Data Scientists are modern-day statisticians that take a shot on complex business problems and unravel them with the assistance of data. Probability Distributions allow a Data Scientist or Data Analyst to recognize patterns in any case totally random variables. A normal distribution is generally described as the bell-shaped curve and it depicts the recurrence of something that you are evaluating, such as the class scores. The focal point of the bend is the mean and the curve width called the standard deviation. The score happens most every now and again is the mean.

If you're in the beginning stages of your data science credential journey, you're either about to take (or have taken) a probability class. As part of that class, you're introduced to several different probability distributions, like the binomial distribution, geometric distribution and uniform distribution. You might be tempted to skip over some elementary topics and just scrape by with a bare pass. Because, let's face it--the way probability is taught (with dice rolls and cards) is far removed from the glamor of data science. When am I ever going to calculate the probability of five die rolls in a row in real life?

Data scientists have hundreds of probability distributions from which to choose. Data science, whatever it may be, remains a big deal. "A data scientist is better at statistics than any software engineer," you may overhear a pundit say, at your local tech get-togethers and hackathons. The applied mathematicians have their revenge, because statistics hasn't been this talked-about since the roaring 20s. They have their own legitimizing Venn diagram of which people don't make fun. Suddenly it's you, the engineer, left out of the chat about confidence intervals instead of tutting at the analysts who have never heard of the Apache Bikeshed project for distributed comment formatting.

Every once in a while, a data visualization trend organically sweeps through Reddit, the popular social news aggregation site, leading to some eye-catching and interesting user-generated results. We've seen users visualize how they spend their disposable income, how they spend their time, and even details of their relationships. Sometimes these charts are fairly personal and uninteresting to the outside world – but other times, they can be quite compelling to a wider audience. Most recently, in the Data is Beautiful subreddit, the trend has been to use data visualization techniques to split up the maps of countries into four evenly populated areas. There are hundreds of maps to be found using this technique, but here are just five examples that we thought were particularly interesting.