Pros and Cons of Top Data Science Online Courses

There are a variety of data science courses online, but which one is the best? Find out the pros and cons of each!

Coursera, EdX, etc

These MOOCs have been around for several years now and continue to grow. But are they really the best option for learning online?

Pros:

  • Lots of Topics including R and Python
  • Affordable and even a free option
  • Well thought out curriculum from professors in great schools

Cons:

  • Not easily translatable to industry
  • Not taught by current industry professionals, but instead academics

Now, these MOOCs are still worth checking out and seeing if it works for you, but beware that you may feel tired of analyzing the iris data set.

PluralSight

Pros:

  • Lots of Topics in R, Python, and databases
  • Easy to skip around through the user interface instead of going in order
  • Taught by industry veterans in top companies that know current trends and expectations
  • You can use your own apps -Anaconda and RStudio – on your computer and not in the website itself

Cons:

  • Still just a bit limited on their data courses, but still growing quickly

DataCamp

Pros:

  • Great options for beginners to intermediate
  • Courses build on each other, fairly good examples
  • Most instructors have spent time in the industry

Cons:

  • You have to use their in website coding tool
  • Exercises are not always that clear
  • Never know if your app will work the same way on your own computer

So that’s a quick overview of options for learning online. Of course blogs are fantastic, too, and stack overflow can really be helpful!

Feel free to add your recommendations, too!

Check out PluralSight’s great offer today!

Shapiro-Wilk Test for Normality in R

I think the Shapiro-Wilk test is a great way to see if a variable is normally distributed. This is an important assumption in creating any sort of model and also evaluating models.

Let’s look at how to do this in R!

shapiro.test(data$CreditScore)

And here is the output:

Shapiro-Wilk normality test
data:  data$CreditScore
W = 0.96945, p-value = 0.2198

So how do we read this? It looks like the p-value is too high. But it is not. The threshold for the p-value is 0.05. So here we fail to reject the null hypothesis. We don’t have enough evidence to say the population is not normally distributed.

Let’s make a histogram to take a look using base R graphics:

hist(data$CreditScore, 
     main="Credit Score", 
     xlab="Credit Score", 
     border="light blue", 
     col="blue", 
     las=1, 
     breaks=5)

Our distribution likes nice here:

Great! I would feel comfortable making more assumptions and performing some tests.