At DataCamp, we pride ourselves on having the best platform and the best curriculum for learning data science. To this end, we put a lot of effort into ensuring that every exercise is both effective at educating and enjoyable. After a course is launched, we don’t consider it to be complete: the launch is just the start of data collection. Whenever a student attempts an exercise in a course, we capture data points such as how many attempts it took them to solve the exercise, and whether or not they needed assistance by asking for the hint or solution. By aggregating this data over all our students, we can get a picture of how difficult an exercise is. Additionally, students can rate exercises from one star to five stars, and provide feedback to us, letting us know how well liked an exercise is.

**By Aisha Javed **.

Here’s an important fact that’s easy to forget: our data is only as helpful as it is understandable.

It is often seen that projects overshoot their normal completion data by at least three times most probably owing to shifting goals, inefficient approaches towards data collection and exploring various solution paths among others. A closer scrutiny often reveals that the delay was avoidable had there been a more disciplined decision making in place. To put it in a nutshell, there are three major principles which, if followed closely, can reduce the entire project completion time by a considerable extent without any need for sacrificing the end results. These three principles are:1. Fail Fast2. 40/70 Rule3. Fermi EstimationHowever, before the three principles are reviewed it is important to define the overall objective in its relevant context. The way you define a problem and an objective has a strong bearing on how you set the bar and the performance metrics that you use. It is of paramount importance that there is a close alignment with your problem, goals and data metric.

Deep Neural Map (DNM) We introduce a new unsupervised representation learning and visualization using deep convolutional networks and self organizing maps called Deep Neural Maps (DNM). DNM jointly learns an embedding of the input data and a mapping from the embedding space to a two-dimensional lattice. We compare visualizations of DNM with those of t-SNE and LLE on the MNIST and COIL-20 data sets. Our experiments show that the DNM can learn efficient representations of the input data, which reflects characteristics of each class. This is shown via back-projecting the neurons of the map on the data space. …

A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I’ve noted over the past month or so.

Taking Amtrak, going through Baltimore, reminded that Baltimore used to be the big city and Washington was the small town. My mother worked for the government in the 1970s-80s and had a friend, Pat Smith, who’d moved to DC during WW2 when they were filling up all the government agencies. Pat told my mom that, way back then, they’d go up to Baltimore to shop, because in Baltimore the stores had the newest dresses. I guess Woodies didn’t cut it.

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Spatial visualizations really come to life when you have a real map as a background. In R, ggmap is the package that you’ll want to use to get these maps. In what follows, we’ll demonstrate how to use ggmap with the Sacramento dataset in the caret package. For each city, we are going to keep track of the median price of houses in that city, the number of transactions in that city, as well as the mean latitude and longitude of the transactions.

By Susan Li, Sr. Data Scientist