File Name: an introduction to statistical and data sciences via r .zip
Bringing a fresh approach to intro statistics, ISRS introduces inference faster using randomization and simulation techniques.
- 21 Free Online Books to Learn R and Data Science
- Statistical Inference via Data Science
- An Introduction to Data Science
- Statistics with R Specialization
Master Statistics with R.
Important Note : This is a previous version v0. For the current version of ModernDive, please go to ModernDive. What do I do? Start with our Introduction for Students.
21 Free Online Books to Learn R and Data Science
Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Learning new things has become more accesible now due to the plethora of material available online.
This is particularly the case for Data Science and Machine Learning. Since I got interested in the field, I have come across a huge amount of learning material which I found immensely useful. This is an attempt to put them togther and make it accesible to others.
There are many wonderful resources which Professors have put up online and this is an attempt to catalogue these awesome resources. It also has been done by Prakhar on Github , which is suited to Software Engineering , so the below list is an attempt to list down resources pertaining to Data Science and focussed more on R software language. I plan to add more Python Material going forward. Hope you find this list useful. Say Hi! Stats without Tears Stan Brown.
Jay Kerns- Youngstown State University. Introduction to Data Science This is an open source textbook aimed at introducing undergraduate students to Data Science. Kim - DataCamp and Amherst College. Principles of Econometrics with R Constantin Colonescu.
Statistical Rethinking with brms, ggplot2, and the tidyverse A Solomon Kurz. Introduction to Data Science Rafael A. Irizarry - Harvard University. Norm Matloff- University of California, Davis. Fundamentals of Data Visualization Claus O. R for Social Sciences Data Carpentry. Visual Statistics Alexey Shipunov.
Applied R for the quantitative social scientist Rense Nieuwenhuis. Statistical Thinking for the 21st Century Russell A. An Introduction to R W. Venables, D. Smith and the R Core Team. The R Inferno Patrick Burn. R user group Oxford : Dedicated to bringing together area practitioners of R to exchange knowledge, inspire new users, and spur the adoption of R for innovative research and commercial applications.
Awesome Blogdown : Awesome curated list of blogs built using blogdown. DALEX: Descriptive mAchine Learning EXplanations : In many applications we need to know, understand or prove how input variables are used in the model and what impact do they have on final model prediction.
DALEX is a set of tools that help to understand how complex models are working. Probability cheatsheet Shervine Amidi. Statistics cheatsheet Shervine Amidi. Distribution Tables cheatsheet Shervine Amidi. Machine Learning tips and tricks cheatsheet Shervine Amidi.
Deep Learning cheatsheet Shervine Amidi. Stats cheatsheet CSE Data Science cheatsheet Maverick Lin. R Studio Online Tutorials. Programming with R Software Carpentry Foundation. Courses taught by Hadley Wickham H. Statistics courses offered in Harvard Harvard University. Prob formally Statistics or STAT is a probability course for undergraduates who have taken Data 8, have a math background, and wish to go deeper into the theory of data science.
The emphasis on simulation and the bootstrap in Data 8 gives students a concrete sense of randomness and sampling variability. Prob will capitalize on this. This will create time to focus on the more demanding concepts that are part of the theoretical foundations of data science. The class starts by providing a fundamental grounding in combinatorics, and then quickly moves into the basics of probability theory. We will then cover many essential concepts in probability theory, including particular probability distributions, properties of probabilities, and mathematical tools for analyzing probabilities.
Finally, the last third of the class will focus on data analysis and Machine Learning as a means for seeing direct applications of probability in this exciting and quickly growing subfield of computer science. The course provides a solid introduction to data science, both exposing students to computational tools they can proficently use to analyze data and exploring the conceptual challenges of inferential reasoning.
Class lectures will have interactive elements, and assignments will be application-driven. How can we make sense of all the information we are acquiring about ourselves? During each week, we will consider a different data set to be summarized with a different goal. We will review analyses of similar problems carried out in the past and explore if and how the same tools can be useful today.
We will pay attention to contemporary media newspapers, blogs, etc. The material provides an introduction to applied data analysis, with an emphasis on providing a conceptual framework for thinking about data from both statistical and machine learning perspectives.
Topics will be drawn from the following list, depending on time constraints and class interest: approaches to data analysis: statistics frequentist, Bayesian and machine learning; binary classification; regression; bootstrapping; causal inference and experimental design; multiple hypothesis testing.
Combining data, computation, and inferential thinking, data science is redefining how people and organizations solve challenging problems and understand their world. This intermediate level class bridges between Data8 and upper division computer science and statistics courses as well as methods courses in other fields. The class will introduce the students to formal statistical reasoning. Building on knowledge of probability and calculus, we will explore how limited noisy observations can be used to learn general characteristics of a population.
