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Statistical Learning

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data analysis. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R by James, Witten, Hastie and Tibshirani (Springer, 2013). As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website.

Probability and Statistics – Self-Paced

AN INCLUSIVE AND SUPPORTIVE ENVIRONMENT TO LEARN

This course aims to provide an accessible, inclusive, and supportive space to learn. Anyone can enroll for free from anywhere in the world. All learners, no matter what their gender, ethnicity, or socio-economic status, can be successful in this course. college classes online The primary goal is not to test and evaluate learners, but to offer a supportive environment to learn more about probability and statistics. Welcome!

ABOUT PROBABILITY AND STATISTICS

This course is self-paced and is provided free of charge. There are no due dates, and participants are welcome to work through as much or as little of the material as they wish. There is no instructor involved, and no credit, Statement of Accomplishment, or any type of verification or certification of completion is given. The course is simply here for people who want to learn more about Statistics.

THE CONTENT

The Probability and Statistics course contains four main units that have several sections within each unit.

Exploratory Data Analysis: This unit is organized into two sections – Examining Distributions and Examining Relationships. The general approach is to provide participants with a framework that will help them choose the appropriate descriptive methods in various data analysis situations.

Producing Data: This unit is organized into two sections – Sampling and Designing Studies.

Probability: In this course the unit is a classical treatment of probability and includes basic probability principles, finding probability of events, conditional probability, discrete random variables (including the Binomial distribution) and continuous random variables (with emphasis on the normal distribution). The probability unit culminates in a discussion of sampling distributions that is grounded in simulation. For a streamlined version of probability that forgoes the classical treatment of probability in favor of an empirical approach using relative frequency, participants may see the OLI Statistical Reasoning course.

Inference: This unit introduces participants to the logic as well as the technical side of the main forms of inference: point estimation, interval estimation and hypothesis testing. The unit covers inferential methods for the population mean and population proportion, inferential methods for comparing the means of two groups and of more than two groups (ANOVA), the Chi-Square test for independence and linear regression. The unit reinforces the framework that the participants were introduced to in the Exploratory Data Analysis for choosing the appropriate, in this case, inferential method in various data analysis scenarios.

Throughout the course there are many interactive elements. These include: simulations, “walk-throughs” that integrate voice and graphics to explain an example of a procedure or a difficult concept, and, most prominently, interactive activities in which participants practice problem solving, with hints and immediate and targeted feedback.

The course is built around a series of carefully devised learning objectives that are independently assessed.

REQUIREMENTS

Knowledge of basic algebra.

FREQUENTLY ASKED QUESTIONS

How much time will it take to complete this course?

This course is designed to be equivalent to one semester of a college statistics course.

Does this course require any software?

Does this course offer a Statement of Accomplishment?

Open Online Courses in Statistics

What is Statistics?

Statistics is an academic discipline concerned with the collection, graphic representation, and analysis of data taken from specific population surveys. College statistics programs often focus on three subsections of statistical study: applied, theoretical, and mathematical statistics. online art colleges Applied statistics courses consolidate the study of descriptive and inferential statistics, which uses results from probability studies to explain clear numeric correlations in data as well as the more random results. On-campus and online statistics courses that discuss theoretical statistics use logic and probability theory to explain why data sets yield certain results while mathematical statistics are concerned with fundamentally altering experiment designs to create different probability data.

What Can Online Courses in Statistics Actually Teach Me?

If you plan on enrolling in an online statistics course, you should know that there are no accrediting agencies for statistics courses or programs around the country. The American Statistical Association (AMSTAT) does offer voluntary accreditation to tenured statistics professors with an advanced statistics degree and over 5 years of teaching experience, but there are no ubiquitous accreditation criteria for online courses in statistics. Any online statistics class can teach you about probability theory, confounding factors, and numeric correlations, but the key aspect of this field is experimentation and sample survey design. Online courses simply cannot fulfill this portion of your statistics education since it is up to you to go out, design experiments, and use analytical thinking to explain results from any particular data set.

