There are terms in Data Science which are most discussed and asked in Interviews and from a job seeker point of view it becomes relatively important to master these terms. Mastering these important terms will not only help you to crack the interview at your most desirous company but will also give you the right direction to move on while developing a career in Data Science. The 10 most important terms in Data Science that every Data Scientist must know while making a career in Data Science are discussed in the present article. While there are many other important terms as well that are used in the field but I have discussed those terms which are a bit unknown to most of the learners. Topics like Supervised Learning, Data Analysis are the most common and have been excluded from here.
Validation is the most important part that you need to know in Data Science. Validating your Machine Learning model to know the stability and accuracy of the model is the key aspect to know whether your model is running the best on unknown Datasets or not. Without cross-validation, it would then become a wasteful task when your model will not run successfully on unknown datasets.
Out of the two versions of the same product choosing the one that best fits your customer demands and also that is most liked by customers is offered by A/B testing. Each of the Businesses today uses A/B Testing to sell the best version of their products to its customers.
Clustering in Data Science is a grouping of a similar set of objects at one place and other dissimilar sets of objects in other. It finds its importance in Data Science in the way that it provides the underlying patterns hidden in Data at Data Analysis.
To model, the relationship between a dependent variable and an independent variable Linear Regression is used in Data science. The unknown variable is predicted with the help of the known variable. Linear Regression is the topic that is asked in the majority of the interviews and is the most important from the interviewer point of view.
Exploratory Data Analysis
Exploratory Data Analysis becomes yet another concept as analyzing datasets and finding out the underlying patterns hidden in the Data is the most important work part of a Data Scientist. A Data Scientist must know about EDA and as well as the techniques achieving EDA.
Used in Data Warehousing, ETL (Extract, Transform and Load) is the procedure of Extracting Data from the source, transforming it (i.e., Data Cleansing) and finally saving it or loading to a central Database for further analysis use.
Algorithms find its usage in solving complex problems. The set of instruction is described in an algorithm to formulate a problem and to reach the desired result after going through various steps. Algorithms is yet another topic most used in Data Science and Coding.