Foundations Of Statistics For Data Scientists Pdf

[Alarico Boyett]

Thiscourse gives an in-depth introduction to statistics and machine learning theory, methods, and algorithms for data science. It covers multiple regression, kernel learning, sparse regression, sure screening, generalized linear models and quasi-likelihood, covariance learning and factor models, principal component analysis, supervised and unsupervised learning, deep learning, and other related topics such as community detection, item ranking, and matrix completion. The applicability and limitations of these methods will be illustrated using mathematical statistics, a variety of modern real-world data sets, and manipulation of the statistical software R.

The software package for this class is R or RStudio. See R-labs below. Most of the computation in this class can be done through a laptop. Laptops with wireless communication turned off can be used during exams, and so are the calculators.

Attendance of the class is required and essential. The course materials are mainly from the notes. Many conceptual issues and statistical thinking are only taught in the class. They will appear in the midterm and final exams.

Problems will be assigned through Canvas approximately biweekly and submitted online. No late homework will be accepted. Missed homework will receive a grade of zero. The homework will be graded, and each assignment carries equal weight. You are allowed to work with other students on the homework problems; however, verbatim copying of homework is absolutely forbidden. Therefore, each student must ultimately produce his or her own homework to be handed in and graded.

There will be one in-class midterm exam and a final exam. All exams are required, and there will be no make-up exams. Missed exams will receive a grade of zero. All exams are open-book and open-notes. Laptops with wireless off and calculators may be used during the exams.

Foundations of Statistics for Data Scientists: With R and Python is designed as a textbook for a one- or two-term introduction to mathematical statistics for students training to become data scientists. It is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modeling. The book assumes knowledge of basic calculus, so the presentation can focus on "why it works" as well as "how to do it." Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.

The book also introduces modern topics that do not normally appear in mathematical statistics texts but are highly relevant for data scientists, such as Bayesian inference, generalized linear models for non-normal responses (e.g., logistic regression and Poisson loglinear models), and regularized model fitting. The nearly 500 exercises are grouped into "Data Analysis and Applications" and "Methods and Concepts." Appendices introduce R and Python and contain solutions for odd-numbered exercises. The book's website ( -aachen.de/) has expanded R, Python, and Matlab appendices and all data sets from the examples and exercises.

Alan Agresti, Distinguished Professor Emeritus at the University of Florida, is the author of seven books, including Categorical Data Analysis (Wiley) and Statistics: The Art and Science of Learning from Data (Pearson), and has presented short courses in 35 countries. His awards include an honorary doctorate from De Montfort University (UK) and Statistician of the Year from the American Statistical Association (Chicago chapter).

Maria Kateri, Professor of Statistics and Data Science at the RWTH Aachen University, authored the monograph Contingency Table Analysis: Methods and Implementation Using R (Birkhuser/Springer) and a textbook on mathematics for economists (in German). She has long-term experience in teaching statistics courses to students of Data Science, Mathematics, Statistics, Computer Science, Business Administration, and Engineering.

"The statistical training for budding data scientists is different than the statistical training for budding statisticians, or other scientists. Data scientists require a different mix of theory and practice than statisticians, plus a great deal more exposure to computation than many other types of scientists. The aspects of this manuscript that I find appealing for the courses I teach: 1. The use of real data. 2. The use of R but with the option to use Python. 3. A good mix of theory and practice. 4. The text is well-written with good exercises. 5. The coverage of topics (e.g. Bayesian methods and clustering) that are not usually part of a course in statistics at the level of this book".

-Jason M. Graham, University of Scranton

"The book is well-written and the examples are well-suited for building foundations for statistical science for data science as a discipline. The material covers most of the theoretical backgrounds in statistics. Throughout the book, the authors have used R programming to illustrate the concepts. In many cases, simulations were presented to support the theory. Each chapter has abundant practical exercises for the readers to explore the materials further. This textbook can serve as a textbook for a data science curriculum."

-Steve Chung, Cal State University Fresno

This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.

Learners will practice new probability skills. including fundamental statistical analysis of data sets, by completing exercises in Jupyter Notebooks. In addition, learners will test their knowledge by completing benchmark quizzes throughout the courses.

Courses do not have to be taken a specific order, though it's recommended that learners follow the sequence of courses if they have no previous experience with data structures or algorithm analysis and design.

Statistical Inference for Data Science Applications is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Learners enrolled in the degree program will earn three credits for successful completion of the specialization.

Learners will learn foundational skills such as calculating probabilities of events, discrete and continuous random variables, and using the Central Limit Theorem to estimate probabilities associated with sums of random variables.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aidOpens in a new tab.

The Data Science Foundations Undergraduate Certificate introduces you to the world of data science. This five-course (14 hours) program is well-suited for all students interested in developing the fundamental knowledge and skills to apply data science principles to future research or careers. This program prepares you to analyze and make relevant discoveries from large data sets through courses emphasizing statistics, computer science, and business data analytics.

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Statistical data science majors are trained in basic and advanced data analysis ranging from introductory statistics and calculus to statistical machine learning and linear algebra. Students learn how to use data ethically; the importance and effectiveness of visualization in communicating results; and programming skills in both R and Python. They also dive deep into an area of interest that forms the basis of their culminating capstone project.

To complete the BS in Statistical Data Science, students are required to only take one sequence of lab courses, along with an additional science course that does not need to be a lab course. This differs from most BS degrees in the College of Liberal Arts and Sciences, which require two sequences of lab courses along with an additional lab course.

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