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Six key points of data science interview

2020-11-08 07:19:00 【Artificial intelligence meets pioneer】

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author |KHYATI MAHENDRU compile |VK source |Analytics Vidhya

Introduce

You did it at last ！ You've got an interview for a data science position . Now? , The day before the interview , You don't know what to learn . The day is coming , But there's a lot more to do ！

Interviews can be daunting , Plus Data Science , You get a headache cocktail . Data science professionals need to combine their technical skills with their soft skills .

It's good to get an interview , But it doesn't mean success yet . That's what makes things so interesting . What should you learn before that ？ What should you do ？

If you're in a similar situation - You're in the right place ！

In this article , I'll focus on what to do the day before your big data science interview 6 thing , To make sure you absolutely seize the opportunity . I will not cover the whole preparation process , Because it's best to start a few months before the interview .

1. Read your data science resume carefully

The basics of any interview , Especially data science interviews . You should be able to explain everything on your resume . Anything you can refer to , You should be able to say .

for example , If you list one NLP project , But it can't explain the details , It's a big red flag for the interviewer .

Edit and revise your resume the day before the interview . Delete unnecessary details , And add new details when needed . Think about whether each project you list is relevant to your position ？

This means that your experience as a non-technical person in a marketing company may not be relevant to a data science position . You should consider deleting these details from your resume . Mentioning it will only make the interviewer feel like you don't know what you want from the job .

in addition , Think about how you will explain your work experience . You should describe your skills and how they lead to progress . Consider the following statements ：

“ Use LSTM To predict the company's share price .”
“ Use LSTM The accuracy of forecasting a company's stock price is higher than the historical average 40%.”

Doesn't the second one sound more impressive than the first one ？

Make sure your achievements are measurable and quantifiable . This will give your data science interviewer a better impression .

I suggest reading our guide , To build an effective data science resume . It refers to 4 Key aspects will determine the success or failure of your data science application ：https://www.analyticsvidhya.com/blog/2019/07/how-to-build-effective-data-science-resume-4-key-aspects/?utm_source=blog&utm_medium=6-essential-tips-should-know-day-before-data-science-interview

2. Research your data science project

Like any other detail on your resume , It's also crucial to decide what projects to talk about in an interview . If there are any projects that have nothing to do with the position you are applying for , So it's not very good to add it in . This will only let your interviewer know that you can't prioritize well .

choice 3 To 4 A project , Show your best job , And be ready to talk about them . These projects can come from your current organization , Internship , Some course assignments , Even independent projects , Data sets used from Analytics Vidhya or Kaggle. in addition , please remember , These projects should be relevant to your work profile .

I've been reiterating that , Because it's very important .

Let me give you an example . I listed a research project I did two years ago on my resume . In hindsight , I should have deleted it , Because it interviewed me for the internship position —— Data Analysis Intern —— It doesn't matter .

As I continue to explain what I've done on this project , I made a mistake , Referred to the “ Cubic spline function ” The word . I'm going to tell you more about cubic splines , I realized I had dug a hole in myself . therefore , I didn't get an internship .

If you're looking for a project , Please refer to our list of 24 The ultimate data science project , To improve your knowledge and skills ：https://www.analyticsvidhya.com/blog/2018/05/24-ultimate-data-science-projects-to-boost-your-knowledge-and-skills/?utm_source=blog&utm_medium=6-essential-tips-should-know-day-before-data-science-interview

3. Key skills for solving scientific problems

Analytical intelligence test is a very popular method to evaluate men's intelligence quotient . You need to have logic , Be creative , Good at solving puzzles with numbers .

Many organizations use puzzles to test candidates' ability to solve problems . They want to know about your thinking process and how you solve problems .

I can't give you a complete tutorial to solve every problem , But I have some suggestions ：

Take your time , Know all the details . If not explicitly mentioned , Please ask if there are any assumptions
It's all about showing your thinking process . So when you think about it , Make sure the interviewer understands your solution
Don't hold on too long . You can get tips from the interviewer , And adjust your approach accordingly
To realize , If you can't solve this problem completely , That's ok . Different problems have different difficulties , Not all problems are solved at once

Try to solve our list of 20 A difficult data science interview problem ：https://www.analyticsvidhya.com/blog/2016/07/20-challenging-job-interview-puzzles-which-every-analyst-solve-atleast/?utm_source=blog&utm_medium=6-essential-tips-should-know-day-before-data-science-interview

4. Prepare case studies facing the role of Data Science

Companies use case studies as a means of assessing how candidates deal with real problems . A case study is the closest thing you will encounter in your position in the future . This is the most difficult part of the data science interview that I've seen college freshmen struggle with .

The tricky part of case studies is , It may not be directly related to data science . for example , I have a story about how to predict Delhi NCR A case study of the number of black cars . It's a tricky question , But if you have a structured mode of thinking - You can take it easy

Because there is no fixed formula to solve these problems , It seems difficult to do case studies . But you can use the following to guide yourself ：

Ask a lot of questions . No matter what's going on in your head , You have to ask clearly ！ It will help you discover many of the details needed for a solution
solve the problem . This organizes all available data into a single table . Structure may reveal some patterns hidden in the data
practice ！ Try case studies in different areas , Retail sales 、 Health care 、 Business, etc . More practice , The easier it is to solve new problems
Remember that the most important thing is brainstorming and a great discussion . Our goal is not to find a fixed or predefined solution , It's about finding a path to it and showing your thinking process

Take a look at some case studies of analysis Vidhya（ Practice each one ）：

5. Overview of the research work and the company

There are obvious advantages in the overview of research work . You will be able to simplify your preparation according to the requirements of your role .

Sometimes , Employers may even ask a question or use keywords , To make sure they read the job description carefully ：

“ What technology do we use ？”
“ What do you expect from this position ？”
“ Can you tell us about the latest open source projects of the data science team ？”

If you don't understand the company and the role , These problems can be terrible .

I strongly recommend taking the time to read the mission of the company 、 Vision and core values . Learn about their major achievements . They try to find the scientific data they have . If possible , Find out the hierarchy of the organization and how the data science team fits into it .

Studying the organization and its structure will help you ask better questions for the interviewer . It shows your passion and curiosity about the company , Also left a deep impression on the interviewer .

6. Hard to understand terms in Data Science

Is there any term in data science that has confused you ？ I'm sure there are some —— Even experienced data scientists .

I encourage you to read some confusing terms or concepts the day before the interview ：

I Classes and II Kind of mistake
Accuracy and recall
False positive case rate and true negative case rate
Business indicators and statistical indicators
Model deployment

I often look for differences between these terms , I'm sure most of you will do the same . If you are asked these questions in an interview , You may be confused . You know the answer , But the subtle difference doesn't show up in you .

About these concepts , Please refer to our general machine learning and data science glossary ：https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/?utm_source=blog&utm_medium=6-essential-tips-should-know-day-before-data-science-interview

ending

This is just a final tip . The whole data science interview preparation is a long process . You need to start building your profile a few months in advance . There are also multiple rounds of recruitment in the data science recruitment process , Include ：