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.
Este curso forma parte de Programa especializado: Introduction to Computational Statistics for Data Scientists
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Acerca de este Curso
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Fechas límite flexibles
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel principiante
Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
Knowledge of Jupyter Notebooks will be beneficial.
Aprox. 13 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Qué aprenderás
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.
Habilidades que obtendrás
- Bayesian Inference
- visualization
- Python Programming
- Scipy
- Statistics
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel principiante
Some experience with Data Science using the PyData Stack of NumPy, SciPy, Pandas, Scikit-learn.
Knowledge of Jupyter Notebooks will be beneficial.
Aprox. 13 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Databricks
Databricks is the data and AI company. Founded by the creators of Apache Spark™, Delta Lake and MLflow, organizations like Comcast, Condé Nast, Nationwide and H&M rely on Databricks’ open and unified platform to enable data engineers, scientists and analysts to collaborate and innovate faster.
Programa - Qué aprenderás en este curso
21 minutos para completar
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.
21 minutos para completar
4 videos (Total 11 minutos), 1 lectura
6 horas para completar
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.
6 horas para completar
17 videos (Total 119 minutos), 7 lecturas, 12 cuestionarios
4 horas para completar
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.
4 horas para completar
12 videos (Total 49 minutos), 2 lecturas, 2 cuestionarios
3 horas para completar
Sampling Algorithms
This module introduces you to various sampling algorithms for generating distributions. You will also be introduced to Python code that performs sampling.
3 horas para completar
6 videos (Total 32 minutos), 2 lecturas, 3 cuestionarios
Acerca de Programa especializado: Introduction to Computational Statistics for Data Scientists
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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