An interesting fact about the data science world is that there are a ton of challenge take home tests available online. One of my favorites is the following. It is a collection of data science take home challenges that I created to help others improve their data science skills.
The take home tests is a series of tests that I personally and a group of other data science students have created which focuses on a specific aspect of data science (e.g. machine learning, statistics, and data analytics). Each take home test contains a brief description, and a series of questions that are designed to test your understanding of a specific data science concept.
The take home tests are designed to help students hone and test their understanding of specific data science concepts. Each test is designed to take about 30-60 minutes to complete. You can find the take home tests here.
The first take home test is to learn about the “Statistics of Probability and Uncertainty”. This is probably the most difficult test and requires students to use statistics and numerical methods to determine the probability and uncertainty of a certain outcome. Many students fail this test, which is why it’s a good idea to read up before taking it.
This particular test is designed to teach the students to think about the probability of a given event happening, and then how to calculate this probability. The second take home test is known as the Linear Regression test, which is essentially a version of the probability test. The third take home test is a word association test, and the fourth and last take home test is a vocabulary test.
The Linear Regression test is a little different than the other take home tests. It’s a test of how well the students can calculate the probability of the outcome of a given event. For example, if one of the students in their class is looking at a list of random numbers, and they see that a number appears three times, then the probability of that number appearing is three times greater than the probability of the same number occurring.
The Linear Regression test is essentially asking the students to use regression to predict the probability of the outcome of a given event. They should be able to do this using only their understanding of probabilities, and the formula, but they don’t have to know about the formula.
One of the problems in statistics is that it’s a black box. Even so, the teacher can have a hard time explaining the formula while keeping students on track. I have a little trick that I use in my data science class with a list of numbers. I give each student a sheet of random numbers on a whiteboard and then ask them to draw a line from one to the other.
The question is, what is the probability that a random number drawn from a list of 1s and 0s on a whiteboard will be a 1? The teacher can tell you it’s something like 12.5%, but if they don’t have a list of numbers they have to calculate it from their understanding of probabilities.