I feel it would be interesting to get to know other each other and why you are participating on this beta. As data science is fairly new, knowing about each other's background would provide us a better picture of the community.
Post your responses as answers to this question. Please try to mention:
So, welcome to Data Science Stack Exchange! Go ahead and introduce yourselves!
I don't consider myself as a "data scientist", but at my workplace, the (research about) topics that I personally consider as being part of "data science" play an important role: Retrieval, Statistics/DataMining, and these particularly in combination with visualizations.
Thus I'm here to read interesting questions about these topics and learn from the answers, and possibly write some questions on my own once I think that I'm able to ask good questions that will be valuable for others.
(BTW: Although the idea of being a "community" is not as important on a Q&A site as, for example, in a forum, I don't understand the downvote of the question: I think that having an idea about who is visiting such a site (and why) can help to more clearly define this site, and if I understood correctly, that's the purpose of a private beta)
I like this question because 'Data Science' is such a ambiguous phrase, and I'm intensely curious about others in this role.
During my graduate studies in applied math, I realized academia wasn't for me. I finished and looked for interesting roles in the industry. I realized I couldn't do anything productive without learning programming languages, so I spent the following year learning as much R and Python as I could. With a background in machine learning, I fell into this 'data science role'. Most of the time, I feel the classic 'imposter syndrome', where I'm in a data science position with a large company and feel I'm flying by the seat of my pants most of the time. I'm beginning to realize that maybe 'data science' is supposed to be like that and there is no cure-all methodology for problems. I've always been a sort of 'jack-of-all-trades' (master of none), or at least I like to think so, and my work let's me do exactly that. My work is 90% data acquisition and cleanup, 9% meetings/report making, and 1% writing statistical/machine learning/text mining code. I enjoy what I do, and like to think it makes a difference.
I've fell in love with stack overflow since I've started learning R/Python, and can't wait to contribute/use this new resource!
While creeping around area51 I saw this project comming up and committed to it, as I really like this part of my work.
I'm no scientist neither, a few month ago I started to work on some big data problems that need to be solved related to eCommerce and productdata/customers.
My first project related to big data was to build a dataminer that made predictions of productcategories and other attributes while reading the productdata like description, name etc. for millions of products.
I hope to read alot of new stuff I don't know yet and perhaps help some out with an answer.
I'm an energy and buildings researcher mostly interested in understanding how to identify inefficiencies in real buildings.
I think I am a data scientist. I code all the time and use my code to analyse lots of data in order to create academic publications as well as useful tools. See this example of what I have been playing with recently.
I use many tools in my work, mostly I focus on python for general programming tasks.
In particular I use:
csv, xlrd/xlwt and json modules for managing data in filesSQLAlchemy for databases (mostly mySQL though moving to NoSQL in next project)Numpy for data munging and basic analysis workhorsepandas for data munging and basic analysis workhorse (only in last six months)pyramid and cornice for building server side applications (hopefully RESTful)matplotlib for most plotting applications, particularly for publicationMy web apps use D3.js extensively, I have used angular.js and ember.js and often use JQuery.
I'm on this site because I use a lot of tools and wouldn't consider myself a master at any of them. I usually get most of my Q and A from stackoverflow but I can see this community growing into a pool of more specific experts in data science. I hope to help the community and get help from the community.
I am a post graduate in computer science and have been working with cloud computing for past few years. I have mainly worked with OpenStack and some virtualization tools like KVM, vagrant etc.
Few weeks ago, I attended a conference on Big data and there was a presentation about role of data scientists. That fascinated me. Since then I have been following every Big data related resource to help myself to learn this new field.
I got there through a reddit post and hope to engage in some useful conversation.
I'm a computer science undergrad, aiming to pursue master's degree. So far, I've been working on data mining and distributed computing as a research assistant, and plan to keep working with distributed/high performance computing during master's course. I've worked on a project to process and analyze spam data, and I'm currently developing a distributed frequent pattern mining application.
I accidentaly laid eye on a side banner while in SO, and saw the invitation for joining this project. I really enjoy the topic, and find it very useful to both learn from others and contribute when possible.
I am software engineer by trade for more than a decade and a programmer by passion for around 15 years, currently pursuing a MS (Computer Science) degree. I have taken several data science related courses during my MS studies.
Apart from general interest in data-mining, in my day job I see a lot of untapped potential in customer data we gather (or can gather) for improving our products.
I am a software engineering undergrad interested in operating systems and robotics. I have been part of a few research oriented projects, and am aiming to pursue a Master's from next year.
In one of the projects I was involved with, a significant portion of digital signal processing was involved in which I ended up collecting, and analyzing data, as well as automating the data visualization process. I had done a bit of R previously, but this project is where I really applied it for the first time (and saw a small part of R's power).