We will study the basics of decision theory, including frequentist and Bayesian solutions to the "paradox of induction. This course introduces fundamental tools and technologies necessary to transform data into knowledge.
We'll cover skill associated with each component of the information lifecycle, including the collection, storage, analysis, and visualization of data. Core competencies underlying this process, including functional programming, use of databases, data wrangling, version control, and command line proficiency, are acquired through real-world data-driven assignments. This course will teach you to be a data analyst. You will learn how to take a large dataset break up into manageable pieces and use a range of qualitative and quantitative tools to summarise it and learn what it has to tell.
You will learn the importance of scepticism and curiosity, and how to communicate your findings. Each section of the course is motivated by a particular dataset, and you will gain experience working with a wide variety of data sources varying in size and quality. This course will cover the principles of digital methods for storing and structuring data, including data types, relational and non-relational database design, and query languages.
Students will learn to build, populate, manipulate and query databases based on datasets relevant to their fields of interest. An introduction to methods for analyzing categorical data.
Emphasis will be on understanding models and applying them to datasets. Topics include visualizing categorical data, analysis of contingency tables, odds ratios, log-linear models, generalized linear models, logistic regression, Poisson regression and model diagnostics.
Examples drawn from many fields, including biology, medicine and the social sciences. This course aims to go far beyond the classical statistical methods, such as linear regression, that are introduced in GSBA Methods are motivated by examples from social sciences, policy and health sciences. Webpage of Dr. Kari Lock Morgan for other course links. As statisticians we cannot always rely on other people and sciences to get the data into formats that we can deal with: we will discuss aspects of statistical computing as they are relevant for data analysis.
Read and work with data in different formats: flat files, databases, web technologies. Elements of literate programming help us with making our workflow transparent and analyses reproducible. We will discuss communication of results in form of R packages and interactive web application. Understand the distinction between supervised and unsupervised learning and be able to identify appropriate tools to answer different research questions.
Become familiar with basic unsupervised procedures including clustering and principal components analysis. Become familiar with the following regression and classification algorithms: linear regression, ridge regression, the lasso, logistic regression, linear discriminant analysis, K-nearest neighbors, splines, generalized additive models, tree-based methods, and support vector machines.
Gain a practical appreciation of the bias-variance tradeoff and apply model selection methods based on cross-validation and bootstrapping to a prediction challenge.
Analyze a real dataset of moderate size using R. Develop the computational skills for data wrangling, collaboration, and reproducible research. Be exposed to other topics in machine learning, such as missing data, prediction using time series and relational data, non-linear dimensionality reduction techniques, web-based data visualizations, anomaly detection, and representation learning. Final Project- Kaggle. The course is intended to be a non-exhaustive survey of regression techniques from both a theoretical and applied perspective.
This class is a practical introduction to statistical modeling and experimental design, intended to provide essential skills for doing research.
We'll cover basic techniques e.
Statistical Inference via Data Science
Introduction to Statistics for the Life and Biomedical Sciences has been written to be used in conjunction with a set of self-paced learning labs. These labs guide students through learning how to apply statistical ideas and concepts discussed in the text with the R computing language. The text discusses the important ideas used to support an interpretation such as the notion of a confidence interval , rather than the process of generating such material from data such as computing a confidence interval for a particular subset of individuals in a study. This allows students whose main focus is understanding statistical concepts to not be distracted by the details of a particular software package. In our experience, however, we have found that many students enter a research setting after only a single course in statistics. These students benefit from a practical introduction to data analysis that incorporates the use of a statistical computing language.
An open-source and fully-reproducible electronic textbook for teaching statistical inference using tidyverse data science tools.
An Introduction to Data Science
This is a free textbook teaching introductory statistics for undergraduates in Psychology. This textbook is part of a larger OER course package for teaching undergraduate statistics in Psychology, including this textbook, a lab manual, and a course website. The primary goal of Bayes Rules! Bayes Rules! This textbook goes farther than just showing you how to make computational models using software or mathematical models using statistics.
Statistics with R Specialization
If you are interested in learning Data Science with R, but not interested in spending money on books, you are definitely in a very good space. R for Data Science , by Hadley Wickham and Garrett Grolemund, is a great data science book for beginners interesterd in learning data science with R. Typically with discount it is much cheaper.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses.
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.
The book was written using the software package LATEX in combination with would like to be at the forefront of scientific innovations and developments? and export is included in manual “R Data Import/ Export” (R Core Team (b)).
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This book provides non-technical readers with a gentle introduction to essential concepts and activities of data science. For more technical readers, the book provides explanations and code for a range of interesting applications using the open source R language for statistical computing and graphics"--Resource home page. Stanton, Jeffrey M. It has been viewed times, with 71 in the last month. More information about this book can be viewed below.