Your Data Analysis Takes How Long?

Reference: <a href="http://www.biblogg.no/">www.biblogg.no</a><br /><br /><div class="entry-author">Av <address class="author vcard"><a class="url fn n" href="http://biblogg.no/author/andy-cotgreave/" title="Andy Cotgreave">Andy Cotgreave</a></address></div><div class="entry-info"> – <abbr class="published" title="2013-10-13T20:36:50+00:00">13. oktober 2013</abbr></div><div class="entry-info"><abbr class="published" title="2013-10-13T20:36:50+00:00"></abbr><span class="entry-cat"><strong>Publisert i: </strong><a href="http://biblogg.no/category/forside/">Forside</a>, <a href="http://biblogg.no/category/forside/toppsak/">Toppsak</a>, <a href="http://biblogg.no/category/trender/">Trender</a>, <a href="http://biblogg.no/category/forside/utvalgte-saker/">Utvalgte saker</a></span></div><div class="entry-info"><span class="entry-cat"></span> </div><div class="entry-content clearfix"><!-- Start Shareaholic LikeButtonSetTop Automatic --><div style="clear: both; height: 3px; min-height: 1px; width: 100%;"></div><div style="clear: both; height: 3px; min-height: 1px; width: 100%;"></div><!-- End Shareaholic LikeButtonSetTop Automatic --><a href="http://biblogg.no/wp-content/uploads/2013/08/andycotgreave.bmp"><img alt="andycotgreave" class="alignleft size-thumbnail wp-image-4064" src="http://biblogg.no/wp-content/uploads/2013/08/andycotgreave.bmp" /></a><br /><br /><br /><strong>Andy Cotgreave, Social Content Manager at Tableau Software, looks at how analytics tools can help to save valuable time </strong><br /><br />Imagine, for a moment, that you’ve been given a task to analyse a dataset inside sixty minutes and share your results. How far do you think you would get in that time?<br />It’s a question I had cause to reflect on recently, after running «Fanalytics», a workshop for users of Tableau Public. In the workshop, we gave people a dataset and one hour to do something cool. Their results were astounding.<br />To understand why they were so impressive, let me provide a little context by considering how many of us work with data.<br />First, let me dispel a myth. Contrary to popular opinion, if you are using spreadsheets or traditional BI tools, it is quite possible to build beautiful charts. Unfortunately, each view of your data takes considerable time to build, Do you have that time to spare in your working life? What if the chart you take 10 minutes to build doesn’t answer your question? What if it inspires a new question? You have to go back and start again.<br />What if you could explore your data at the speed of thought instead? What if each mouse click changed the view instantly? This is what we call visual analytics: it allows you to find insight in your data at speeds unimaginable just a few years ago.<br />You’re probably wondering how all of this relates to the <span style="text-decoration: underline;"><a href="http://www.tableausoftware.com/about/blog/2013/6/fanalytics-session-wrap-feeding-your-passion-tcceu-23660" target="_blank" title="Fanalytics">Fanalytics competition </a></span>I mentioned earlier. Well, during the session, we gave our teams a list of every UK Number one album since 1956, downloaded from Wikipedia. The instructions they were given were to analyse the data and publish something interesting within one hour.<br />Did they deliver? Oh, boy, yes, and in ways that made my jaw drop. Each entry was different. The winning team analysed albums that had been to number one more than once, revealing perennially popular music, and the effects of sales on a musician’s death. Another team came up with an album explorer that found out which album was number one on your birthday. One team created a visually gorgeous dashboard, sure to engage anyone. A further team came up with a predictive model based around the likelihood of any album title to get to number one. You can see all the entrants on Tableau’s Fanalytics blog post. What was truly amazing was that they did this in one hour. Sixty minutes!<br />Unfortunately, many people are stuck with tools that are cumbersome or too hard to use. It often takes more than an hour just to connect to data. The simple lesson I’ve learnt from the recent session is that although some tools can make amazing charts, they are often unnecessarily complicated. With some, you need to fill in five steps in a property wizard just to draw a chart. In others, you are required to write custom scripts before you can start drawing anything.<br />The question we need to ask is whether we are using the right tools to answer questions quickly and in the most efficient way? If not, then perhaps it’s time to ditch these time hogs and focus on analytic tools that save you time instead!</div>

Your Data Analysis Takes How Long?

