Five Reasons Why “Data Miner” is the Best Job in the World

panner_smallYes. I do believe that “Data Miner” is the best job on Earth. I will give you five reasons why I think so. Of course, I’m a data miner. I’m thus not objective, but who cares? Here is why everybody should be jealous about data miners:

1. We can predict the future
Yes, we can. Of course, this is not 100% sure, but still we predict the future. As long as we have enough data with some patterns inside (the famous gold nuggets), we can predict the future. What product will this customer buy next? What will be the pollen concentration in the air tomorrow? How will the stock market evolve in the following week? Yes, to some extend, and with some errors, we can predict the future. Isn’t that cool?

2. We can change our job without changing it
Sounds strange? Let me explain this. Data mining is a field where we apply machine learning and statistical techniques to some concrete problems in a certain field. Every new project may cover a different field. This gives you the opportunity to discover and learn new domains without changing your job. While working as a data miner, I have – in six years – discovered fields such as meteorology, civil engineering, finance and telco. Isn’t this great?

3. We can easily impress friends and family
This is not the case in every job. Being a data miner, it is. Of course, we could use simple terms such as “data, analysis and results” to explain what we do. However, we prefer to use terms such as “random set of points, support vector machines and score probabilities” instead. It is much more impressive, so why not use them? Yes, we are like this. Aren’t we awesome?

4. We can spy on anybody
Who has never dreamed of being a spy like James Bond? Being a data miner, we are kind of spies, at least according to the non-technical news. We steal data and we mine them to discover very personal information. We break the privacy of every human being on Earth by spying on them. Yes, we do that in our everyday job, and to be honest we love that. Of course, we could stop, but it is too much fun to play the spy. Isn’t it?

5. We never fail
Sounds impossible? It isn’t: we are data miners. Which means that if we find patterns in your data, we are good. If we don’t – it’s not our fault – it means there are some data quality issues (missing values, not enough data, etc.). But it’s not our fault. And if you insist, we can still find something for you. You bet? Give us any data (even random!) and we will find some patterns using clustering, for example. This is like finding patterns in clouds. If you look long enough, you will always see an image appear. Isn’t it beautiful?

If you think of other reasons, feel free to comment on this post and help me explain why “data miner” is the best job on Earth πŸ™‚


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Comments Icon32 comments found on “Five Reasons Why “Data Miner” is the Best Job in the World

  1. NOBODY can predict the future. In the real world, everybody should be wary of geeks with formulas and models. Saying that you can predict the future is simply ridiculous.
    “We can impress family and friends”, why do you have to nerdify data mining? no wonder why data mining hasn’t gone mainstream. And no, the fact that you may have many important clients doesn’t mean that is being adopted heavily!

  2. @Michael: Of course we can predict the future… we are data miners! πŸ™‚ Well, I thought my smiley at the end of the post was clear enough, but it seems not to everybody…

  3. #5 is the best. I see it constantly in my work. Even when I can’t find what I’m looking for, I find something (ie. data quality problems that need fixing).

    Unfortunately, #3 doesn’t work for me. because I’m required to spend my time defining the terms I’m using before they’ll be impressed. Even then most of the time they end up confused.

  4. About impressing people / predicting the future, unfortunately there seems to be an emerging trend consisting of blaming quants and their formulas for the economic situation.

    For example see Wired’s “Recipe for Disaster: The Formula That Killed Wall Street”: The article claims that David X. Li’s Gaussian copula function (among others) is one of the root causes behind the crisis.


    At least, “Data Miner” is not a dirty word yet (unlike “Banker”) πŸ™‚

  5. @Tommy: I’m sorry for you man, #3 is maybe the best one πŸ™‚

    @Dominic: I’m quite confident that in a few decades, the term “data miner” will be as dirty as “banker” πŸ˜‰ Thanks for the link!

  6. Priceless πŸ˜€

    I should visit your blog more often (or using rss). I may miss something.
    All the best for 2010.


  7. That explain why everybody wants to be a data miner. I had a boss who would go ” have you tried the K nearest neighbor of the Bayesian Probabilities..” he had no clue of what data mining is but he couldn’t help spewing out all sorts of gibberish stuff.

  8. Hi sandro
    i was searching a blog like yours for a long time.
    Finally i gotta gud one..
    your posts are very good and informative.
    since am doing my masters degree,i took a course
    in data mining as well as data warehousing.
    since am interested in data mining
    I want to do atleast 2 month internship in data mining area.
    Am beginner in this arena,i donot know much about
    it but have a craze and very eager to delve into
    any research related to mining.. so could u guide me?

  9. @Kalaivani: Thanks for your comment. Data mining can be applied to a very wide range of domains so it is quite hard to propose areas of research. I think the best would be to buy a data mining book with examples of applications and see what you like best. Hope this helps.

  10. hey Sandro Saitta,well while searching about data mining i gone through ur 5 pts.i impressed by these.i want to get some white papers regarding these.can u help me from where should i start for reserch.

  11. I say #2 is just it. i am creating a clearcut path for myself, trying to get the real deal with data mining and I have done research in 2 distinct areas so far, but I must say seeking out the domain knowledge can be challenging, but can soon make you an “almost expert” in something you never actually thought of in the first place. I’m so loving data mining for that.

  12. Ha! Great list.
    You’ll like my “Top 10 Data Mining Mistakes”. It’s got real-world ways we analysts overdo or over-interpret data. (“Seeing things in clouds”, like your #5 is there, too.)
    It’s chapter 20 in Nisbet, Elder, Miner: “Handbook of Statistical Analysis & Data Mining Applications”. (Or, ask SAS, one of the vendors, along with SPSS and STATISTICA, that includes free, limited-time mining software, as they are printing that chapter and giving it away free.)
    -John Elder

  13. @John: Thanks a lot for your comment. I have already started to read your data mining book, which is excellent. I will soon post a review about it on Data Mining Research.

  14. Excellent post. I want to become data miner. What do you recommend if in my region there are not courses about data mining? Dou you know about a roadmap to study? I have a computer science degree with some certifications in warehousing.

  15. yes its realy intresting wht u wrote abut datamining.m continueing my mtech so i wana to a thesis in datamining..could u guide me the posible way to do so.
    thank you

  16. Nice piece of reading πŸ˜‰
    Right now I’m not sure which career to pursue but I definitely considering data mining as an option. I have to choose between a MS in Data Mining at a mediocre university and MS or PhD in Statistics at a relatively solid one – how do you think which is better for job opportunities in the future? Thanks in advance.

  17. @Andy: thanks for your comment. I would suggest the better university. If you do a master in stats, the field of data mining is open to you. If you choose a phd in stats, then you can maybe focus on data mining research. This way, you will be ready for data mining in industry. What is sure is that with a background in stats, data mining is not an issue. Hope this helps.

  18. Hi,

    You’re right. I could never think that any other job would be as interesting as my job as Data Miner!!!

    Hooray for us!

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