tinymce loader

Today, in the era of Big Data, many companies are in search of specialists who would be able to get deep insights into data and effectively support decision-making processes. This means that multiple opportunities are open for those who wish to occupy industrial positions related to big data analysis.

An interview is usually a stressful thing for the majority of people no matter which industry they’re in. Being challenged on your knowledge and skills definitely requires some self-confidence, and the preparation for an interview can often help you improve this aspect of your personality. The variety of questions related to data science is endless, however there are certain domains, which you need to be familiar with in order to nail interviews and get the position of your dream.

We've collected a list of most popular concepts in data science for you to let you better understand your current professional level and get practical knowledge of those that seem unfamiliar to you.

Basic programming languages: you should have solid knowledge of a statistical programming language like R or Python (along with Numpy and Pandas Libraries), and a database query language like SQL.

Statistics: you should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators, and confidence intervals. Statistics is very important for extracting most important numerical dependencies out of huge datasets. Complicated interrelations within your datasets that you discover by applying your statistical knowledge are often of a critical importance for decision-making support.

Machine Learning: to be successful in your data scientist role, you should be able to explain such methods and algorithms for data analysis as K-nearest neighbors, random forests, and ensemble methods. These techniques are usually implemented in R or Python. Understanding these ideas will be a good indicator for potential employers. You will be able to prove that you have a solid grasp of how data science can be used in practical settings.

Data Wrangling: you should be able to prepare data before taking it to the next level. This basically means transforming data with the help of artificial intelligence techniques, identifying impure, noisy data and correcting or getting rid of irrelevant data points.

Data Visualization: data scientists need to communicate their findings to Product Managers in order to make sure that the discovered hidden data patterns can be used in real industrial applications. Thus, familiarity with data visualization tools is very important as you can show data in various forms and dimensions, not just talk about it.

Software Engineering: you should know algorithms and data structures as they are often important for creating efficient algorithms for your data projects. You should know use cases, applications and techniques dealing with data structures such as queues, arrays, lists, stacks, trees, etc.

Product Management: this one is definitely arguable, but specialists who understand the product are the ones who know which metrics are most important. Tons of test and benchmark numbers are available for analysts, designers, developers, and product managers, so product-oriented data scientist should be able to pick the right metrics to experiment with. There are some terms you should be familiar with: Usability Testing, Wireframing, Retention and Conversion Rates, Traffic Analysis, Customer Feedback, Internal Logs, and A/B Testing.

We have discussed just a few most important issues you should be prepared to in case you have been thinking of applying for a data-related position. Of course, there are many other questions that you can encounter in the process of job interviews. So, read and learn as much as you can, and then let your enhanced knowledge base do the talking for you. Good luck!