Online courses in statistics are useful tools for learning about probability theory, experiment design, inferential statistics, and data mining. During these courses, you will ask yourself questions such as:

  • Which experiment factors have direct numeric correlations with one another?
  • Which factors in the sample survey are confounding and fail to fully explain seemingly random experimental results?
  • Did I choose a diverse set of people for my statistical sample?
  • Does my data corroborate my hypothesis?
  • How would changing fundamental aspects of my statistical survey alter the results?

Since there are no criteria for university or online-based statistics courses, you will have to rely upon your own independent research and discretion when selecting a relevant, informative course. Before enrolling, you should research the prestige of the school, the positions its graduates hold in the job market, as well as whether your professor has a PhD in statistics. Accredited postsecondary schools only hire faculty with doctorates in their field, so any college that falls short of that hiring criteria probably lacks an informative, current statistics curriculum. Largely populated universities often have online portals dedicated to student course reviews. You should heed the insight of students whom took your desired courses in the past when deciding if they fit your academic needs.

Free Online Courses in Statistics From Around the Web

We have compiled relevant open courseware in statistics and organized it in the directory below. Open courseware is simply a collection of online tests, video lectures, and related course materials from mostly prestigious universities from around the world. While these materials are non-restrictive and free to access, you will have to learn independently since you cannot interact with the professor of the course. proofread essays online However, these courses can be excellent resources if you are considering an on-campus or online statistics degree and want to gauge your interest in the subject matter and ability to meet classroom work requirements.

Introduction to Applied StatisticsUMass Boston

Open Courseware

Course Description:

Through use of the materials in this online course, you can learn how to use statistics to model practical problems in science and engineering. To get the most out of this course, you will need MATLAB, a statistics software package for performing scientific research, as much of the course focuses on MATLAB data analysis. The materials for this course were originally offered as a graduate course in the summer of 2011, so having a background in an engineering or science field is required to get the most out of the materials.

Statistical ReasoningCarnegie Mellon University

Open Courseware

Course Description:

These materials from an undergraduate online course are offered by the Open Learning Intiative at Carnegie Mellon University, and will teach you about basic logic for statistical reasoning. This course will also teach you how to apply statistics to real world situations and other fields of study, and assumes no prior knowledge about statistics. You will need statistical analysis software like Microsoft Excel or StatCrunch, as well as Flash and Java, to complete the exercises offered by the course.

Patients and Populations: Medical Decision-MakingUniversity of Michigan

Open Courseware

Course Description:

This online course focuses on the medical practitioner’s responsibility to make sound decisions related to patient care based on genetics, epidemiology, information gathering and health assessment. Through this course, you will develop an in-depth understanding of fundamental concepts in biostatistics and research and design in order to effectively and systematically apply probabilistic reasoning to diagnostic issues that arise in clinical situations. Originally published as open courseware in 2012, this course is intended for first-year medical students with backgrounds in health and medicine.

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Mathematics for Machine Learning: PCA

1. Some ability of abstract thinking

2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis)

3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization)

4. Basic knowledge in python programming and numpy

Principal Component Analysis (PCA) is one of the most important dimensionality reduction algorithms in machine learning. In this course, we lay the mathematical foundations to derive and understand PCA from a geometric point of view. In this module, we learn how to summarize datasets (e.g., images) using basic statistics, such as the mean and the variance. We also look at properties of the mean and the variance when we shift or scale the original data set. We will provide mathematical intuition as well as the skills to derive the results. We will also implement our results in code (jupyter notebooks), which will allow us to practice our mathematical understand to compute averages of image data sets.

Data can be interpreted as vectors. Vectors allow us to talk about geometric concepts, such as lengths, distances and angles to characterise similarity between vectors. internet courses This will become important later in the course when we discuss PCA. In this module, we will introduce and practice the concept of an inner product. Inner products allow us to talk about geometric concepts in vector spaces. More specifically, we will start with the dot product (which we may still know from school) as a special case of an inner product, and then move toward a more general concept of an inner product, which play an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes). We have a lot of exercises in this module to practice and understand the concept of inner products.

