Interview Questions

Hello All, I am new one in this forum and I want to know the data science interview questions as a fresher level. If there is any data scientist person so please suggest me interview questions.

Hi Ankit,

According to me, there is a lot of data scientist but if you are talking about the perfect one then I can't say anything. Because I never heard anyone is perfect in any stream.

As a fresher first you need to be aware of these things, If you are just passed our from the engineering then first be familiar with these skills, If you already working somewhere as a developer or in IT field or in any which are related to the data science filed then start your journey:

1. Education: Most of the data scientist are highly educated. 88% of Data scientist who has a master's degree and 46% who has PhDs. There is a very rare case where some who is data scientist without having this background.

2. R programming: A Person who is very new or has not any awareness about the programming languages then it is too tougher for them to learn it because a person can not learn R programming without having a basic background of programming. As a survey, it is proved that 43% of the data scientist is using R languages to solve the analytical problems.

3. Python Programming According to the google trends it is the most trending language in the world, I typically seeing python is the required language for data science subjects along with the Java, Perl, C & C++. According to the O'Reilly surveys, 40% of the user is buying Python Books.

4. Hadoop PlatformsIt is not highly required but preferred in many cases. As a data scientist, you may encounter a situation where the volume of data you have exceeds the memory of your system or you need to send data to different servers, this is where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system.

5. SQL Coding: While a large number of query already in Hadoop and NoSQL but still it is expected that a candidate will be able to write and execute complex queries in SQL. SQL is known as Structure Query languages, that will help you carry out operations like add, delete and extract data from a database.

6. Apache Spark:Apache Spark is becoming the most popular big data technology worldwide. It is a big data computation framework just like Hadoop. The only difference is that Spark is faster than Hadoop. This is because Hadoop reads and writes to disk, which makes it slower, but Spark caches its computations in memory.

Apache Spark is specifically designed for data science to help run its complicated algorithm faster. It helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. It also helps data scientist to handle complex unstructured data sets. You can use it on one machine or cluster of machines.  

7. Machine Learning and AI: A large number of data scientists are not proficient in machine learning areas and techniques. This includes neural networks, reinforcement learning, adversarial learning, etc. If you want to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. These skills will help you to solve different data science problems that are based on predictions of major organizational outcomes.

8. Data Visualization:As a data scientist, you must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help you to convert complex results from your projects to a format that will be easy to comprehend. The thing is, a lot of people do not understand serial correlation or p values.  You need to show them visually what those terms represent in your results.

Data visualization gives organizations the opportunity to work with data directly. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of competitions.

9. Unstructured data:It is critical that a data scientist be able to work with unstructured data. Unstructured data are undefined content that does not fit into database tables. Examples include videos, blog posts, customer reviews, social media posts, video feeds, audio, etc.  They are heavy texts lumped together. Sorting these type of data is difficult because they are not streamlined.