Reference: www.biblogg.no Av Andy Cotgreave – 13. oktober 2013 Publisert i: Forside , Toppsak , Trender , Utvalgte saker   Andy Cotgreave, ...

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Interview with Jill Dychè on Data Management in the Era of Big Data

<div class="entry-title">Guest blog: <a href="http://www.biblogg.no/">www.biblogg.no</a> </div><div class="entry-title"> </div><div class="entry-title">Av </div><div class="entry-info"><div class="entry-author"><address class="author vcard"><a class="url fn n" href="http://biblogg.no/author/jill-dyche/" title="Jill Dyche">Jill Dyche</a></address></div></div><div class="entry-content clearfix"><!-- Start Shareaholic LikeButtonSetTop Automatic --><div style="clear: both; height: 3px; min-height: 1px; width: 100%;"></div><div class="shareaholic-like-buttonset" style="float: none; height: 30px;"><div style="float: left; margin: 0px 5px !important; padding-top: 0px !important;"><iframe allowtransparency="true" frameborder="0" scrolling="no" src="https://www.facebook.com/plugins/like.php?app_id=207766518608&href=http://biblogg.no/2013/10/13/interview-with-jill-dyche-on-data-management-in-the-era-of-big-data/&send=false&layout=button_count&width=90&show_faces=false&action=like&colorscheme=light&font&height=21" style="border: currentColor; height: 21px; overflow: hidden; width: 90px;"></iframe></div><a class="shareaholic-fbsend" data-shr_href="http%3A%2F%2Fbiblogg.no%2F2013%2F10%2F13%2Finterview-with-jill-dyche-on-data-management-in-the-era-of-big-data%2F" href="http://www.blogger.com/null"></a><style type="text/css"> div.shareaholic a{background-image:url(http://biblogg.no/wp-content/uploads/shareaholic/spritegen/shareaholic-publishers-mini.png) !important;}.shr-generic-count{height:53px;width:57px;font-weight:bold;font-size:14px;padding-top:7px;text-align:center;background:transparent no-repeat scroll;background-position:0px -80px;color:black;cursor:pointer;text-decoration:none;display:block;}.shr-generic-count-compact,.shr-generic{height:23px;width:63px;font-weight:bold;font-size:14px;padding-top:7px;text-align:center;background:transparent no-repeat scroll;background-position:0px -50px;color:black;cursor:pointer;text-decoration:none;display:block;} .shr-7-count{background-position:-57px -80px;}.shr-7-count-compact,.shr-7{height:14px;width:55px;background-position:-228px -79px;}.shr-7-count-compact:hover,.shr-7:hover{height:14px;width:55px;background-position:-228px -100px;}.shr-7-count-compact:active,.shr-7:active{height:14px;width:55px;background-position:-228px -121px;}.shr_compact_div{background:#EFEFEF;border:1px solid #C1C1C1;float:left;font-weight:normal;height:14px;margin-left:1px;min-width:15px;padding:1px 2px 1px 2px;text-align:center;font-size:11px;display:block;margin-top:2px;margin-right:15px;line-height:1.3em;}.shr_compact_tick{float:left;position:relative;z-index:2;width:5px;top:5px;left:2px;border-collapse:separate;border-spacing:2px 2px;margin-top:2px;}.shr_compact_tick i{left:2px;top:-6px;border-right-color:#EFEFEF;font-style:italic;}.shr_compact_tick s{text-decoration:line-through;text-align:left;}.shr_compact_tick s, .shr_compact_tick i{border:solid transparent;border-right-color:#C7C7C7;display:block;position:relative;border-width:5px 5px 0 0;}</style><div class="shareaholic" style="float: left; margin: 0px 0px 0px 10px !important;"></div></div><!-- End Shareaholic LikeButtonSetTop Automatic --><a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche.png" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img alt="jill dyche" class="alignleft size-thumbnail wp-image-4240" height="150" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-150x150.png" width="150" /></a><br /><br /><br /><br /><strong></strong><br /><strong></strong><br /><strong></strong><br /><strong></strong><br /><strong></strong><br /><strong></strong><br /><strong></strong><br /><strong>Jill Dyche, </strong><span style="font-size: x-small;"><strong>Vice President of SAS Best Practices, was keynote speaker at SAS Forum Norway in September, and was interviewed by Lars Rinnan</strong> </span><br /><br />Jill, you delivered a strong keynote, and the audience was really attentive.