Data Science Career Tips: Can You Become a Data Scientist?

So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/38Kt3PN Data science is a super-hot topic and the data scientist is one of the most illustrious jobs of the 21st century. But how does one actually become a data scientist? You can ask around, read Quora answers, or talk to someone in the industry, sure, these methods will supply you with information, but there’s no doubt that this information will be biased towards someone else’s personal experience. How others became data scientists is of little importance to you, I bet. What you’re interested in is whether YOU can become one. Are your skills appropriate for this field? What steps do you need to take to become a successful data scientist? Will your background affect the chances of becoming a data scientist? All valid questions. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/2TZDriF If you want to become a data scientist, you should answer questions like one. A data scientist wouldn’t take the experience and background of just one or two other data scientists and accept them as a quintessential guide. So, how much data would be statistically adequate to give us an idea of what it takes to become a data scientist? 100 profiles? 500? How about 1,001? Well, that’s exactly what we did. You can find the whole study here: https://bit.ly/2NLGO7G Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #data #science #datascientist

Data Science & Statistics: Hypothesis testing. Null vs alternative

In this tutorial we'll introduce hypothesis testing. There are four steps in data-driven decision-making. First, you must formulate a hypothesis. Second, once you have formulated a hypothesis, you will have to find the right test for your hypothesis. Third, you execute the test. And fourth, you make a decision based on the result. Let’s start from the beginning. What is a hypothesis? Though there are many ways to define it, the most intuitive I’ve seen is: “A hypothesis is an idea that can be tested.” This is not the formal definition, but it explains the point very well. So, if I tell you that apples in New York are expensive, this is an idea, or a statement, but is not testable, until I have something to compare it with. For instance, if I define expensive as: any price higher than $1.75 dollars per pound, then it immediately becomes a hypothesis. Alright, what’s something that cannot be a hypothesis? An example may be: would the USA do better or worse under a Clinton administration, compared to a Trump administration? Statistically speaking, this is an idea, but there is no data to test it, therefore it cannot be a hypothesis of a statistical test. Actually, it is more likely to be a topic of another discipline. Conversely, in statistics, we may compare different US presidencies that have already been completed, such as the Obama administration and the Bush administration, as we have data on both. Generally, the researcher is trying to reject the null hypothesis. Think about the null hypothesis as the status quo and the alternative as the change or innovation that challenges that status quo. In our example, Paul was representing the status quo, which we were challenging. *Special Offer 20% Off*. Complete Data Science Online Training Program. Earn a data science degree at your own pace. Access your 20% off here: https://bit.ly/30YeGom So, you want to become a data scientist? Great! Our free step by step guide will walk you through how to start a career in data science: https://bit.ly/2TZF0gx Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2XIOUSS Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you! #hypotesis #testing #tutorial

Data Science & Statistics: Type I error vs Type II error

In general, we can have two types of errors - type I error and type II error. Sounds a bit boring, but this will be a fun lecture, I promise! First we will define the problems, and then we will see some interesting examples. Type I error is when you reject a true null hypothesis and is the more serious error. It is also called ‘a false positive’. The probability of making this error is alpha – the level of significance. Since you, the researcher, choose the alpha, the responsibility for making this error lies solely on you. Type II error is when you accept a false null hypothesis. The probability of making this error is denoted by beta. Beta depends mainly on sample size and population variance. So, if your topic is difficult to test due to hard sampling or has high variability, it is more likely to make this type of error. As you can imagine, if the data set is hard to test, it is not your fault, so Type II error is considered a smaller problem. Follow us on YouTube: https://www.youtube.com/c/365DataScience Connect with us on our social media platforms: Website: https://bit.ly/2TrLiXb Facebook: https://www.facebook.com/365datascience Instagram: https://www.instagram.com/365datascience Q&A Hub: https://365datascience.com/qa-hub/ LinkedIn: https://www.linkedin.com/company/365d... Prepare yourself for a career in data science with our comprehensive program: https://bit.ly/2HnysSC Get in touch about the training at: support@365datascience.com Comment, like, share, and subscribe! We will be happy to hear from you and will get back to you!