In this module, we will look at orthogonal projections of vectors, which live in a high-dimensional vector space, onto lower-dimensional subspaces. This will play an important role in the next module when we derive PCA. We will start off with a geometric motivation of what an orthogonal projection is and work our way through the corresponding derivation. We will end up with a single equation that allows us to project any vector onto a lower-dimensional subspace. However, we will also understand how this equation came about. As in the other modules, we will have both pen-and-paper practice and a small programming example with a jupyter notebook.

We can think of dimensionality reduction as a way of compressing data with some loss, similar to jpg or mp3. Principal Component Analysis (PCA) is one of the most fundamental dimensionality reduction techniques that are used in machine learning. In this module, we use the results from the first three modules of this course and derive PCA from a geometric point of view. Within this course, this module is the most challenging one, and we will go through an explicit derivation of PCA plus some coding exercises that will make us a proficient user of PCA.

Statistics

Probability and statistics are used not only to predict outcomes, but to determine the accuracy of data.

In the age of the internet misinformation flourishes, and being able to interpret and understand statistical data is more important than ever.

Alison offers free, online courses in statistics and probability to help you collect and understand data more accurately and efficiently.

Courses like Probability and Chance in Mathematics start you off with the fundamentals of chance before working your way into more advanced courses on topics like regression analysis, data collection, and distribution models.

Students taking Alison’s free online statistics courses also have the chance to earn a diploma in statistics. Get started learning statistics today.

Intro to Statistics

Making Decisions Based on Data

Nanodegree Program

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Accelerate your career with the credential that fast-tracks you to job success.

About this Course

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.

Intro to Statistics
Course Cost
Approx. 2 months
Skill Level
Included in Course

Rich Learning Content

Taught by Industry Pros

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This free course is your first step towards a new career with the Artificial Intelligence Nanodegree Program.

Free Course

Intro to Statistics

Enhance your skill set and boost your hirability through innovative, independent learning.

Nanodegree Program

Artificial Intelligence

Accelerate your career with the credential that fast-tracks you to job success.

Course Leads

Sebastian Thrun

What You Will Learn

Visualizing relationships in data

  • Seeing relationships in data.
  • Making predictions based on data.
  • Simpson's paradox.

Visualizing relationships in data

  • Seeing relationships in data.
  • Making predictions based on data.
  • Simpson's paradox.

Probability

  • Introduction to Probability.
  • Bayes Rule.
  • Correlation vs. Causation.

Probability

  • Introduction to Probability.
  • Bayes Rule.
  • Correlation vs. Causation.

Estimation

  • Maximum Likelihood Estimation.
  • Mean, Median, Mode.
  • Standard Deviation and Variance.

Estimation

  • Maximum Likelihood Estimation.
  • Mean, Median, Mode.
  • Standard Deviation and Variance.

Outliers and Normal Distribution.

  • Outliers, Quartiles.
  • Binomial Distribution.
  • Manipulating Normal Distribution.

Outliers and Normal Distribution.

  • Outliers, Quartiles.
  • Binomial Distribution.
  • Manipulating Normal Distribution.
  • Confidence Intervals.
  • Hypothesis Testing.
  • Confidence Intervals.
  • Hypothesis Testing.

Regression

  • Linear regression.
  • Correlation.

Regression

  • Linear regression.
  • Correlation.

Final Exam

Final Exam

Prerequisites and Requirements

This course does not require any previous knowledge of statistics. online technical schools Basic familiarity with algebra such as knowing how to compute the mean, median and mode of a set of numbers will be helpful.

Why Take This Course

This course will cover visualization, probability, regression and other topics that will help you learn the basic methods of understanding data with statistics.

What do I get?
  • Instructor videos
  • Learn by doing exercises
  • Taught by industry professionals
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About this Course

NOTE: This course has been divided into two courses: Descriptive and Inferential Statistics. If you are new to statistics, we recommend taking these courses instead.

We live in a time of unprecedented access to information. data. Whether researching the best school, job, or relationship, the Internet has thrown open the doors to vast pools of data. Statistics are simply objective and systematic methods for describing and interpreting information so that you may make the most informed decisions about life.

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Coursera, a company founded by Stanford professors offers online courses from over 140 universities.

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2075 courses / 126615 followers

edX is a nonprofit MOOC provider founded by Harvard and MIT. It has around 100 university partners.

