Este curso forma parte de Programa especializado: Introduction to Computational Statistics for Data Scientists

3.3
estrellas
24 calificaciones

Dr. Srijith Rajamohan

2545 ya inscrito

ofrecido por

Programa especializado: Introduction to Computational Statistics for Data ScientistsDatabricks

Acerca de este Curso

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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.
The instructors for this course will be Dr. Srijith Rajamohan and Dr. Robert Settlage.

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Curso 1 de 3 en

Programa especializado: Introduction to Computational Statistics for Data Scientists

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.

Curso 1 de 3 en

Programa especializado: Introduction to Computational Statistics for Data Scientists

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)

Instructor

Calificación del instructor

3,25/5 (13 calificaciones)

Dr. Srijith Rajamohan

Sr. Developer Advocate (Data Science)

3543 alumnos

3 cursos

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

Semana

Semana 1

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

Semana

Semana 2

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

17 videos

Introductions1m

Chance regularities and random processes6m

Outcomes, events and spaces8m

Addition rules of probability6m

Multiplication rules of probability5m

Conditional probability, Random Variables and Experiments8m

Random Variables and Distributions12m

Moments, mean and variance10m

Joint distributions of Random Variables5m

Estimation using MoM and MLE9m

Basics of Bayes' Rule7m

Decisions and Loss Functions11m

Priors introduction5m

Priors as conjugates2m

Informative vs non-informative priors3m

Jeffrey's Prior2m

Prior distributions and posterior ramifications12m

7 lecturas

Introduction and references2m

Rules for manipulating probability10m

Random variables10m

MoM and MLE10m

Bayes' and decisions10m

Loss functions10m

Semana 3

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

12 videos

The Binomial Distribution5m

Negative Binomial Distribution4m

Poisson Distribution3m

Exponential Distribution2m

Gamma Distribution3m

Normal Distribution3m

Lognormal Distribution2m

Student's t-distribution5m

Beta Distribution4m

MLE Estimation using a Beta Distribution 4m

Gaussian Mixture Model5m

Non-parametric Methods: Kernel Density Estimation3m

2 lecturas

Reference1h

2 ejercicios de práctica

Common distributions30m

Non-parametric methods20m

Semana

Semana 4

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

Reseñas

3.3

11 reseña

5 stars
33,33 %
4 stars
14,81 %
3 stars
25,92 %
2 stars
11,11 %
1 star
14,81 %

Principales reseñas sobre INTRODUCTION TO BAYESIAN STATISTICS

por MG13 de ago. de 2021

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!!

por TS25 de feb. de 2022

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.

Ver todas las reseñas

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:

The basics of Bayesian statistics and probability Understanding Bayesian inference and how it works The bare-minimum set of tools and a body of knowledge required to perform Bayesian inference in Python, i.e. the PyData stack of NumPy, Pandas, Scipy, Matplotlib, Seaborn and Plot.ly A scalable Python-based framework for performing Bayesian inference, i.e. PyMC3 With this goal in mind, the content is divided into the following three main sections (courses). Introduction to Bayesian Statistics - The attendees will start off by learning the the basics of probability, Bayesian modeling and inference in Course 1. Introduction to Monte Carlo Methods - This will be followed by a series of lectures on how to perform inference approximately when exact calculations are not viable in Course 2. PyMC3 for Bayesian Modeling and Inference - PyMC3 will be introduced along with its application to some real world scenarios. The lectures will be delivered through Jupyter notebooks and the attendees are expected to interact with the notebooks.

Introduction to Computational Statistics for Data Scientists

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Introduction to Bayesian Statistics