<br /> You talked about big data and how to get the c-suite to listen. It sounds almost impossible. How do you get them to listen?<br /><br /><i><a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png"><img alt="jill dyche figur2" class="alignleft wp-image-4266" height="70" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png" width="50" /></a>You need to meet executives where they are. In other words: figure out what’s important to them now, and then map big data as the answer.</i><br /><i>Here’s an example: A large cable company sees an uptick in customer complaints in its call center. They have to add expensive headcount to the support staff. But they decide to incent customers to use social media interactions to ask for support or lodge complaints.  By adding social media transactions to customer profiles, the cable company can not only monitor valuable customers who may be at-risk, it can also “score” its brand reputation based on text analytics of social media interactions. They understand that over half of customer feedback comments are actually installation questions and not complaints. They develop customer support videos and post them on YouTube. Both questions and complaints are reduced, and support staff can be redeployed to cross-selling functions.</i><br /><br />Data governance is essential, but how do you get the CXO interested?<br /><br /><i><a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png"><img alt="jill dyche figur2" class="alignleft wp-image-4266" height="70" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png" width="50" /></a>Find the problem the CXO needs to solve, and explain how data enables the solution. Many senior executives don’t make the link between a business need and data. If you “deconstruct” the business problem into the data necessary to solve it, you can see the lights go on with executives. </i><br /><br /><br />How would you start a data governance process at a large company who has no clue of data governance?<br /><i>W<a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png"><img alt="jill dyche figur2" class="alignleft wp-image-4266" height="70" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png" width="50" /></a>e recommend starting with what I call a “small, controlled project.” Take a business problem, scope it down to a level where data can enable it quickly, and then implement a well-bounded process around data rules and policies necessary to address it.</i><br /><br /><br />How is data governance related to big data?<br /><br /><i><a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png"><img alt="jill dyche figur2" class="alignleft wp-image-4266" height="70" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png" width="50" /></a>Big data is like any other data: It requires policy making and oversight. In that respect big data should be beholden to larger rules. For instance, data from sensors or devices that may be streaming into your company should be handled in a different way. Is it sensitive? Is it defined? Is it targeted to be consumed by a department? An individual? A machine? All of these factors, and others, should inform the policies around that data. And that’s data governance.</i><br /><br />In your experience, are businesses giving data governance enough attention in terms of resources, technology and funding?<br /><br /><i><a href="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png"><img alt="jill dyche figur2" class="alignleft wp-image-4266" height="70" src="http://biblogg.no/wp-content/uploads/2013/10/jill-dyche-figur2.png" width="50" /></a>Only after they feel enough pain. Very few companies new to data governance actually say, “Hey! Let’s make sure we factor data governance into this new initiative.” Most have to experience the pain of not having the right data for the right business purpose. Go back to the days of Customer Relationship Management and remember how everyone thought they could just plug in a new tool? The validity of the data is directly proportional to the value of the resulting application. Data can no longer be considered an afterthought.</i><br /><br />Thanks you for sharing these valuable insights with the readers of biblogg, Jill!</div>

