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.
이 강좌는 Introduction to Computational Statistics for Data Scientists 특화 과정의 일부입니다.
제공자:

이 강좌에 대하여
Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
Knowledge of Jupyter Notebooks will be beneficial.
배울 내용
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.
귀하가 습득할 기술
- Bayesian Inference
- visualization
- Python Programming
- Scipy
- Statistics
Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
Knowledge of Jupyter Notebooks will be beneficial.
제공자:

Databricks
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강의 계획표 - 이 강좌에서 배울 내용
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.
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.
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.
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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INTRODUCTION TO BAYESIAN STATISTICS의 최상위 리뷰
Content/notes wise this course is great, But teaching style needs to be improved. Rather than reading the notes instructor should teach by giving examples and driving some of the results.
This course would be a bit hard for "complete" beginners, but would be enough for people who wish to refresh knowledge about Bayesian inference and stuff. The notes and codes are very good!!
Introduction to Computational Statistics for Data Scientists 특화 과정 정보
The purpose of this series of courses is to teach the basics of Computational Statistics for the purpose of performing inference to aspiring or new Data Scientists. This is not intended to be a comprehensive course that teaches the basics of statistics and probability nor does it cover Frequentist statistical techniques based on the Null Hypothesis Significance Testing (NHST). What it does cover is:

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