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공개·회원 10명
Valentine Ponomarev
Valentine Ponomarev

An Introduction To Statistics With Python: With... BETTER



This textbook provides an introduction to the free software Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics. Working code and data for Python solutions for each test, together with easy-to-follow Python examples, can be reproduced by the reader and reinforce their immediate understanding of the topic. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis and an interesting alternative to R. The book is intended for master and PhD students, mainly from the life and medical sciences, with a basic knowledge of statistics. As it also provides some statistics background, the book can be used by anyone who wants to perform a statistical data analysis.




An Introduction to Statistics with Python: With...



In the era of big data and artificial intelligence, data science and machine learning have become essential in many fields of science and technology. A necessary aspect of working with data is the ability to describe, summarize, and represent data visually. Python statistics libraries are comprehensive, popular, and widely used tools that will assist you in working with data.


DataFrame methods are very similar to Series methods, though the behavior is different. If you call Python statistics methods without arguments, then the DataFrame will return the results for each column:


The box plot is an excellent tool to visually represent descriptive statistics of a given dataset. It can show the range, interquartile range, median, mode, outliers, and all quartiles. First, create some data to represent with a box plot:


Statistics simply means numerical data, and is field of math that generally deals with collection of data, tabulation, and interpretation of numerical data. It is actually a form of mathematical analysis that uses different quantitative models to produce a set of experimental data or studies of real life. It is an area of applied mathematics concern with data collection analysis, interpretation, and presentation. Statistics deals with how data can be used to solve complex problems. Some people consider statistics to be a distinct mathematical science rather than a branch of mathematics.


Hypothesis testing is a type of inferential procedure that takes help of sample data to evaluate and assess credibility of a hypothesis about a population. Inferential statistics are generally used to determine how strong relationship is within sample. But it is very difficult to obtain a population list and draw a random sample.


A self-paced course to help prepare students with the base level of statistics knowledge for success in the data science program. The modules in this course cover an introduction to statistics, probability, and probability distributions. Descriptive statistics and hypothesis testing are also covered in this course.


This method returns many useful descriptive statistics with a mix of measures of central tendency and measures of variability. This includes the number of non-missing observations; the mean; standard deviation; minimum value; 25th, 50th (a.k.a. the median), and 75th percentile; as well as the maximum value. It's missing some useful information that is typically desired regarding the mean, this is the standard error and the 95% confidence interval. No worries though, pairing this with Researcpy's summary_cont() method provides the descriptive statistic information that is wanted - this method will be shown later.


This course is for students doing advanced studies in statistics and certain other fields will provide an introduction to modern machine learning methods. Topics include supervised learning, sparsity, logistic regression, SVM, kernel methods, deep learning, unsupervised learning, and real world problems including fairness and interpretability of black box models.


The Signal and the Noise is yet another great statistics book for data science. It even reached New York Time Best Sellers list within a week of its first print. The author of this book, Nate Silver has explained the practical art of mathematical model building using statistics and probability using his own learnings.


Ranging from basics like central tendency and distributions to advanced concepts like T-tests, regression, ANOVA, etc, this book covers the fundamentals of statistics in-depth and with examples. The book also provides links to various useful tools and resources.


Computer Age Statistical Inference is basically statistics in a time machine. This book takes you on a breathtaking journey of how statistics and its inference have evolved from before to after the introduction of modern-day computers.


The books mentioned in this article are the best statistics books for Data Science. They can help you start with and understand the statistics needed to pursue data science, and make better inferences about the data. I hope you enjoy reading these books and implement the learnings effectively in your Data Science journey.


You can go through one of the statistics books above and start implementing the concepts with the help of programming. You can pick a statistics book that contains code and data examples. This way you can see much of the statistics in action.


Introduction to the fundamental ideas of statistical thinking with R programming. Explore survey, experimental, and observational study design; common sources of random and systematic error in data; the bootstrap as a tool for quantifying uncertainty; hypothesis testing; regression; and the role of statistics in an ethical and just society. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 313 with a grade of at least C-.


An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation. Includes probability, distributions, sampling, interpolation, iteration, recursion, and visualization. Three lecture hours and one laboratory hour a week for one semester. Statistics and Data Sciences 318 and Statistics and Scientific Computation 318 may not both be counted.


Covers fundamentals of probability, combinatorics, discrete and continuous random variables, jointly distributed random variables, and limit theorems. Using probability to introduce fundamentals of statistics, including Bayesian and classical inference. The equivalent of four lectures hours a week. Statistics and Data Sciences 321 and 431 may not both be counted. Prerequisite: Mathematics 408C, 408L, 408R, 408S, or 408Q with a grade of at least C-.


An introduction to the fundamental theories, concepts, and methods of statistics. Emphasizes probability models, exploratory data analysis, sampling distributions, confidence intervals, hypothesis testing, correlation and regression, and the use of statistical software. Three lecture hours a week for one semester. Statistics and Data Sciences 325H and Statistics and Scientific Computation 325H may not both be counted. Prerequisite: Admission to the Dean's Scholars Honors Program in the College of Natural Sciences or consent of instructor.


Explore advanced methods in statistics and data science. Examine modeling data with multilevel (hierarchical) structure and causal inference, including design and analysis strategies. Discuss smoothing methods; spatial and time series models; additive models; and models for network data. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 334 with a grade of at least C-.


Introduction to the mathematical theory of statistics. Explore maximum likelihood estimation, confidence intervals, hypothesis tests and statistical decision theory, tail and concentration bounds, concentration of measure, and nonparametric statistics. The equivalent of three lecture hours a week for one semester. Prerequisite: Statistics and Data Sciences 431 with a grade of at least C-; Mathematics 340L or 341 or Statistics and Data Sciences 329C with a grade of at least C-; and credit with a grade of at least C- or registration for Statistics and Data Sciences 334; and a solid foundation in calculus, probability theory, and linear algebra.


Same as Mathematics 378K. Sampling distributions of statistics, estimation of parameters (confidence intervals, method of moments, maximum likelihood, comparison of estimators using mean square error and efficiency, sufficient statistics), hypothesis tests (p-values, power, likelihood ratio tests), and other topics. Three lecture hours a week for one semester. Mathematics 378K and Statistics and Data Sciences 378 may not both be counted. Prerequisite: Mathematics 362K with a grade of at least C-.


Concepts of probability and mathematical statistics with applications in data analysis and research. Three lecture hours a week for one semester. Statistics and Data Sciences 384 and Statistics and Scientific Computation 384 may not both be counted unless topics vary. May be repeated for credit when the topics vary. Prerequisite: Graduate standing; and Statistics and Data Sciences 382 (or Statistics and Scientific Computation 382), an introductory probability course and a statistics course, or consent of instructor.


Generally speaking, statistics is split into two subfields: descriptive and inferential. The difference is subtle, but important. Descriptive statistics refer to the portion of statistics dedicated to summarizing a total population. Inferential Statistics, on the other hand, allows us to make inferences of a population from its subpopulation. Unlike descriptive statistics, inferential statistics are never 100% accurate because its calculations are measured without the total population. 041b061a72


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