Interview with Jill Dychè on Data Management in the Era of Big Data

Guest blog: www.biblogg.no   Av Jill Dyche Jill Dyche, Vice President of SAS Best Practices, was keynote speaker at SAS Forum Norway in Sep...

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The Data Science Mindset

<h2>Intro </h2><section>Names like ‘R’, ‘SQL’, and ‘D3’ make data science seem more like alphabet soup than a deliberate practice of working with data. It’s so easy to get lost in the sea of acronyms, packages, and frameworks that we often find our students prematurely optimizing for the right toolset to use, unable to move forward until they have researched every available option. In reality, data science isn’t just about the tools. It’s a mindset: a way of looking at the world. It’s about taking advantage of our modern computers and all of the information that they’re already collecting to study how things work and push the limits of human knowledge just a little bit further. We have a favorite saying around here — data is everything and everything is data. If we begin with this mindset, a lot of data science approaches naturally follow.<br /></section><section><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDAg2tgYSGHswoblrjrS-nBRdAefFmVW09pelSXqLrgQLX8yL4x_-k2JMk06slwYqRHfqJOFhWxioNg2HlCrzqbsYVTnM9METgBo05US31gvsf48W155yd-h1J-6DOYsFxUrcgi9E-3077/s1600/day-of-week.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"></a> </div></section><section><h2>Store Everything</h2></section><section>Storage is cheap. Collect everything and ask questions later. Store it in the rawest form that is convenient, and don’t worry about how or even when you’re going to analyze it. That part comes later.<br /></section><section><h2>Use Existing Data</h2></section><section>We’re already storing data — let’s use it. When faced with questions, data scientists regularly adapt the query so that it can be approximately answered with an existing and convenient dataset. The best part of data science is discovering surprising applications of existing stores of data. For example, there is a plethora of satellite imagery of Earth. We can use this data to learn about <a href="http://www.youtube.com/watch?v=3ajFogCaRbs" target="_blank">fertilizer use in Uganda</a>, or use pictures of the Earth at night to estimate <a href="http://www-personal.umich.edu/~brianmin/MinEtAl_Detection_IJRS_2013.pdf" target="_blank">rural electrification</a> in developing countries.<br /></section><section><h2>Connect Datasets</h2></section><section>We’re storing everything, all over the world, inexpensively, for the first time in history. There are many lessons to be learned by utilizing more of this treasure trove. Don’t worry about making the best use out of a single source of data. Focus on connecting disparate datasets rather than tuning your models. Conventional statistics teaches a lot about how to choose analysis methods that are appropriate for your data collection approach and how to tune the models for a specific dataset.<br />Effective data science is about using a range of datasets, connecting the dots between one set of data and another, such as predicting restaurant health scores based on Yelp reviews. In machine learning speak: it’s often better to collect more features rather than spend days optimizing hyperparameters.<br /></section><section><h2>Anything Can Be Quantified</h2></section><section>Our culture loves to quantify. If you can turn it into a number, that number can be put into a table. Importantly, that table can now be processed by a computer.<br />A <a href="https://data.illinois.gov/Municipality/SSMMA-Combined-Sewer-Overflow/5yuf-j7kn?" target="_blank">spreadsheet about sewer overflows</a> is clearly data to most people, but what about a <a href="http://www.mo.gov/meetings/" target="_blank">calendar</a>? At first, a calendar might not seem like the sort of data that you analyze with statistics. However, you can also represent a calendar as a <a href="https://data.mo.gov/Government-Administration/Open-Meetings/au6r-w9n3" target="_blank">spreadsheet</a> and <a href="http://thomaslevine.com/!/socrata-calendars" target="_blank">as a graph</a>.</section><section><br /><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDAg2tgYSGHswoblrjrS-nBRdAefFmVW09pelSXqLrgQLX8yL4x_-k2JMk06slwYqRHfqJOFhWxioNg2HlCrzqbsYVTnM9METgBo05US31gvsf48W155yd-h1J-6DOYsFxUrcgi9E-3077/s1600/day-of-week.