In this article, we are going to learn a bit more about a popular method of creating and visualizing predictive models and algorithms – decision trees. We are going to learn what are decision trees, what are the types of decision trees and when you should use each. Finally, at the end of the article, we will take a look at the advantages as well as disadvantages of using decision trees.

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This article will take you into the world of predictive analysis. We will learn why is important and what are its benefits. We will take a look at a few examples of businesses that use it, and most importantly we will explore the three common types of predictive analytical models used in predictive analytics – decision trees, regression, and neural networks. In addition to that, we will take a look at predictive analytics tools that are powered by even more models, such as classification models, clustering, forecast, outliers, and time-series models among many, as well as and 5 common predictive analytics algorithms that can be applied to a wide range of use cases.
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“Bi” means two and the term bivariate analysis refers to the understanding of the relationship between two variables. In comparison, univariate analysis is about analyzing one variable, while multivariate analysis refers to understanding relationships between more than two variables.

Bivariate analysis has a lot of use in real life because it can help understand the strength of the relationship between the two variables. But before bivariate analysis can define the strength of relationship, it first must define whether there is any casualty and association between two variables – whether the value of the dependent variable will change if the independent variable is modified.

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This article is going to scratch the surface of descriptive statistics – we are going to define it and see what purpose it serves. With a help of descriptive statistics, we are going to take a look at univariate analysis – an analysis of a single variable – and we will observe the three major characteristics when observing a single variable – the distribution, the central tendency, and the dispersion, and we are going to take a quick peek at bivariate and multivariate analysis, so you have a better understanding of what analyzing one, two or more variables mean.

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What is a variable and what do we do with a variable? This and many more are just some of the things that we are going to learn in this article. These are the very basics of data science, but they are super important before you take a leap into topics that are much more complex. So, let’s start.
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If you want to be good in data science, you cannot underestimate data analytics and statistics. Both hate guessing and they both love facts. If you are knowledgeable in data analytics and statistics, you will be able to think critically and most of all make data-driven decisions. In this article, we are going to review the basic statistical terms every data scientist should be familiar with.
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The first thing you probably did is google search “what skills do I need to become a data scientist” phrase. And what did you get? You were faced with a relatively long list of skills required to become a data scientist, ranging from technical to nontechnical skills – from statistics to programming in Python and R, up to storytelling skills and making presentations. But who has all these skills? The good news is probably nobody. The fact is that two data scientists do have a sharable foundation of knowledge, nevertheless, each of them has their own narrow specialty, which is sometimes so deep that they couldn’t switch their jobs. One data scientist’s job could be close to a job of a statistician’s, while another could be an expert in Python. But, if you want to start with data science, where do you begin? Do you really have to be an expert in Phyton? Should your knowledge of statistical analytics be so deep? The fact is that data scientists have a diverse skill set that is usually not found in a single individual.
In this article, we are going to take a look at nontechnical and technical skills that are required (or at least some of them) to become a data scientist.

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