You might have come across the news which relates data as a valuable asset of the 21st century which is indeed true. The rapid changes in the generations of computer and internet have eventually made human beings more dependent on technology for every aspect of life. Data is processed by using various tools and techniques to arrive at a decision. One such crucial tool for extracting meaningful information out of data is cumulative distribution function. Know more about cumulative distribution function and its importance below.

**Importance of Statistics for a Data Scientist**

Practical tools are used to enhance the profitability of business today. A data scientist certainly is more well-versed in statistics than a software engineer. There are different statistical data used by data scientists for getting an edge in business. Statistics in data sciences is used to increase the profits of a business by cutting down the cost in some way or the other.

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To find out some quick pointers that will help you understand the important concepts integrated with statistics, read on.

**Cumulative Distribution Function**

An integral concept of Probability Distribution Function (PDF) is the cumulative distribution function (CDF). A common aspect of PDF and CDF is that both of them are used to represent the random variables. Just like the basics of a probability density function, probability mass function and Bernoulli distribution data scientist needs the understanding of cumulative frequency distribution. A CDF is used to ascertain the probability of a random variable that is less than a certain value.

**Types Of Data To Understand CDF Better**

To ascertain the concept of cumulative frequency distribution better we need to know about the different types of data. There are apparently two types of data such as discrete variables and continuous variables. The discrete variables are those that have a set of finite variables. For example, you cannot have the 3,34567 medical procedures as it would be misleading. The number of medical procedures, in this case, can be either 3 or 4.

Whereas, a continuous variable cannot be listed like a discrete variable. But these have to be referred to by a formula as there can be an infinite number of continuous variables. An example to understand continuous variables better would be your age say you are 35 years old. You cannot be exactly 35 but 35 years, 210 days, 2 hours, 25 seconds and so on. Different probability distribution techniques are used for calculating with discrete and continuous variables.

**CDF Explained With an Example**

A cumulative probability is represented by a graph of the cumulative distribution function. Therefore, when we take cumulative distribution function example as a six-sided die, the cumulative distribution function for it will look like a staircase. Every step moving upward will have the value 1/6 plus the value of the previous probability. At the end of the graph in this case at the six-step, it will be 100%.

The cumulative distribution function is one of the basic tools of statistics essentially required by the data scientists to ace the job. To figure out the concepts running underneath the hood of data sciences concepts like cumulative distribution frequency are a must.

**Conclusion**

You may sometimes use only Python or Oracle programs to solve your issues but having an in-depth knowledge of statistics will give you and your team a better approach towards the solution. Learn more about CDF and move towards achieving the organisational goals better and faster. So, stop thinking and join the statistics courses on cumulative distribution frequency in Acadgild for easy manipulation and abstraction of data.