks, enhancing flexibility in how they engage with the course materials. 2. **Introduction to the Fundamentals of Probability**: - This module lays the groundwork for understanding probability and its significance in statistics. Here, you will explore key terms and concepts that are vital for grasping more complex Bayesian principles later in the course. This foundational knowledge is essential for any aspiring data scientist. 3. **A Hands-On Introduction to Common Distributions**: - In this module, you'll dive into different probability distributions commonly used in statistics. The hands-on experience with Python coding is particularly valuable, as you will learn to generate, plot, and interact with various distributions. The focus on Maximum Likelihood Estimation (MLE) and Kernel Density Estimation (KDE) equips students with practical techniques that can be applied in real-world scenarios. 4. **Sampling Algorithms**: - The course introduces various sampling algorithms necessary for generating distributions and Bayesian inference. You'll delve into the Python code that facilitates sampling, which forms the backbone of many statistical methods. Understanding these concepts is crucial for performing effective Bayesian analysis. ### Recommendation I wholeheartedly recommend the **Introduction to Bayesian Statistics** course for anyone looking to build a strong foundation in Bayesian methods. The combination of theoretical knowledge and hands-on practice ensures that students develop both the understanding and the skills necessary to succeed in the field. The use of Python and Jupyter notebooks enriches the learning experience, as Python is one of the most widely used programming languages in data science. Moreover, the supportive online community and accessible course structure make it easier for learners from various backgrounds to engage with the material effectively. As a first course in the specialization, it paves the way for further exploration into more advanced Bayesian techniques and applications. Whether you're preparing for a career in data science or seeking to enhance your existing knowledge, this course is a great investment in your professional development. Embarking on this journey will not only bolster your statistical expertise but also prepare you for the practical challenges faced in data science today. Don't miss out on the opportunity to advance your skills in this critical area!

Syllabus

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.

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

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.

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