Skip to main content

## Posts

Showing posts from June, 2020

### Expert Advice on How Important is Math for Data Science

Data science is one of the fastest growing technologies in the world. There are a lot of jobs in data science. That's why the majority of students are enrolled in data science. Most students believe that data science is all about computer science, but that's not true. It's a combination of statistics, mathematics and computer science. Therefore, whenever students want to enroll in data science, they must have basic knowledge of mathematics, computer science and statistics. But they still don't know what math to learn for data science. Even some students have a question in their minds is how much math is for data science and how important mathematics is to data science. Apart from that, students even ask what mathematics is required for data science. Here in this blog, we'll talk about mathematics for data science. Similarly, statistics on data science and mathematics for data science are also critical. If you are talking about basic mathematics for data science, you

### Most Prominent Methods of How to Find Outliers in Statistics

Extreme values are data values that are very different from most specific data sets. These data values fall outside the general trend, which already lies in the data. Extreme values are too low or too high in a particular set of data that can create an error in your statistics. For example, if someone measures the length of a child's nose, their common value may lie if Pinocchio is included in a particular class of data values. There is a need to examine the data set given to study extreme values in statistics, and how to find extreme values in statistics that may cause some challenges. Although this may be easy to identify with the help of a radical chart in which some values differ from specific data values. So, how much variability does the value as an external value? We'll study a specific analysis that provides an external standard on what develops a data deviation. What are outliers in statistics ? The definition of extreme values in statistics can be considered as a sect

### Statistics for Economics: Its Benefits and Limitations

Economics is one of the crucial parts of our lives. It is heavily used by financial professionals. But the economy without statistics is no longer useful. Here in this blog, we'll share economic statistics with you. Different types of statistics are used in the economy. This blog will help you reveal those statistics for the economy. But before we start, let's take a look at the meaning of statistics for the economy. Statistics in Economics Statistics are used to deal with data selection. Use different numbers to represent the specific information that is used with data collection. Statistics in economics include a technique to deal with data collection, tabulation, classification, and presentation. Apart from that, it's also helpful to reduce and condense data. Statistics in economics are very useful in data analysis and data interpretation. Benefits of Statistics in Economics There are a lot of statistical benefits in the economy. We cannot think of the economy without st

### Major Types of Statistics Terms That You Should Know

Statistics are one of the critical topics for students. Almost every student studied statistics in academic life. It is, therefore, necessary for each student to be familiar with the terms of the statistics. It is enough that most students know the basic terminology of statistics. On the other hand, if students want to get a job in statistics or data science. They must then know the basic terms as well as the basic terms of statistics. Apart from that, they should be well versed in other statistical terms as well. Here in this blog, we will share with you all the terms of statistics that you may not be familiar with. Let's take a look at these terms: - Statistics Terms Mean The average is part of the descriptive statistics. Is the average data set given. You can calculate the average by adding all the data set values and then dividing the total values by the number of values in the data set. For example, if you have a data set for the age of students, i.e. 16, 18, 17, 20, 15 years.