Source: The raw facts on big data – The University of Sydney Business School

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# Statulator blog

### Statistical blogs, tutorials and news

## The raw facts on big data – The University of Sydney Business School

## Demystifying statistics: Estimating sample size for a survey

## Four skills you need to know for the future | The New Daily

## Six questions with Kennedy Elliott – Visualising Data

## Data Visualization: How To Merge Form & Function And Make Great #Infographics

## If you don’t really understand big data how about tackling medium data first?

## R Programming for Data Science by Roger D. Peng

## Why coding is not the new literacy – Quartz

## So You Want to be a Data Scientist

## Three Key Reasons to Switch to Interactive Data Visualization – PHARMICA Consulting

## Discussion: What is big data anyway?

## Descriptive Analysis and Visualization of all variables in a snap!

## Conducting stratified analysis to test for confounding and interaction

## Free Online Sample Size Calculations and Visualizations

## 16 Free Data Science Books

## Scientific method: Statistical errors : Nature News & Comment

## Open data key to tackling neglected tropical diseases – SciDev.Net Sub-Saharan Africa

## The Age of Insight: Telling Stories with Data

## My ggplot2 cheat sheet: Search by task | Computerworld

## 10 things statistics taught us about big data analysis | Simply Statistics

## Descriptive Analysis: Take it easy!

Source: The raw facts on big data – The University of Sydney Business School

Read moreWhether you want to understand people’s preferences for a product, estimate proportion of people preferring a political party or estimate prevalence of a disease in a population, you will need to calculate the number of respondents sufficient for your survey objective.

Read moreWANTED: ‘sexy’ statisticians and workers competent in the culture of many other countries. Source: Four skills you need to know for the future | The New Daily

Read moreIn order to sprinkle some star dust into the contents of my book I’ve been doing a few interviews with various professionals from data visualisation and related fields. These people span the spectrum of industries, backgrounds, roles and perspectives. Source:

Read moreThis infographic breaks down the basics of data visualization. It shows how beginners can merge form and function, and design beautiful infographics. Source: Data Visualization: How To Merge Form & Function And Make Great #Infographics

Read moreThe lifeblood of the information age is data and the prevailing wisdom is that the companies like Zara that extract insights from data have an advantage over those that don’t. Source: If you don’t really understand big data how about

Read moreSource: R Programming for… by Roger D. Peng [Leanpub PDF/iPad/Kindle]

Read moreDespite the good intentions behind the movement to get people to code, both the basic premise and approach are flawed. The movement sits on the idea that “coding is the new literacy,” but that takes a narrow view of what

Read moreSummary: In which we attempt to answer the question, how does someone in school or recently out enter the exciting world of data science.There is no question Source: So You Want to be a Data Scientist

Read moreA picture is worth a thousand words. A switch to interactive data visualization can make your data picture appear clearer, more valuable, & more efficient.

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Interactive data visualizations is the future.

Read moreMany people talk about big data these days. Recent startups are moving towards exploring big data. But what exactly is big data?

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Read moreExploratory analysis and data visualizations are essential before building statistical models. Besides enabling identification of outliers and illogical values, these descriptive approaches discover patterns and trends in the data, and thus provide new insights about the data and guide inferential analyses.However, descriptive analyses are often ignored or not given due importance by students or investigators because they are laborious and time consuming. There is a tendency among naïve investig

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Read moreStratified analysis is a powerful statistical approach that allows you to test for confounding and interaction, but unlike logistic regression, it is quite simple and doesn’t distance you from your data. You can ‘see’ the associations and enjoy the insights gained from analysis.This approach is useful when you are interested in testing association between two categorical variables – say exposure and disease – by adjusting for a third categorical variable. If done correctly, it also enables you t

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Read moreStatulator is a free online statistical program that conducts statistical analyses, interprets the results and provides suggestions about reporting them. It was made available to general public last week and can be accessed by clicking here.It includes a suite of sample size calculators for six scenarios: estimating a single proportion, comparing two independent proportions, comparing paired proportions, estimating a single mean, comparing two independent means and comparing paired means. All sa

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Read more16 free data science books for the aspirational data scientist, covering

statistics, Python, machine learning, the data science process, and more.

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Read moreP values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume. Source: Scientific method: Statistical errors : Nature News & Comment

Read moreAccess to open data could enable researchers collaborate and boost the control of neglected tropical diseases. Source: Open data key to tackling neglected tropical diseases – SciDev.Net Sub-Saharan Africa

Read moreJournalism is undergoing a data-driven revolution. Pioneers in data journalism speak about the role and importance of using data in reporting, walk through some examples of their work, and share their thoughts on where the industry is headed. Featuring Ezra

Read moreHere’s your easy-to-use guide to dozens of useful ggplot2 R data visualization commands in a handy, searchable table. Plus, download code snippets to save yourself a boatload of typing. Source: My ggplot2 cheat sheet: Search by task | Computerworld

Read moreSource: 10 things statistics taught us about big data analysis | Simply Statistics

Read moreDescriptive analysis is an important first step for conducting statistical analyses. It gives you an idea of the distribution of your data, helps you detect outliers and typos, and enable you identify associations among variables, thus preparing you for conducting further

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