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792 courses / 75750 followers

FutureLearn is a UK-based provider with 130 partners and has a focus on social learning.

At MIT and Georgia Tech, MOOCs Are Showing Up On Campus

For the first time, on-campus students can earn credit from a MOOC.”

via MOOC Report

Massive List of MOOC Providers Around The World

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MOOCs on campus, a product at every price point, content paywalls, and more.

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To support our site, Class Central may be compensated by some course providers.

Estadística Aplicada a los Negocios

Este curso proporciona una introducción al análisis de datos en base a las principales herramientas estadísticas, enfocándose en la estadística descriptiva y la estadística inferencial.

Se estudian los diferentes tipos de gráficos estadísticos utilizados para describir el comportamiento de los datos. El curso de estadística aplicada presenta la definición de una variable aleatoria, funciones de densidad de probabilidad, de distribución acumulativa; también se presentan las variables aleatorias para el caso continuo y estimación con intervalos de confianza, finalizando con una introducción a los modelos econométricos.

Lección 1: Introducción al Análisis de Datos

Esta primera lección te aportará una visión general sobre la importancia de analizar la información, organizar los datos de forma adecuada para facilitar su posterior uso y la toma de decisiones.

  • Inteligencia de Negocios vs. Análisis de Datos
  • ¿Por qué es importante el Análisis de Datos?
  • Tipos de herramientas BI (Business Intelligence)

Lección 2: Fundamentos de Estadística Descriptiva

Aprenderás a describir los datos mediante el uso de distribuciones de frecuencia, representaciones gráficas, medidas de tendencia central y de dispersión.

  • Distribución de frecuencias
  • Representaciones gráficas
  • Medidas de Tendencia Central y Dispersión
  • Conceptos básicos de probabilidad

Lección 3: Variables aleatorias

Conocerás los principios de probabilidad discreta y continua.

  • Variables aleatorias discretas
  • PDF, CDF, Valor Esperado y Varianza
  • Distribuciones discretas de probabilidad
  • Variables aleatorias continuas
  • PDF, CDF, Valor Esperado y Varianza
  • Distribución continua de probabilidades

Lección 4: Aplicaciones empresariales de la estadística inferencial

Aprenderás todo lo relacionado a las distribuciones muéstrales, teorema del límite central, estimadores, intervalos de confianza, prueba de hipótesis y valores P.

  • Estimadores
  • Intervalos de confianza
  • Prueba de hipótesis
  • Valores p

Lección 5: Econometría y Análisis de regresión

Descubrirás por qué es importante la econometría para tu organización y construirás modelos de regresión lineal.

  • ¿Qué es la econometría?
  • Construyendo un Modelo de Regresión Lineal
  • Errores
  • Autocorrelación
  • Multicolinealidad

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Share this course

Just Announced
Self Paced
Starts Now
#2 Modern & Contemporary American Poetry (“ModPo”)

University of Pennsylvania via Coursera

#7 Programming for Everybody (Getting Started with Python)

University of Michigan via Coursera

#27 Decision Making in a Complex and Uncertain World

University of Groningen via FutureLearn

Computer Science

832 courses / 185795 followers

1520 courses / 151312 followers

Humanities

875 courses / 86011 followers

Data Science

373 courses / 131870 followers

Personal Development

253 courses / 133099 followers

Art & Design

537 courses / 88934 followers

Engineering

665 courses / 90269 followers

Health & Medicine

655 courses / 86196 followers

Mathematics

298 courses / 81335 followers

914 courses / 87707 followers

Education & Teaching

777 courses / 81925 followers

Programming

742 courses / 168936 followers

170 courses / 183705 followers

169 courses / 136047 followers

134 courses / 195648 followers

Georgia Tech

104 courses / 57920 followers

Tsinghua University

48 courses / 12142 followers

IIM Banglore

28 courses / 13919 followers

75 courses / 17870 followers

43 courses / 30259 followers

133 courses / 82231 followers

3231 courses / 178120 followers

Coursera, a company founded by Stanford professors offers online courses from over 140 universities.

207 courses / 82102 followers

Udacity, born out of a Stanford experiment, partners with tech companies to offer career-focused courses.

