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                <h2>applied mathematician/data scientist/writer</h2>

		<p class="text-light">

		this github page describes some of my statistical, data analytic and coding interests and practices. for a more 		comprehensive view of what i do, follow the links below:</p>
		<p class="text-light">i'm an applied mathematician, freelance writer, hacker & educator currently working at <a href="http://www.datacamp.com">datacamp</a>, an online, interactive education platform for all things data science. You should check it out! my job there is to build out the python curriculum: if you love python & are passionate about education, you should get in touch because i'd love to discuss creating a course with you! you can also find many of my research interests 
		<a href="http://www.hugobowneanderson.com">here</a> and much of my work as a freelance writer
		<a href="http://www.hugobowneanderson.com/writing/">here</a>.</p>
		
		
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                    <h2 class="section-heading">statistics, data analysis, data science</h2>
                    <p class="text-light"> until recently, i worked in basic science research for a number of years and much of my work on campuses (yale university, max planck institute for cell biology & genetics, dresden) involved consulting with experimentalists on best statistical practices & methodologies. in this work, i aimed to provide 		experimentalists with a statistical and data analytic framework whereby they may view the processes of statistical inference 		as not 	merely necessary evils that occur between doing experiments and publishing results, but as intuitive ways of thinking 		that can help with all steps of the scientific process, from exploratory research to the posing of well-formed scientific 		questions to experimental design. Straight off the bat, experimentalists can think about questions such as ‘how many data 		points will i need to find a statistically significant result?’ and ‘what type of experimental set-up is required to retrieve 		the signal-to-noise ratio necessary to validate anticipated results?’</p>
		<h3>practical statistics workshops</h3>
		<p class="text-light">to this end, i have taught formal workshops on statistical inference for experimentalists at the max 		planck institute for cell biology and genetics, dresden, and at yale university, new haven, along with more informal 			workshops conducted in locations such as the marine biology laboratory, woods hole. in fall, 2015, i taught a ’practical 		statistics for experimentalists’ workshop in the molecular biophysics & biochemistry department (mb&b) at yale university to 		15 graduate students and postdocs from both mb&b and yale medical school. there were 4 workshops in total: the 1st was on (click 		on links for workshop notes) <a href=Workshops/Workshop_I.html>plotting data, summary statistics and the central limit 			theorem</a>, the 2nd on <a href=Workshops/Workshop_II.html>statistical hypothesis testing</a>, the 3rd on 
		<a href=Workshops/Workshop_III.html>model fitting and model selection (mle, linear regression, nonlinear least squares)</a> 		and the 4th on 	<a href=Workshops/Workshop_IV.html>bayesian inference</a>. The emphasis was on developing the 				experimentalists’ practical statistics toolkit in terms of i) knowledge of statistical tools & ii) practical ability to 		implement them, using the programming language R. I will teach a similar workshop at yale in early 2016. </p>
		<h3>(data) scientific reproducibility</h3>
		<p class="text-light">statistical literacy is a huge challenge for both the natural & social sciences. another massive 	challenge is <i>reproducibility</i>. ioannidis’ 2005 paper 
<a href=http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124><i>why most published research findings are false</i></a> gave a precise formalism to a serious, endemic problem facing scientific communities in all fields. i am interested in and committed to the development of robust statistical frameworks that will contribute to the reproducibility of scientific results as a whole, in contrast to the messy toolbox of statistics that is currently used in high and low-impact journals alike. papers such as ioannidis’ cited above & cummings’ 2013
<a href=http://pss.sagepub.com/content/25/1/7><i>the new statistics: why and how</i></a>
 are a great a place to start to think about potential avenues, for those interested. this issue of <i>reproducibility</i> is also one that will plague data science and big data and so is worth investing in at all levels of society, not merely the academic.<commit_msg>professional changes<commit_after><!DOCTYPE html>
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                <h2>applied mathematician/data scientist/writer</h2>