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgDAg2tgYSGHswoblrjrS-nBRdAefFmVW09pelSXqLrgQLX8yL4x_-k2JMk06slwYqRHfqJOFhWxioNg2HlCrzqbsYVTnM9METgBo05US31gvsf48W155yd-h1J-6DOYsFxUrcgi9E-3077/s640/day-of-week.png" width="640" /></a><br /><br /><br />Data science becomes a creative endeavor when peeling away the obvious variables presented to you. Maybe you have a bunch of PDF documents. You could easily extract the text in the PDFs and search through the content. Depending on the problem you are solving, these files hold more interesting information than just the text. You can get the page count, the file size, and the shapes of the pages and the <a href="http://thomaslevine.com/!/parsing-pdfs/" target="_blank">program that created it</a>. There is information hidden in many datasets that goes beyond what’s immediately obvious.<br />There is a lot of talk about the difference between different kinds of data. There’s “qualitative” vs. “quantitative” and “unstructured” vs. “structured.” To me, there isn’t much difference between “qualitative” and “quantitative” data, nor is there between “unstructured” and “structured” data because I know that I can convert between the different types.<br />At first, the registration papers of company might not seem like interesting data. They begin as paper, most of the fields are text, and the formats aren’t particularly standardized. But when you put them in <a href="http://opencorporates.com/" target="_blank">a database</a> in a machine-readable format, qualitative data becomes <a href="http://registries.opencorporates.com/" target="_blank">quantitative data</a> that can be used to supplement other data sources.<br /></section><section><h2>Send Boring Work to Robots</h2></section><section>We no longer live in an era where “computer” refers to someone who carries out calculations. Find yourself doing something over and over? Give it to the bots. As far as data analysis goes, modern computers can be far more effective at rote tasks, such as drawing new graphs with every update of a dataset.<br />Data collection is a prime example of a task that should be automated. A common scene in university research labs is swaths of grad students handing out paper questionnaires to participants of studies. The data scientist says: collect the data automatically and unobtrusively, using existing systems whenever possible. The supercomputers we carry in our pocket are a great place to start.<br />This mindset can be applied not only to the data, but also to the process itself. Rather than learning and remembering your entire analysis process, you can write a program that does the whole thing for you, from the original acquisition of the data, to the modeling, to the presentation of results to another person. By making everything a program, you make it easier to find mistakes, to update your analyses, and reproduce your results.<br /></section><section><h2>Tools</h2></section><section>Once inside the data science mindset, solving interesting problems becomes a function of data acquisition and processing. Computers can fit models and make predictions about datasets that are too big to wrap your head around and convert paper documents into electronic tables. They probably know more about you and your habits than you know yourself! Use the tools available to you, but don’t get caught up on the tools themselves.<br />Properly discussing these relevant tools is another post (maybe a book), but here’s one thought. While it always helps to have more education, you don’t need a PhD in math or computer science in order to create useful things. Loads of wonderful algorithms have already been implemented for you, and simple algorithms often work quite well. If you’re just getting started, focus on the “plumbing” that connects different datasets and systems together.<br /></section><section><h2>Data Science Mindset at Zipfian Academy</h2></section><section>Our course teaches many data science tools, but we also teach the data science mindset, because you need both to be a great data scientist. To this end, we organize our 12-week course by projects — such as a recommendation engine or spam filter — rather than software packages or algorithms. We teach the various tools in context of applied projects so students learn how to choose the appropriate tool and how to build the plumbing that connects them.<br />In the end, it’s not about the newest, trendiest framework or fastest data analysis platform. It’s about finding interesting insights from your data and sharing it with the world. Start small, get your hands dirty, and have fun!</section><br />