2075 courses / 126615 followers

edX is a nonprofit MOOC provider founded by Harvard and MIT. It has around 100 university partners.

FutureLearn

792 courses / 75750 followers

FutureLearn is a UK-based provider with 130 partners and has a focus on social learning.

At MIT and Georgia Tech, MOOCs Are Showing Up On Campus

For the first time, on-campus students can earn credit from a MOOC.”

via MOOC Report

Massive List of MOOC Providers Around The World

Where to Find MOOCs: The Definitive Guide to MOOC Providers

via MOOC Report

6 Biggest MOOC Trends of 2017

MOOCs on campus, a product at every price point, content paywalls, and more.

via MOOC Report

To support our site, Class Central may be compensated by some course providers.

Bayesian Statistics

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Bayesian Statistics. Please take several minutes read this information. Thanks for joining us in this course!

Welcome! Over the next several weeks, we will together explore Bayesian statistics.

In this module, we will work with conditional probabilities, which is the probability of event B given event A. Conditional probabilities are very important in medical decisions. By the end of the week, you will be able to solve problems using Bayes’ rule, and update prior probabilities.

Please use the learning objectives and practice quiz to help you learn about Bayes’ Rule, and apply what you have learned in the lab and on the quiz.

In this week, we will discuss the continuous version of Bayes’ rule and show you how to use it in a conjugate family, and discuss credible intervals. By the end of this week, you will be able to understand and define the concepts of prior, likelihood, and posterior probability and identify how they relate to one another.

In this module, we will discuss Bayesian decision making, hypothesis testing, and Bayesian testing. By the end of this week, you will be able to make optimal decisions based on Bayesian statistics and compare multiple hypotheses using Bayes Factors.

This week, we will look at Bayesian linear regressions and model averaging, which allows you to make inferences and predictions using several models. By the end of this week, you will be able to implement Bayesian model averaging, interpret Bayesian multiple linear regression and understand its relationship to the frequentist linear regression approach.

This week consists of interviews with statisticians on how they use Bayesian statistics in their work, as well as the final project in the course.

In this module you will use the data set provided to complete and report on a data analysis question. editing papers online Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.

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University Statistics Courses Available Online

Essentially, statistics is the study of data. In the professional world, there is always a constant flow of new data “” customer trends, stock patterns, sales, imports, exports, sports figures, and on and on “” which businesses use to plan their strategies. In an online statistics course, students will learn the skills, methods, and principles necessary to collect and present qualitative data, and to make inferences based on that data, either for their own benefit, or for the benefit of a company. Specifically, an online college statistics course will cover statistical methods, regression analysis, survey sampling, probability and random distribution, and data relationships.

In general, most science and math majors require at least one introductory course in statistics. Sociology and economics majors will rely heavily on statistical principles, and will have to continue with more advanced statistics classes, where they will gain further insight into survey compilation and analysis, introduce more and more uncertainty into the data they interpret, and formulate more targeted hypotheses and estimations based on those data.

For students who want to get a head start on their college classes, or prefer to pursue their studies at home on their own schedule, many online colleges and some traditional schools offer at least one introductory online statistics course for college credit. The American Statistical Association recently developed a statistics accreditation program, and is slowly implementing that standard across academic institutions across the nation, so it will be imperative in the coming years for students to find out whether their prospective program is accredited before enrolling.

Statistics and Your Career

There are a wide variety of applications of statistics, and many majors require at least one statistics class, largely because statistical expertise is an extremely valuable skill to have in almost any field. The ability to draw statistical conclusions will be especially useful for business owners and economists, but statisticians will be important anywhere quantitative data are found. For example, bookkeepers, accountants, and financial managers may benefit from having a background in statistics because they often deal with investments and spending.

Outside of business, the study of statistics can lead to positions in weather forecasting, business trend prediction, biological estimation, research analysis for the stock market, econometrics for research and development firms, financial analysis for financial service firms, and beyond. All in all, taking a statistics course can help you improve your understanding of research, data compilation, and probability, so even if you don’t use your statistics knowledge in your career path, you can still use it in your day-to-day activities.

Free Online Statistics Courses:

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