		<p class="text-light">

		this github page describes some of my statistical, data analytic and coding interests and practices. for a more 		comprehensive view of what i do, follow the links below:</p>
		<p class="text-light">i'm an applied mathematician, freelance writer, hacker & educator currently working at <a href="http://www.datacamp.com">datacamp</a>, an online, interactive education platform for all things data science. You should check it out! my job there is to build out the python curriculum: if you love python & are passionate about education, you should get in touch because i'd love to discuss creating a course with you! you can also find many of my research interests 
		<a href="http://www.hugobowneanderson.com">here</a> and much of my work as a freelance writer
		<a href="http://www.hugobowneanderson.com/writing/">here</a>.</p>
		
		
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	<a name=stats></a>
	<!-- Intro Section -->
    <section class="intro">
        <div class="container">
            <div class="row">
                <div class="col-lg-8 col-lg-offset-2">
                	<span style="font-size: 60px"></span>
                    <h2 class="section-heading">statistics, data analysis, data science</h2>
                    <p class="text-light"> until recently, i worked in basic science research for a number of years and much of my work on campuses (yale university, max planck institute for cell biology & genetics, dresden) involved consulting with experimentalists on best statistical practices & methodologies. in this work, i aimed to provide 		experimentalists with a statistical and data analytic framework whereby they may view the processes of statistical inference 		as not 	merely necessary evils that occur between doing experiments and publishing results, but as intuitive ways of thinking 		that can help with all steps of the scientific process, from exploratory research to the posing of well-formed scientific 		questions to experimental design. Straight off the bat, experimentalists can think about questions such as ‘how many data 		points will i need to find a statistically significant result?’ and ‘what type of experimental set-up is required to retrieve 		the signal-to-noise ratio necessary to validate anticipated results?’</p>

		<h3>practical statistics workshops</h3>
		<p class="text-light">to this end, i have taught formal workshops on statistical inference for experimentalists at the max 		planck institute for cell biology and genetics, dresden, and at yale university, new haven, along with more informal 			workshops conducted in locations such as the marine biology laboratory, woods hole. in fall, 2015, i taught a ’practical 		statistics for experimentalists’ workshop in the molecular biophysics & biochemistry department (mb&b) at yale university to 		15 graduate students and postdocs from both mb&b and yale medical school. there were 4 workshops in total: the 1st was on (click 		on links for workshop notes) <a href=Workshops/Workshop_I.html>plotting data, summary statistics and the central limit 			theorem</a>, the 2nd on <a href=Workshops/Workshop_II.html>statistical hypothesis testing</a>, the 3rd on 
		<a href=Workshops/Workshop_III.html>model fitting and model selection (mle, linear regression, nonlinear least squares)</a> 		and the 4th on 	<a href=Workshops/Workshop_IV.html>bayesian inference</a>. The emphasis was on developing the 				experimentalists’ practical statistics toolkit in terms of i) knowledge of statistical tools & ii) practical ability to 		implement them, using the programming language R. I will teach a similar workshop at yale in early 2016. </p>
		<h3>(data) scientific reproducibility</h3>
		<p class="text-light">statistical literacy is a huge challenge for both the natural & social sciences. another massive 	challenge is <i>reproducibility</i>. ioannidis’ 2005 paper 
<a href=http://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.0020124><i>why most published research findings are false</i></a> gave a precise formalism to a serious, endemic problem facing scientific communities in all fields. i am interested in and committed to the development of robust statistical frameworks that will contribute to the reproducibility of scientific results as a whole, in contrast to the messy toolbox of statistics that is currently used in high and low-impact journals alike. papers such as ioannidis’ cited above & cummings’ 2013
<a href=http://pss.sagepub.com/content/25/1/7><i>the new statistics: why and how</i></a>
 are a great a place to start to think about potential avenues, for those interested. this issue of <i>reproducibility</i> is also one that will plague data science and big data and so is worth investing in at all levels of society, not merely the academic.
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