Tag: logistic regression

SAS Learning path and resources – Business Analyst in SAS

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This is a curriculum compiled by Analytics Vidhya as a guide to learning SAS for business analytics. The curriculum is composed of the following steps: Step 0: Why learn SAS Step 1: Downloading and Installing SAS Step 2: Base SAS on sas.com Step 3: SQL Step 4: Descriptive Statistics with the Udacity Intro to Descriptive Statistics course Step 5: Inferential Statistics with the Udacity Intro to Inferential Statistics course Step … Continue Reading

Learn Data Science

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Learn Data Science is “a collection of Data Science Learning materials in the form of iPython Notebooks” and the “associated data sets.” The following topics are covered, with at least three notebooks including an Overview – an exposition of the technique for the math-wary”, Data Exploration – “the nuts and bolts of real world data wrangling”, and Analysis – “using the technique to get results”. Topics: Linear Regression Logistic Regression … Continue Reading

Data Science from Scratch: First Principles with Python

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From Amazon: “Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get … Continue Reading

DataCamp – Credit Risk Modeling in R

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From DataCamp: “This hands-on-course with real-life credit data will teach you how to model credit risk by using logistic regression and decision trees in R. Modeling credit risk for both personal and company loans is of major importance for banks. The probability that a debtor will default is a key component in getting to a measure for credit risk. While introducing other models in this course as well, we focus on … Continue Reading

Machine Learning Cheat Sheet

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This cheat sheet contains many classical equations and diagrams on machine learning, which will help you quickly recall knowledge and ideas in machine learning.

The Elements of Statistical Learning

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This book describes the important ideas in statistics, data mining, machine learning, and bioinformatics in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

The Analytics Edge

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Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.

Statistics One

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Statistics One is designed to be a comprehensive yet friendly introduction to fundamental concepts in statistics, and also provide an introduction to the R programming language.

Practical Data Science with R

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Practical Data Science with R shows you how to apply the R programming language and useful statistical techniques to everyday business situations using examples from marketing, business intelligence, and decision support.

Pattern Recognition and Machine Learning

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This textbook is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics, and is aimed at advanced undergraduates or 1st-year PhD students, as well as practitioners.