How to get a SIM card with data in Amsterdam


If you are visiting Amsterdam, it’s convenient to have data on your phone so you can use Google Maps and find your way around. Yes, there’s wifi in many places, but there’s also a lack of wifi in many places. I have split a lot of hair and lost a lot of Euros experimenting with the different services available to visitors and I’ve finally figured out the best way to do this right. This post assumes you have an unlocked phone and you already know why you need to get data on your phone.

The most common card that people go for is Lebara. They are the easiest to get started with. You can find Lebara folks at the airport, at the railway stations, on the street etc. They do a good job marketing, have the best prices, are easy to recharge and have the best customer service. Yet, Lebara didn’t work for me. Why? The internet speeds are very slow. And Skype/Facetime calls are blocked on them. Lebara resells bandwidth from KPN, but it caps it at a speed that’s just barely enough to use Google Maps and to load pages very slowly. It was always between 0.2MBPS – 0.9MBPS. Everything is very slow, things time out. If your internet needs aren’t heavy, then sure, go wit Lebara. If you need more, read on.

The only real alternative is to go with KPN directly. Now, KPN has a larger set of resident customers and they haven’t yet figured out how to make things really work well with transient/visitors. Amsterdam, outside of the very touristy central area, is like the rest of Europe when it comes to customer service. To an American, “appalling” or “severely lacking” are the nicest words to use for the customer service in Europe. I think it’s a cultural thing, the expectations are just very different. And yet, KPN does have the best speeds and the best network in Amsterdam. So here’s how to go about it:

  1. Find a KPN store – they are a few in the centre and also in most neighborhoods
  2. Get a new SIM and ask them you also want to get a dataplan with it. They will inform you that the max package is for 1GB for a month. That’s the one I usually get. But now, pay special attention to the next part.
  3. Most KPN staffers are actually clueless about what actually happens when you activate a new SIM and try to get data on it.
  4. $10 is the cost of a new SIM. $16 is the cost of the 1GB data plan. So you basically pay $30 to get a new card and data.
  5. Before you put the SIM card in, make sure you go into Settings>Cellular and turn off Cellular Data. This is super super important.
  6. Now put the SIM in and see that it works, finds the KPN network and you may get a few welcome text messages. You can now use the phone to make phone calls etc. But keep your cellular data turned off until you activate the internet bundle
  7. You have to dial *147# to enable the data bundle.
  8. You will get a message back in Dutch confirming that your request was received. This is a very misleading looking message. If you have ever done this before, you might think it’s already active at this point. But it is not.
  9. No one will tell you this, but it actually takes more than 24hrs for the data bundle to be active. You have to keep cellular data turned off until that happens.
  10. Wait until you get one more message from KPN about the 1GB data plan.
  11. If you don’t get a message, try again after 24hrs with the *147# call.
  12. While you wait, install the MyKPN app from the app store. Create an account, sign-in, and associate your new SIM card and phone number with that account.
  13. I have found this to be the only reliable way to know what’s going on with your account. Here, you will see your balance, and if the data plan is truly active or not.
  14. Only once you have verified that the data plan here is active, you can now go ahead and turn on cellular data. If the settings are missing, use these below
    1. APN:
    2. Username: <blank>
    3. Password: <blank>
  15. Now you are set

It takes longer to get a new KPN connection, but if you are planning to be in Amsterdam for more than 3-4 days, and do need a faster connection, I recommend this approach.

Understanding Variances based on Sample Sizes


Every now and then you read something that really furthers your understanding of the world around us. I read this fascinating piece in the book by Howard Wainer: Picturing the Uncertain World. The specific chapter I read was called “The Most Dangerous Equation” where he discusses De Moivre’s equation. It’s quite a bite to chew on and I tried explaining it to my team using just words and that just didn’t cut it. So I put together a quick graphic visualizing some of the basis of it. This may not be academically super accurate, but gets the gist across, so bear with me and I welcome you to follow along 🙂

Below are 32 hypothetical students’ heights, each represented by one vertical bar. They are grouped by color into individual classrooms A, B, C, D … H making it 8 classrooms in all.

In the first row at the top, the solid green horizontal line shows the average of the heights of all the individual students across all 32 individual measurements. The rightmost section shows the average height and also shows the maximum height and the minimum height for this sample of all students.

In the second part, we first calculate the average height of each classroom separately e.g. instead of looking at each yellow bar separately, we are now only looking at the single green line across those yellow bars that represents the average height of that classroom. And we do that for each cluster of colors. So now we only have 8 measurements that reflect the average height of each classroom. Taking an average of those 8 averages results in the exact same average height. However, the variance in this sample is much lower i.e. it’s more likely that the tallest kid in a class gets balanced out by other short kids in a class so the average height of a classroom will show less variation than the average height of the kids individually.

Also, a large classroom is always closer to the mean than the average height of smaller classrooms which will have more outliers as it’s easy for a single tall student to throw off the average of a small classroom. But in a large class room, a single tall student has less impact on the average height.

The third section shows that distribution. Classrooms with the tallest average height tends to be smaller classrooms. Similarly, classrooms with the shortest average height also tend to be the smaller classrooms.

It would be erronous to just look at the top of the distribution and conclude that smaller classrooms have taller students compared to large classrooms. However, now replace height with grades. And that’s exactly the premise of the “small schools” movement. Without understanding the underlying real world distribution of data and how sample sizes affect variance, small school lobbying centers around the belief that small schools have better grades. This is true. But due to statistics and how data is distributed and measured. Not because small schools actually do something different. Also, the worst performing schools are also small schools by the same distribution.

Understanding this relationship between sample sizes and variances observed in them is very important when making sense of data. Yet, the chapter states, many examples of large policy decisions have been made by incorrect understanding of the datasets or by looking at just one side of the distribution.

iPhone vs Foursquare: comparing what they know about me


One of the biggest technology news this week has been the announcement made by Alasdair Allan and Pete Warden, researchers at O’Reilly, that theiPhone keeps a log of every location you have been to over the past one year and more. One could argue that it isn’t really news but it definitely is a rude surprise to most people. More so because the researchers also made a tool which makes it super easy for anyone to easily parse the contents of the file their own iPhone has been keeping on them.

Though I agree that saving an indefinite history of sensitive location data without explicit user notification is a terrible oversight at the least, I was also tempted to see what my own data held. So I went ahead and here’s what it looks like.

My iPhone faithfully recorded my road trip halfway across the country, my SXSW visit to Austin, Bay Area and LA trips and also my trip to Michigan and Ohio. I think it makes a very interesting sharing object at this level of zoom. Especially because I have been voluntarily giving that data to Foursquare anyway. Foursquare is a lot sparser than the iPhone data but it has more explicit knowledge of the exact business/venue I went to as opposed to the iPhone data that can only be used to make a reasonable guess. However, overall the data that the iPhone has been accumulating is obviously more exhaustive.

I am curious to run more detailed analysis on my own data, and possibly compare it with other people I know and other data sources I have to see what interesting stuff I can find. For example, it would be cool to see how much time my wife and I spend with each other and how it correlates to how many steps I took that day, what I ate, or what music I listened to.

Are we really as unique and different as we like to believe or are we just predictable dots on the map? At a higher aggregate level, data from cellphone carriers has already been used to find that we actually are quite predictable!