The Data Science Mindset

Intro  Names like ‘R’, ‘SQL’, and ‘D3’ make data science seem more like alphabet soup than a deliberate practice of working with data. It’s ...

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Becoming a Data Scientist – Curriculum via Metromap

<span style="font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">by </span>Swami Chandrasekaran<br /><br /><span style="font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">Data Science, Machine Learning, Big Data Analytics, Cognitive Computing .... well all of us have been avalanched with articles, skills demand info graph's and point of views on these topics </span><em style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">(yawn!)</em><span style="font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">. One thing is for sure; you cannot become a data scientist overnight. Its a journey, for sure a challenging one. But how do you go about becoming one? Where to start? When do you start seeing light at the end of the tunnel? What is the learning roadmap? What tools and techniques do I need to know? How will you know when you have achieved your goal?</span><br /><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">Given how critical visualization is for data science, ironically I was not able to find (except for a few), pragmatic and yet visual representation of what it takes to become a data scientist. So here is my modest attempt at creating a curriculum, a learning plan that one can use in this <em style="box-sizing: border-box;">becoming a data scientist</em> journey. I took inspiration from the metro maps and used it to depict the learning path. I organized the overall plan progressively into the following areas / domains,</div><ol style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px; list-style-image: initial; list-style-position: initial; margin: 0px; padding: 0px 0px 0px 35px;"><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Fundamentals</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Statistics</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Programming</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Machine Learning</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Text Mining / Natural Language Processing</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Data Visualization</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Big Data</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Data Ingestion</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Data Munging</em></li><li style="box-sizing: border-box; margin-bottom: 5px;"><em style="box-sizing: border-box;">Toolbox</em></li></ol><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;">Each area  / domain is represented as a "metro line", with the stations depicting the topics you must learn / master / understand in a progressive fashion. The idea is you pick a line, catch a train and go thru all the stations (topics) till you reach the final destination (or) switch to the next line. I have progressively marked each station (line) 1 thru 10 to indicate the order in which you travel. You can use this as an individual learning plan to identify the areas you most want to develop and the acquire skills. By no means this is the end; but a solid start. Feel free to leave your comments and constructive feedback.</div><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;"><span style="box-sizing: border-box; color: grey;"><em style="box-sizing: border-box;">PS: I did not want to impose the use of any commercial tools in this plan. I have based this plan on tools/libraries available as open source for the most part. If you have access to a commercial software such as IBM SPSS or SAS Enterprise Miner, by all means go for it. The plan still holds good.</em></span></div><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;"><span style="box-sizing: border-box; color: grey;"><em style="box-sizing: border-box;">PS: I originally wanted to create an interactive visualization using D3.js or InfoVis. But wanted to get this out quickly. Maybe I will do an interactive map in the next iteration.</em></span></div><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;"><span style="box-sizing: border-box; color: grey;"><em style="box-sizing: border-box;"><br /></em></span></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUvNZtZN1REa66E6n8VOJ-YjgRO9BYm41bDo5WZ7qjY2q6XArqHUwhKWGmALpmnMSasm6PxB5Zov9Kfg47x8hzv0u69bvbAuu0lhS2MHiXt-3RzLcaItkhO53nwAeu81VRNzInwl7ECLiC/s1600/RoadToDataScientist1.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="519" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgUvNZtZN1REa66E6n8VOJ-YjgRO9BYm41bDo5WZ7qjY2q6XArqHUwhKWGmALpmnMSasm6PxB5Zov9Kfg47x8hzv0u69bvbAuu0lhS2MHiXt-3RzLcaItkhO53nwAeu81VRNzInwl7ECLiC/s640/RoadToDataScientist1.png" width="640" /></a></div><div style="box-sizing: border-box; font-family: 'Segoe UI', 'Lucida Grande', 'Helvetica Neue', sans-serif; font-size: 15px;"><span style="box-sizing: border-box; color: grey;"><em style="box-sizing: border-box;"><br /></em></span></div>

Becoming a Data Scientist – Curriculum via Metromap

by  Swami Chandrasekaran Data Science, Machine Learning, Big Data Analytics, Cognitive Computing .... well all of us have been avalanched wi...

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THE NORWEGIAN BI BAROMETER

<h1 class="mh" id="8" style="background-color: white; color: #60605f; font-family: Arial, sans-serif; font-size: 1.2em; font-weight: lighter; margin-top: 0px; text-transform: uppercase; width: 740px;"><br /></h1><div style="background-color: white; color: #60605f; font-family: Arial, sans-serif; font-size: 0.8em;"><strong>The Norwegian BI barometer: A survey of BI maturity in the Norwegian market</strong><br />Are Norwegian companies best in class when it comes to using information for management purposes? Or are they laggards compared to other countries, relying on gut feeling and experience? For the first time there will be a large scale survey of BI maturity in Norway. The survey will give an indication of the level of maturity in using BI in Norwegian companies. The survey will point out which areas are more mature, and which require more attention, both in total and divided by industry and company size. Is your company more or less mature than the industry average? Which areas are most and least mature? Get useful pointers for your own BI efforts at this presentation.</div><div style="background-color: white; color: #60605f; font-family: Arial, sans-serif; font-size: 0.8em;"><br /></div><div style="background-color: white; color: #60605f; font-family: Arial, sans-serif; font-size: 0.8em;"><span style="text-decoration: underline;"><span style="font-size: small;"><a href="https://www.research.net/s/BIBarometeret" style="color: black; text-decoration: none;" target="_blank">Click here to answer the survey. Your opinion counts!</a></span></span></div>

THE NORWEGIAN BI BAROMETER

The Norwegian BI barometer: A survey of BI maturity in the Norwegian market Are Norwegian companies best in class when it comes to using inf...

