10 Concepts Every Data Scientist Should Know

 2 years ago
source link: https://towardsdatascience.com/10-concepts-every-data-scientist-should-know-70beddcd4ac2?gi=cc9695a093bd
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The concepts that are likely to be encountered at an interview.


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Data science is such a broad field. If it was a recipe, the main ingredients would be linear algebra, statistics, software, analytical skills, storytelling and all seasoned with some domain knowledge. The amount of ingredients change according to the tasks you are working on.

Whatever you do as a data scientist, there are some terms and concepts you should definitely be familiar with. In this post, I will cover 10 of these concepts. Please note that this post is by no means aimed to be a comprehensive list of the topics you need to know. However, knowing the following concepts will absolutely add value to your skillset and help you in your journey to learn more.

Let’s start.

1. Central Limit Theorem

We first need to introduce the normal (gaussian) distribution for central limit theorem to make sense. Normal distribution is a probability distribution that looks like a bell:


X-axis represents the values and y-axis represents the probabilities of observing these values. Normal distribution is used to represent random variables with unknown distributions. Thus, it is widely used in many fields including natural and social sciences. The reason to justify why it can used to represent random variables with unknown distributions is the central limit theorem (CLT) .

According to the CLT , as we take more samples from a distribution, the sample averages will tend towards a normal distribution regardless of the population distribution.

Consider a case that we need to learn the distribution of the heights of all 20-year-old people in a country. It is almost impossible and, of course not practical, to collect this data. So, we take samples of 20-year-old people across the country and calculate the average height of the people in samples. According to the CLT, as we take more samples from the population, sampling distribution will get close to a normal distribution.

Why is it so important to have a normal distribution? Normal distribution is described in terms of mean and standard deviation which can easily be calculated. And, if we know the mean and standard deviation of a normal distribution, we can compute pretty much everything about it.


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