How to Prepare for Data Science Interview?

Appearing in data science interviews but struggling to crack the interview. Are you scaring to get into a data science interview? Or you don’t know what to expect in data science interview then don’t worry I have come up with the 6 steps that will definitely help you to crack data science interviews.

Cracking data science interviews need a massive amount of knowledge and research. So practicing only will help you to crack the interview on that big day.

Read on to understand a quick, step-by-step approach to specific areas of skills, technical know-how, and skills that are required not only to end the interview but also to excel in big data and machine learning provide.

The thing about data science is that its application, and therefore expectations vary widely across industries. The role is interpreted differently depending on the company, some could call a doctorate. Statistician as a data scientist, for others it means an excellent skill, while for some it can be a generalist for artificial intelligence and machine learning.

6 steps for Preparing a Data Science Interview

Here I am going to mention 6 steps that will help you to prepare and crack your data science interview. So brush up your skills and follow these steps.

Step 1:

Before appearing in data science interview first read the job roles or job profile especially for Skills, Techniques, and Tools. If the job description has not enough detail mentioned the research on the company website and check what type of data science position is available there and what kind of knowledge they are expecting from the candidate.

Mostly data science interview is a combination of the Aptitude, Technical Knowledge and Analytical Reasoning.

Step 2:

Don’t forget to brush up your knowledge of relevant skills before the interview. To test your technical skills, the interviewer will generally ask you about statistics, machine learning, and programming, etc.  Ensure to brush up on languages like Python, R and Tableau.  The interviewer generally asks the programming question from these languages and will check your knowledge on these languages.

Step3:

Brush up your skills on some primary important topics like:

•       Probability
•       Statistical Models.
•       Machine Learning and Neural Networks etc.

So here, you will essentially have your exam through a case study or a discussion of your problem-solving skills. If you are able to define the problem for them on the scenario presented and will help add the suggested solution and its impact on the business. In doing so, cite examples of case studies or research papers to support the suggested solution.

Step4:

Although you can develop the necessary skills and qualities, make sure throughout the interview that you are willing to learn and that you can adapt flexibly to the current organization such as data science and its applications are unique.

Step5:

Having a tight resume and predicting how you will relate your experience to the position given during the interview.

Step6:

If you are doing data science projects specifically, when you are fresher, there are many public areas available. In addition, it is advisable to attend MOOC - Massive Open Online courses to be exposed to various and targeted applications.

Keep in mind that lately the role of a data scientist is seen as someone who can bridge the gap between the different functions of a company. It is not intended or required that you are a specialist in all aspects, but you should be able to link functions, ideas, and solutions across domains. In order to stand out in an interview, you not only need to demonstrate your individual strength and expertise in this area, but also act as a person with sufficient management skills and good communication and technical skills who can fit in and participate in the heart of a problem.

Read More:  Top 20 Reactjs interview question and answer for fresher in 2020

Conclusion:

So here I have explained 6 steps to prepare your data science interview and also explained what skills you will need to crack the data science interview. I hope you have understood all 6 steps. If you think that I didn’t mention the important skills that are more important in the data science interview then you can comment in the below section.

Near Learn is the best data science with Python Training in Bangalore and provides training on various courses like Artificial Intelligence, Machine Learning, Deep Learning, Full-Stack Development, Mean-Stack development, Golang,  React Native and other technologies as well.

What is Data Science and why we need Data Science?

Before getting into the best training institute in Bangalore first I would like to tell you about data science that what is data science and why it is important.

What is Data Science?

 As the world has entered into the big data every company knows the importance of data. As this is the world of big data so its storage importance is also growing but after the Hadoop and other software came into the market, the storage problem was solved. Now the main concern is on the processing of data. As we have the solution of storage of data but how can we process the huge amount of data. So to solve this problem, data science came into the market.  

Data science term has grown because of big data, data interpretation and the development of scientific statistics. Data science is part of artificial intelligence.

Why we need Data Science?

Before the data was very structured, small in size and can be analyzed by using BI tools or other simple tools which are available in the market but nowadays the data is coming unstructured, big in size and coming from very different sources. Below the graph is showing that by 2020 more than 80% of data will be unstructured.

This unstructured data is generating from different sources like multimedia forms and sensors and instruments. Such kind of unstructured data cannot be analyzed by the BI tools and other tools these tools are not capable of analyzing such kind of data. For this, data science came into the market. For processing such kind of huge data and to analyze the data we need some powerful tools which can analyze, process and draw meaningful insights.

We cannot do only these things we can do another thing as well by using data science. That’s why data science has become much popular. Let’s take a look at what other things can be done by data science.

1.       How about if you could understand the customer’s need based on their existing data like past purchase history, browsing history age and income. No doubt it will give you a great business profit if these things can be done. This will be possible with data science and cannot be done by other analytics tools.

2.       How about if data science can be used in predictive analytics. Data science can give the prediction in weather forecasting. It can take and analyze the data from ships, aircraft, radar, and different satellites and give you the prediction of whether or also can tell you about some natural calamities. So you can aware before any natural calamities happen

3.       Let’s take another example of data science to suppose if your car had the intelligence to drive you home. These self-driving cars take the data from different sensors, cameras, radars, and lasers. Then this data is analyzed and based on this data your car takes the decision like when speed high when to speed up, when to take a turn and when to stop. This can be done by the latest machine algorithms.

Conclusion:

 I hope you have understood the role of data science and the need for data science in this modern world. Near Learn is the top Machine learning institute and it provides the best data science training in Bangalore. It also provides training on machine learning, blockchain, big data, and deep learning, etc.