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Abstractly, vectors are objects that can be added together (to form new vectors) and that can be multiplied by scalars (i.e., numbers), also to form new vectors. Concretely (for us), vectors are points in some finite-dimensional space. Although you might not think of your data as vectors, they are a good way to represent numeric data. For example, if you have the heights, weights, and ages of a large number of people, you can treat your data as three-dimensional vectors (height, weight, age). If you’re teaching a class with four exams, you can treat student grades as four-dimensional vectors (exam1, exam2, exam3, exam4). ## Related Posts #### What is data science in simple words?

In this article, I am going to give a brief introduction to Data Science. Data science is all about understanding the data and using that data to solve complex business problems. Its main goal is to find out the hidden pattern from the raw data. For achieving this goal data scientists use various tools, machine learning principles, and algorithms. This in turn allows organizations to manage costs, boost their market, and increase efficiency. At the end of this article, you will understand the following pointers.
27-dec-2020 /8 /84 #### What is meant by inferential statistics? What is the difference of descriptive and inferential statistics?

Inferential statistics use measurements from the sample of subjects in the experiment to compare the treatment groups and make generalizations about the larger population of subjects. There are many types of inferential statistics and each is appropriate for a specific research design and sample characteristics. Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population.
27-dec-2020 /8 /84 #### What is vector and scalar in data science?

Abstractly, vectors are objects that can be added together (to form new vectors) and that can be multiplied by scalars (i.e., numbers), also to form new vectors. Concretely (for us), vectors are points in some finite-dimensional space. Although you might not think of your data as vectors, they are a good way to represent numeric data. For example, if you have the heights, weights, and ages of a large number of people, you can treat your data as three-dimensional vectors (height, weight, age). If you’re teaching a class with four exams, you can treat student grades as four-dimensional vectors (exam1, exam2, exam3, exam4).
3-jan-2022 /8 /84
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