Table of Contents
Introduction
Data science is one of the most influential disciplines among all the sub-branches of computer science engineering. As such, it is also one of the most competitive and lucrative fields to pursue. There are currently very few institutions in the country that offer a full-fledged master’s degree in data science. However, a large number of institutions have come up to fill in the demand-supply mismatch. These institutions offer a lot of customized Python data science courses at various levels like beginners, intermediate and advanced. They are also available in the short term, medium-term, and long term durations ranging from 2 months to 4 months and 6 months respectively.
In this article, we walk across different questions of data science that are asked in different interviews.
What is the difference between data science and big data analytics?
Both data science and big data analytics deal with structured or unstructured data sets. However, the scope and domain of data science are much larger than big data analytics. Big data analytics comes under the domain of data science itself. This is because the life cycle of data science involves data collection, processing, analytics, and visualization. So, it is in the 3rd stage itself that the origin of big data analytics can be traced.
What are some of the commercial applications of data science?
Data science has tremendous significance in various commercial domains. For instance, in the domain of e-commerce, data analysis can be used for product recommendation, customer personalization as well as customer targeting. Similarly, in the domain of the stock market, data science can be used to predict the rise and fall of shares with a high degree of accuracy.
Examine the significance of data visualization.
Data visualization is one of the most important tools that is used to present complex findings of large quantities of data in a simplified manner. Area charts, bar charts, bubble clouds, histograms, pie charts, and heat maps are some of the techniques that are used in data visualization.
How is data science helpful to businesses?
Data science helps a business at many stages. At the inception stage, data science helps businesses in the design and development of AI-engineered products that are in demand in the present market cycle. At the mature stage, data science helps businesses by identifying prosperous sectors of expansion. At a later stage, data science enables a business to go for acquisitions and mergers by helping in predictive analytics.
What is the significance of sampling in data collection?
As the name suggests, sampling refers to a process in which a certain section of the population or a smaller data set is selected for further observation on the basis of various parameters. There are usually two types of sampling techniques. The first one is called probability sampling and the second one is called non-probability sampling. Some of the examples of probability sampling include snowball sampling and random sampling. On the other hand, quota sampling is a technique that falls under non-probability sampling.
What do you mean by overfitting?
Consider a machine that has been fed with a sample data set based on certain attributes and is trained to classify data based on assigned parameters. A large number of iterations are performed and the machine is finally able to classify data sets on the basis of assigned parameters. However, when new data sets are assigned, the machine shows a very low accuracy. It is unable to distinguish between data sets with the accuracy that it used to do for the test data set. This is called overfitting.
How is cross-validation used for improving the performance of a model?
As discussed in the above question, there is a high probability that the model may not perform well on new data sets due to the problem of overfitting. It is in this case that we go for cross-validation. Cross-validation is a technique in which a large data set is segregated into smaller data sets. These small data sets are supplied to the model in a periodical manner to ensure that the problem of overfitting is resolved. The aim of overfitting is to improve the performance of the model by making it perform well for new data sets. K fold cross-validation is one of the most popular techniques that is used for improving the performance of a model.
Author’s advice
The code to crack a data science interview is simple. The basics need to be understood and the application part needs to be mastered to ensure that you own the interview and perform with perfection.