This blog is where I share insights from any number of adventures in data analysis.
I will cover best practices from modeling and analysis projects; share tips on using new tools; outline new projects; recount war stories from Wikipedia and other FOSS projects I contribute to, and discuss challenges on information retrieval challenges, natural language processing tricks and game theoretic insights, portfolio analysis, social network analysis.
I wanted to download some course material on RL shared by the author via Google drive using the command line. I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
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Business Analytics for Managers - Review
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Wolfgang Jank's text "Business Analytics for Managers" is part of the Use R! series published in 2011 by Springer.
It stand out as easy introduction for using R for exploratory data analytic and data modeling at an introductory level. While R and statistics are skill not easily acquired working with the book makes for an easy learning curve by focusing on the business side of the work while omitting much of the platform specific implementation details. This makes some sense if the manager will want to ask someone else to work with R to get him the results.
The text explains how to work with different type of data and how a manager would analyse the different data-sets. It explains the benefits of using a large cross section of R's visualization techniques for assessing unfamiliar data for global or cross sectional patterns.
It then goes on to explain the essentials of of data handling techniques such as creation of dummy variable interaction variables, variable transformation as well as both linear and non-linear regression models. The reader will quickly grasp what the output of R's regression models mean, how to compare different model's quality and if significant data is also of practical for business application.
Wolfgang Jank kindly furnished me with the data-sets he uses in the book. In this and the following posts I'll be adding my notes and R code I used to follow along the text and reproduce the results and their graphical visualization.
I wanted to download some course material on RL shared by the author via Google drive using the command line. I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue https://drive.google.com/drive/folders/1V9jAShWpccLvByv5S1DuOzo6GVvzd4LV if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.
Regression Analysis TLDR Regression is the oldest and most powerful tool in a data scientist's toolbox. Under ideal conditions multiple linear regression would be the best and only tool a data scientist would want to use... In reality you would use a modern nonparametric variant or a different algorithm. Still, the main ideas I . discuss here will pop up in many other models and algorithms. This post is my brain dump on regression - I'll update it as time allows to cover the many aspects of this technique.
Selection and projection are two high level processes taking place when SQL queries are executed. Selection is choosing some records (rows) from a table and leaving others out. e.g. rows having name='oren'. Projection is the choosing some columns from each record and leaving others out. e.g. only name. So the select keyword performs projection while the where and keyword performs selection. Clearly the choice of using the keyword select for projection (choosing columns) rather than choosing rows, is an unfortunate flaw in the design of SQL, but this oversight is too well established to be fixed.
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