In the 21st century, a great deal of jobs is projected to be redundant. This is due to the effect of machine learning and artificial intelligence being installed in the industries.
As the internet of Things’ devices generates data each and every day, this data needs to be collected, compiled and stored for future reference.
Being a great job of our times, many people are looking to fill positions of data scientists so as to get the opportunity to interact with valuable data and earn attractive perks.
However, before you get into these ‘powerful’ positions, you will want to have some basic skills that are relevant to the job.
Here are some of the must-know data science Interview questions that you would not want yourself to fail because of any reason.
1. What is Data Science?
As simple and trivial as it may sound, hiring companies will always ask prospective staffers this question.
Data Science is a multi-disciplinary field of various scientific techniques, processes, and knowledge. It is used to transform structured, semi-structured and unstructured data into meaningful patterns.
Some of the concepts in data science are statistics, regression, mathematics, computer science, algorithms, data structures, and information science. These also have subfields like data mining, machine learning, databases or cloud storages.
Data Science vs Machine learning concepts have evolved to a greater extent in the area of computing technology in order to perform data analysis on the existing data where the growth of data is in terms of an exponential with respect to time.
2. Why is data cleaning essential in Data Science?
Data cleaning is so important in Data Science as the final results of the data analysis come from the existing data where insignificant data needs requires periodic cleaning. This ensures the data reliability & accuracy is maintained and also memory is freed up.
This is especially quite important in government or e-commerce databases as it helps in flushing out old ‘useless’ data and replacing them with the current and updated information.
During the cleaning process, care has to be taken when deciding on how to manipulate this data.
This is taking into consideration the different data types data sources like dirty data, clean data, mixed clean and dirty data and sample clean data.
3. What is the best Programming Language to use in Data Science?
Now, the most used programming languages in data science are R Programming and Python. These open-source programming languages have been in existence since 1990 and data scientists continue to use them.
The beauty with the two programming languages is that whereas python is a great tool for data manipulations and iterations, R programming comes in handy in the querying, data retrieval, customized data presentations and analysis.
4. What is Linear Regression in Data Science?
Linear regression is a technique used in supervised machine learning to help in predictive analysis of data.
In linear progression, the available data is used to predict future trends by leveraging the present patterns.
5. What is A/B testing?
In A/B Testing, you compare different versions of a system to determine which of the two performs better.
In relation to data science, A/B testing allows the data scientist to pit two data variables against each other to determine which one meets the business needs.
Being the sexiest job of the century, data science is a fairly lucrative field. It is only in order that you also arm yourself with the answers to these 5 must-know data science questions.