Thursday, August 24. 2017Data science = marketing + advertising(Note: This is a follow-up rant of my pessimistic conclusion about what data science means from two years ago.) Let me come straight to the point: Data science is synonymous for marketing. Period. Do not let yourself be misguided by online data science/machine learning/statistics lectures which only cover topics from the mathematical or programming area. These will only take up 5% of your work! The rest is 60% hot-air blowing, 50% managing/organizing and 40% delegating to juniors or externs, making that a total workload of 155% (60+ h/week), with expectations on you to generate projects and to build Big Data strategies. (After all, you’re smart, right? Otherwise you wouldn’t know that much math and AI. You’re smart, thus you can land big projects, right?) And the area of application is advertising, the data is customers, the outcome is customers—more people buying more stuff. No, thanks for the offers, but I won’t work in advertising. Who actually is making Big Data a hot topic? It’s those who create these solutions in the first place and sell them to companies who use these in their marketing processes. “Data is the new oil.” Really? So, how do you power your cars with data? BS! We’re still living in the oil interval. It’s so sad that AI is not solving any problems. It’s used for making game characters act smarter, it’s used for more effective advertising, it’s used for making call centers obsolete (by executing voice commands), it’s used for shopping agents (by executing voice commands), it’s used for autonomous driving of cars, vacuums and lawnmowers. It’s entirely used for making us (fat first-world people) even more lazy (fat), even more consumeristic (fat) and even more entertained (fat). These are solutions looking for a problem, but they can’t find one. Seriously, I see much more potential in the blockchain, which deserves its own rant soon. Wednesday, July 8. 2015A pessimistic conclusion about what data science means(Note: This is a rant, so what I’m trying to say is possibly written between the lines.) 2012, three years ago, I was working in the context of computer vision, teaching computers to see. While this is still an exciting field, yielding exciting technology, no one is really making money there so far, because these are solutions looking for a problem—there is no itch to be scratched. Our department was selling tunnel surveillance systems to the traffic industry, which was quite a niche and didn’t contribute to getting our company out of financial trouble. I started a learning phase, trying to get deeper into that machine learning thing, seeing myself as a technical expert in a few years, being known for bringing complex theoretical concepts to life in successful solutions—at a place where such skills throw off money. During generic research I collected more and more knowledge about the new hot field called data science, a magical mixture of statistical modeling and modern computer technology with its application in business. Since media mentioned IBM as player in the first row, I got in touch with their local office. And really, they hired me! However, I found myself placed onto the wrong track: I was expected to ensure that others do the work I was interested in doing, to generate projects, to devise proposals from zero to signings, to tell bank reps that they had to understand their customers as individuals to compete in today’s market. I was definitely not needed as a mathematician with a knowledge of data mining algorithms there. They needed business economists, marketers and sellers with an understanding of industries. The actual work that I was interested in doing—hacking fancy predictive models—would be delivered by folks who work at external business partners. How could that have happened? Both sides seemed to have had different expectations and interpretations. So, I was immediately job hunting again, and data science disappeared from my career radar during my way back to the software engineering world. At my current employer, I’m somewhat known as the guy who knows about big data (although I haven’t ever tried Hadoop) and data mining (although some of my coworkers are “real” statisticians). But during the recent months I concluded that all this data science is just one good old thing: marketing. The big part that actually defines data science is totally not explained by its name: It’s definitely and exclusively solving business problems. Data mining, on the other hand, has different interpretations. I, too, was blinded by what tech people see when hit with this buzzword: Hadoop, MapReduce, statistical algorithms, other fancy formula-heavy or technological stuff, applied to data of manifold origin. The business folks however have that marketing interpretation: Data mining is finding more people to sell stuff to. Data mining is market basket analysis (what stuff people buy), upselling (more expensive stuff), cross-selling (additional other stuff), understanding a company’s customers (people who buy stuff) to prepare marketing campaigns (telling people to buy stuff). Hey, business analyst, find more people to sell our stuff to! Oh, you’re a data scientist? Well, what difference does it make? Find more people—they might be customers already, possibly thinking about leaving us, or they aren’t our customers just yet. Or, possibly create a new product. Data mining is also about creating more stuff to sell to more people. So, be careful not to mistake data science with data mining. As a data scientist, you won’t just practice R programming, cleaning data, data analysis, statistical inference, or creating data products. If someone wants to hire a data scientist, they are looking for a business professional who, pointing at data in a spreadsheet, tells CEOs how they should transform their company. See, sometimes, someone tries to headhunt me for “[…] acting as a partner for marketing executives and collaborating with colleagues in management accounting […] Developing procedures to measure marketing campaigns on a global level together with managers and executives in marketing and sales […] identify new business opportunities […] Demonstrate business acumen […]”. Only rarely it goes like “[…] work with complex, varied, high-volume data sets that have real meaning for our customers’ health and wellbeing […] Identify patterns and correlations of a user’s fitness data […] Good statistical, mathematical and predictive modelling skills to build the algorithms […]”—Wait, what, Runtastic are Austrian!? (Or rather: Runtastic are awesome although they are Austrian!?) Maybe that topic comes back to me once that pile of sensor data has become higher and the internet of things takes off. But I’m not in my twenties anymore, so the doors and clefts to slip through have become narrower. Sunday, August 12. 2012Link roundup, week 32/2012
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Sunday, August 5. 2012Link roundup, week 31/2012
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Sunday, July 29. 2012Link roundup, week 30/2012
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Friday, July 20. 2012Link roundup, week 29/2012It took me quite a long time to discover that my favorite knowledge management tool, Diigo, provides a feature to post one’s bookmarks to a blog. As I often had the desire to repost certain links I stumbled upon, I will do that occasionally from now on, mainly about everything from the topic pool of data mining (and related buzzwords), with flavors ranging from theory to applications, from technology to business. (I can’t really do that to social media sites, as it’s almost impossible to explicitly consume posts topic-wise. So, blogs aren’t really obsolete—yet.) Btw, Diigo is really awesome: You can highlight text on webpages and add annotations to help understanding an article and create a summary on the fly, right while going through it. In this sense: If you want to be briefed, read at least this. (And don’t worry, the next episodes will contain less content; this one ranges back a few weeks.)
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Monday, June 30. 2008Wir verlassen die StadtHeute haben wir den Vertrag für unsere neue Wohnung unterschrieben. Sie befindet sich deutlich außerhalb von Wien. Nachdem ich vor Dass ich Kinderzimmer erwähne, führt auch schon zum Hauptgrund, warum wir nicht (mehr) in Wien leben wollen: Da wir selbst Landeier sind und mit dem tristen Flair des Die Ortschaft, in der wir nun leben werden, ist relativ beschaulich, aber groß genug, um die wichtigste Infrastruktur (Schulen, Ärzte, etc.) zu beherbergen. Vielmehr liegt die Entscheidung dafür aber in der strategischen geografischen Lage: Es handelt sich um einen Bahnknotenpunkt, der Weg zur Arbeit nach Wien dauert genauso(!) lang wie der bisherige quer durch die Großstadt, und zwei größere Städte sind – sogar mit der Bahn – nur einen Katzensprung entfernt. Ein Hausbau kam für uns nicht in Frage, da dann viel weniger finanzieller Spielraum bliebe für einen guten Lebensstil. Allerdings wird der Bau erst im Laufe des Sommers fertiggestellt und im Herbst übergeben. Am Freitag haben wir den Parkettboden ausgesucht. Wir können’s kaum erwarten! Tuesday, December 18. 2007Project summaryWhile being employed 40 hours/week I started to repeat basics in functional analysis in January 2006. In April I started to do some general reading on the subject of time-frequency analysis. I wanted to have the topic of my Master’s thesis set until May, but it took me until August to file it with the working title “Gabor Analysis for Image Processing”. The finish of my thesis had originally been targeted for Christmas 2006, but it soon was clear that it would also take the whole spring of 2007. With May 2006 I reduced my working times to Some statistics: It took me 8 months to read up on time-frequency analysis (while being employed). I was in the official status of a graduand for 16 months. I authored my thesis within 9 months.
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