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TICKETS

<article id="art0" style="background-color: white; clear: both; color: #60605f; float: left; font-family: Arial, sans-serif; margin-bottom: 20px; width: 740px;"><h1 class="mh" id="7" style="font-size: 1.2em; font-weight: lighter; margin-top: 0px; text-transform: uppercase; width: 740px;"><br /></h1><div style="font-size: 0.8em;"><span style="font-size: x-small;">GOBI2013 will be held in Oslo Spektrum June 10th 2013. When registering you get full access to the sessions, restaurants, cafes, expo and entertainment for the whole conference.</span></div><div style="font-size: 0.8em;"><span style="font-size: x-small;">The web shop for conference tickets will ease the j</span>ob of managing your tickets. Through the web shop you can buy any number of tickets and assign them to co-workers and/or friends who shall attend the conference. Each individual attendee is responsible for updating his or her contact information. When required information has been registered, the ticket will be sent directly to the attendee. Tickets are sent as a PDF containing a QR code on e-mail. Attendees must bring the QR code for registration at the conference site on June 10th. A conference pass will be created and handed out at registration.</div><div style="font-size: 0.8em;"><span style="font-size: small;"><strong>Price: 5.900 NOK (EarlyBird, until 15.04.2013)</strong></span></div><div style="font-size: 0.8em;"><span style="font-size: small;"><strong><span lang="EN-US">Price: 6.900 NOK (<span lang="EN-US">LateBird after 15.04.2013)</span></span></strong></span></div><div style="font-size: 0.8em;">Tickets for GOBI2013 are administrated by <a href="http://macsimum.no/" style="color: black; text-decoration: none;">Macsimum Event AS<br /></a></div><div style="font-size: 0.8em;"><a href="http://www.eventsystems.no/es/event/gobi/" style="color: black; text-decoration: none;" target="_blank"><span style="color: #00ccff; font-size: small; text-decoration: underline;"><strong>Buy your GOBI2013 ticket here</strong></span>.<br /><br /></a></div><div style="font-size: 0.8em;"><strong><span style="font-size: small;">Post Conference Seminar with Cindi Howson and Wayne Eckerson</span><br /></strong></div><div style="font-size: 0.8em;">Want to get the most out of the gurus while they’re in Oslo? Would you like to have a deep-dive seminar with BOTH keynote speakers? Attend the GOBI post-conference seminar on June 11th.<br /></div><div style="font-size: 0.8em;"><span style="font-size: small;"><strong>Price: 3.900 NOK (Without valid GOBI pass)</strong></span></div><div style="font-size: 0.8em;"><span style="font-size: small;"><strong>Price: 2.900 NOK (With valid GOBI pass from June 10th)</strong></span></div><div style="font-size: 0.8em;"><br /><span style="color: #00ccff; font-size: small; text-decoration: underline;"><strong><a href="https://www.eventsystems.no/es/event/gobi/" style="color: black; text-decoration: none;" target="_blank"><span style="color: #00ccff;">Buy your GOBI2013 Post Conference ticket here.</span></a></strong></span></div><div style="font-size: 0.8em;"><br /></div><div style="font-size: 0.8em;">Questions about tickets? Contact: <a href="mailto:webshop.gobi@eventsystems.no" style="color: black; text-decoration: none;" target="_blank">webshop.gobi@eventsystems.no</a></div><hr style="border-color: rgb(207, 207, 207); border-style: solid; clear: both; float: left; margin-bottom: 15px; padding: 0px; width: 740px;" /></article><article id="art1" style="background-color: white; clear: both; color: #60605f; float: left; font-family: Arial, sans-serif; margin-bottom: 20px; width: 740px;"><h1 class="mh" id="4" style="font-size: 1.2em; font-weight: lighter; margin-top: 0px; text-transform: uppercase; width: 740px;"><a href="" name="Contact us" style="color: #00b1ea;">CONTACT US</a></h1><div style="font-size: 0.8em;">For questions about the conference, please do not hesitate to contact us.</div><div style="font-size: 0.8em;"><span style="font-size: x-small;">General questions:<a href="mailto:info@gurusofbi.com" style="color: black; text-decoration: none;" target="_blank"> info@gurus</a></span><span style="font-size: x-small;"><a href="mailto:info@gurusofbi.com" style="color: black; text-decoration: none;" target="_blank">ofbi.com</a></span></div><div style="font-size: 0.8em;"><span style="font-size: x-small;">Phone +47 982 16 400.  Att: Lars Rinnan.</span></div><div style="font-size: 0.8em;">Questions regarding the program: <a href="mailto:info@gurusofbi.no" style="color: black; text-decoration: none;" target="_blank"> </a><a href="mailto:info@gurusofbi.com" style="color: black; text-decoration: none;" target="_blank">info@gurusofbi.com</a></div><div style="font-size: 0.8em;">Questions about partnership at GOBI2013:<a href="mailto:info@gurusofbi.com" style="color: black; text-decoration: none;" target="_blank"> info@gurusofbi.com</a></div><div style="font-size: 0.8em;">Questions about tickets and Webshop: <a href="mailto:webshop.gobi@eventsystems.no" style="color: black; text-decoration: none;" target="_blank">webshop.gobi@eventsystems.no</a></div><div style="font-size: 0.8em;">Other practical questions: <a href="mailto:info@gurusofbi.com" style="color: black; text-decoration: none;" target="_blank">info@gurusofbi.com</a></div><div style="font-size: 0.8em;">Phone +47 975 22 172. Att: Anette Aass Flølo</div></article>