I am interested in data science primarily to help me make sense of data that I may see and analyze in future projects. I am currently taking Coursera's Data Science Specialization (completed 2/10 so far) to gain a more formal understanding of data science methods. My choice of tools are R primarily and Python from time to time.
I found the proposal on A51, and felt it could become an excellent resource over time.
I am a civil engineer-in-training. As an undergraduate, I focused on topics of energy and environment, through the lens of public infrastructure and the built environment.
I have been an off-and-on hobbyist in programming and web design since I got my first computer in the early '90s, using the internet and books to give myself an informal education. I work for an environmental regulatory agency where I mainly use Python and MySQL. At home, I am enjoying my first in-depth experience with C and Linux, thanks to CS50x.
The closest thing I have to formal education in data science is a couple classes on linear regression and spatial data (GIS). I did a small project looking for correlations between census data and renewable energy production as a percentage of states' overall energy portfolios. What got me really interested in data science though was my work, where I spend a lot of time with my head stuck in public data, and a side project with one of my professors where I spent some time learning about distributed sensor networks, and more specifically the Air Quality Egg project.
I am on this site to browse for tools and resources that will make my life easier at work, and to contemplate more formal education in this area. I'd like to help make this public beta successful, but I don't feel like I have good questions to ask because of my inexperience with the subject.
Approximately 25 years ago, one of my friends bought me an already-old Ice Felix HC85. Three weeks later I finally came out of my room and showed to my father my first program, a chess interface which was able to move pieces only. I felt trapped and free at the same time.
I had enough luck to often put myself in situations for which I was not prepared. I have a deep respect for well-done science in general, and I spent the rest of my life learning, marveling at the math, computer science, physics pearls.
Three years ago I participated at a machine learning contest, knowing nothing about statistics, machine learning and so. I was trying to find an answer to the question: "What an experienced programmer can do in this field without apriori knowledge?". I did well, but the most important fact is that the contest was held for an year, and I spent that year thinking on how to find the truth when it is covered with noise?
After the contest was done I knew that I will do that for the rest of my life and I started to learn statistics and machine learning properly. I decided also to write for myself the best set of tools, because the only way I know in order to learn is the hard way. I am far away, but that does not hurt me, because there are a lot of good things to do, a lot of good problems to solve and an infinity of wonders to start my mind at. And now, I am right here.
I am more of a self taught technical hustler. I prefer to work in Clojure, but I am dangerous in Python, Javascript, and SAS. I am also data scientist, 7 years in with experience running the gamut. In my journey I have done NLP, predictive model building, fraud detection, realtime bidding, geo spacial analysis, and so much ETL.
Currently, I am lead data scientist at a startup, and I enjoy taking on a lot of side projects on top of that.
What interests me in data science is the ability to exercise my curiosity muscle. My data science journey can be traced back to a book I read in high school: "How to Lie with Statistics", and one early on in my career: "The Black Swan". Each was an eye opening experience that only propelled me further down the rabbit hole.
I am on this site is because I believe a community is only as strong as the voices holding it up, so if my experience can help others I am more then willing to share.
I have a background in sociology and computational science. In a couple weeks, I am starting as a data scientist at Hudl, where I will predict athlete performance, model sports games and answer other interesting data questions.
I am here because there's a ton of crap on the internet and I feel very strongly about data science surviving as a legitimate discipline. I am hopeful that this stack exchange can help.
I'm a BI Developer in the energy industry, and I'm doing an MSc in Computing while I work. My most immediate (and pressing) link to data science is that I'm doing my dissertation on data mining in a set of energy data. It's been a lot to learn given my background (English degree, followed by several years working in application support and development, followed by a year in BI, while doing a Computing MSc which doesn't touch data mining or machine learning topics). I found this site while looking for answers to questions I had about how to carry out some of my dissertation work.
The dissertation won't be the end if my involvement in data science, though; it's been the realisation of a long-term desire to work with data in this way. There are a number of things which I now realise all rest within data science which I'd always been intrigued by, and I suspect that if I were younger and going through school now, I'd probably be aiming myself at a related degree. My hope is to continue some of the things I'm working on for my dissertation in my workplace, and thus continue significant learning in the field of data science. As I find this stuff fun, I'll probably be doing it in my own time as well, on other data sets!
Areas I'm especially interested in at the moment (some very much influenced by my dissertation or available work data sets) include time series analysis, forecasting, useful Python libraries (pandas, scikit-learn, iPython, etc.), and data visualisation. I'm also interested in the trend towards highly reproducible research, which I first ran into after finding iPython.
