Probability for Data Science: Everything You Need to Know

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In today’s era, like any other profession, the Data scientist job has become the talk of the town. Its unstoppable popularity is attracting more young talents to enter the field of data science.

If you are considering making your career in the field of data science then you should be aware of the basics of probability. They are paramount to getting into this field. Probability enables data scientists to evaluate the possibility of outcomes of a specific experiment.

Did you know that according totheBureau of Labor Statistics (BLS), job opportunities for computer scientists are going to boom by 22% from 2020-2030?

As companies look for new solutions for collecting as well as analyzing great amounts of data, the need for talented and trained data science professionals will rise across various industries globally.

But, why the knowledge of probability is required to grow the data science field?

Let’s help you understand through this informative blog that helps you explore more about probability and what it is meant for data science.

Probability: Overview

Probability refers to a branch of mathematics that mainly focus on the study of random phenomena. It is essential for data scientists that involve using data affected by chance.

With randomness happening everywhere, professionals use probability theory to analyze chance events. The objective is to figure out the probability of an event happening, often utilizing a numerical scale that lies between “0” (indicates impossibility) and “1” (indicates certainty).

Probability plays the most essential role in several areas of scientific research. Certified data scientist can implement uncertainty into their essential research models as an approach to explaining their findings. This makes them do predictive distribution of findings related to what may have been witnessed earlier.

Probability for Data Science

Probability means the possibility of an event occurring. The likelihood of an uncertain numerical outcome can have multiple values, each one related to a probability. It lies between 1(will always occur) and 0(can never occur).

In reality, probability is utilized to do weather forecasts and even cricket scores. Also, it is helpful enough for people who want to know the reason behind statistical analysis in real-life applications.

Here are a few types of probability that play a vital role in data science career:

  • Conditional Probability

From predicting the weather to the stock market, the previous or existing outcomes have a great impact on future probability prediction.

Conditional probability can be presented as P(A|B) which indicates the probability of event A, given event B has already happened. During data scientist certification, you’ll learn about this in detail.

  • Probability Distribution

This can be defined as the probability of every one of the possible variable values. It’s a mathematical variable that actually relates the variable value with the possibility of an event happening.

An incessant probability distribution for supposed random variable X is known as a normal distribution curve.

As a data scientist professional, you’ll find various categories of probability distributions that will help you understand the basic concepts.

  • Random Variables

This refers to a set of probable values that come from any random study or experiment.

The random variables are of two types, namely:

  • Discrete Random Variable: It has a fixed set of possible values.
  • Continuous Random Variable: It has set values which are basically intervals.
  • Bayes’ Theorem

It’s a way to find out the probability of an event on the basis of past events. This theorem helps calculate the probability on the basis of the hypothesis. It can give information about how diagnostic tests are performed.

When we head to the doctor’s clinic to get tested, we want to be aware of being sick, provided the test is positive.

Likelihood or P(B|A):  Probability of “B” being True, specified “A” is True.

Prior or P(A): Probability of “A” being True.

Posterior or P(A|B): Probability of “A” being True, specified “B” is True.

Marginalization or P(B): The probability of “B” being True.

Formula to Calculate P Value

A hypothesis is referred to as an assumption made on few available data. In the top data science certification course, this portion is covered really well. A p-value is mainly used to give validation to a hypothesis against experimental data.

  • The null hypothesis shows there is no important relationship between the two variables.
  • The alternative Hypothesis shows there is an important relationship between the two variables.
  • The P-value can be defined as the probability of getting the observed outcome, pretending that the null hypothesis is true.

When it comes to a hypothesis of population, the P value is calculated and that value help to make decisions whether reject or accept the null hypothesis.

To receive highly accurate statistical tests, data scientists utilize 90% – 99% confidence level values.

Did you know: A 0.01 confidence level is extremely substantial than a 0.05 confidence level? 

After understanding various types of probability, now it’s time to understand how it can be used in data science.

How is Probability Used in Data Science?

Undoubtedly, the probability is among the most widely used statistical testing criterion for data analysis. In multiple situations, the ability to predict the possibility of something occurring is important.

Weather forecasting is one such distinctive application of possibility in predictive modeling, a discipline that has progressed since it initially emerged in the 19th century. Probability can be utilized by data-driven businesses such as Netflix or Spotify to predict the kind of movie or music you might like to see next.

In Conclusion

Presently, new data science professionals are required to have a clear understanding of the basic concepts of probability such as important concepts including statistical significance, probability distribution, regression, and hypothesis testing.

You can begin interpreting machine learning models with a probabilistic perception and understand them in the form of posterior/prior probabilities, Bayes rule, and posterior/prior probabilities to get complete clarity on your gathered data.

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