Data science is a huge field and the subject itself is a mixture of many subjects. Data science job roles are very sought after as the salary of a data scientist is very lucrative.
Data scientist is called the sexiest job of the 21st century. It has a great potential for growth. Data science is trending right now and the demand for data scientists far exceeds the availability today. So now is the right time to prepare for a data scientist career.
Being a data scientist and learning all the required skills is a long and arduous process. An easier way to do it if you don’t have a relevant master’s degree is to do a data science online course.
You should know the differences between data science and machine learning as well as the different concepts Artificial intelligence and data science. These are the additional skills that will give you an edge over your competitors.
Once you have the education part nailed down, get a strong resume ready. Work on live projects and get some experience and you can use this for your portfolio.
In the cover letter, explain, in brief, how you solved a particular business issue and what methodologies you used and how it is expected to solve the issue. If you have seen the results, then present how effective the solution was. You can explain in detail in the interview. All these are value addition to your resume that will land you that interview.
There is a lot of scope for data science in india and abroad. They are also among those who get top salary packages. Overall, it is a very lucrative field. Though it requires quite a bit of expertise, the learning curve is not that steep. Also, the data scientist is a job requirement in every industry that is gearing up for making smarter and data-driven decisions.
There are many job profiles for data science and data scientists is one of the most popular roles in this field. But, as such there is no standard definition for the job of a data scientist. Every industry has its own use of data scientist and every company floats their own definition and the work expected of a data scientist.
In general, a data scientist works on problems of a company that does not have a very clear cut solution. They work on difficult problems and usually the results they produce that may seem very small will have a larger impact across the performance of the organization. One template is not enough but we’ll try to get more than a few skills that are a common requirement for any data scientist job.
Some data scientists love Python and others prefer other languages for data science projects. This means you may have the necessity to know more than one programming language to get a job successfully as a data scientist.
Responsibilities of a data scientist.
To reach the interview stage, you need a strong resume and strong subject knowledge. There are strict process for hiring data scientists. To be a data scientist, you need to display domain knowledge in addition to your data analysis and problem solving skills. along with technical interview preparation, you also need to prepare for the HR interview. Know what skills you need to be a data scientist.
In this section, we’ll see some questions in the technical interview that are general and are aimed at assessing your overall domain knowledge and skills.
General Interview questions
General questions touch upon your experience in detail, your leadership, critical thinking and problem solving abilities.
Answer each and every question with clarity. Don’t muddle through and don’t project negativity. At the same time, don’t try to portray yourself as perfect. Mistakes happen with everybody. That doesn’t mean you should highlight issues. Just mention them and also explain how you overcame the problem and how you applied the solution successfully.
What programming languages do you use?
Explain what languages you know and how you can apply them in data science. There are many programming languages that are used in data science. Describe some pros and cons of each language as pertaining to data science. This is to show that you know that there are issues that come with using each language, but that you also know how to handle it.
What’s your favourite programming language?
Describe an instance where you have used a language and how you completed the project. Explain the problems faced and how you worked around it to finish the project.
Explain a business problem you solved for your previous employer?
Give a brief about the problem and how you went about solving it. Do not use any company specifics and avoid sharing confidential data by accident. Tell the interviewer what tools you used and how you completed the task.
What do you think is the most important quality for a data scientist?
There are many important qualities for being a data scientist. Talk about the quality that sets you apart from other and you feel is a must have for every data scientist.
How do you stay updated and know of new developments and trends?
Answer honestly. You can’t be updated all the time. There is always something new on the tech space. Keep your answer short and technology minimum. You should not throw about tool names that you just came across. Remember that you may be asked to answer a few follow up questions on the new tool or technology.
Do you work with any new technology?
This question is the extension of the previous one. If you did use any new tools, expound about it. Talk about features that was new, why you liked it or didn’t like it.
Sample questions for technical interview
Why do you prefer Python?
Python has always been easy to use and is the preferred tool and language of choice for many data scientists. While R is a better choice for heavy statistics projects, Python makes it easy to use.
It is easy to work with and has easier learning curve. Explain why you feel Python and data science tools are good for your project. If you have done projects previously with Python based tools, then explain how you made the project successful. Learn Python online course with Crampete.
Why do you like R for this project?
There are many reasons why you should choose R for your data science project. State your reasons, like how easy it is to use, has strong community support, and using packages for machine learning and other features and functionality in detail. If you have already used R tools, then describe how you successfully used it for your data science project.
Curse of dimensionality?
Having a lot of dimensions complicate everything. It means that there is lesser chances of finding common factors or patterns in the data. Explain how to reduce dimensions in the model you use.
Should you prefer to plot your data before you analyze it?
Yes. It is preferable to plot the available data before you analyze it to avoid errors. Data is never error free. You may find some weird and senseless data that skews the entire result. And, in the case of analysis where the result is dependent on the mean or the median, the results will be corrupt in case the bad data is used. So it is good to plot the data before analyzing it.
Why is it preferred to have or include fewer predictors against including many?
The fact is that you keep adding predictors to a model, it grows more and more complicated. Also, some of the predictors will not be relevant to the requirement and will skew the data. Correlation becomes harder as well and overall computational costs keep increasing with more complexity. So it makes sense to keep predictors to the required minimum.
How to suggest where a franchisee should open a new store?
Use demographics to collect all relevant data to analyse. Get KPIs that comprise of the most desirable factors. Use machine learning algorithm to assess location. This is not a step by step. Explain each step in details as to how you arrive to this conclusion and what tool you use
Domains that you should prepare for a data science oriented job role.
There are many topics which you should prepare for the interview as a data scientist. Here we have mentioned some of the domains where your expertise is tested.
The best way to prepare for the data scientist interview is to orient your preparation around the requirements that are listed in the job listing.