TICKETS

GOBI2013 will be held in Oslo Spektrum June 10th 2013. When registering you get full access to the sessions, restaurants, cafes, expo and en...

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Strategy is important, but it is the execution that counts!

<div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><a href="http://biblogg.no/wp-content/uploads/2012/10/jan-johanson.jpg" style="color: #cc0000; outline: none; text-decoration: none;"><img alt="jan johanson" class="alignleft size-full wp-image-2913" height="150" src="http://biblogg.no/wp-content/uploads/2012/10/jan-johanson.jpg" style="background-color: #f3f3f3; background-position: initial initial; background-repeat: initial initial; border: 1px solid rgb(221, 221, 221); float: left; margin: 0px 1em 1em 0px; padding: 4px; text-align: center;" width="150" /></a></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><br /></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><br /></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><br /></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><strong> Jan Johansson, VP Nordic branch of the Palladium Group</strong></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;"><br /></div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;">You’ve developed a brilliant strategy that promises impressive results—leaps in profitability, unprecedented workforce productivity, increased efficiencies, major inroads into your competitors’ market share, or improved mission outcomes. But can you execute that strategy—put it into action through the right day-to-day processes, operations, and technologies?</div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;">90% of organizations fail at execution. That’s because they lack a comprehensive, disciplined system for managing the implementation of their strategy.</div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;">This session describes how to make strategy execution a long-term core competency in your organization—no matter what its industry, where it operates, or whether it’s a corporation, not-for-profit entity, or government agency.</div><div style="background-color: white; color: #333333; font-family: 'Lucida Grande', Arial, Helvetica, sans-serif; font-size: 12px; line-height: 18px; margin-bottom: 1.25em; padding: 0px;">Check out this presentation at the Gurus Of BI (GOBI) conference on June 1oth: <a href="http://www.gurusofbi.no/" style="color: #313428; text-decoration: none;">www.gurusofbi.no</a></div>

Strategy is important, but it is the execution that counts!

 Jan Johansson, VP Nordic branch of the Palladium Group You’ve developed a brilliant strategy that promises impressive results—leaps in prof...

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