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Six key points of data science interview

2020-11-08 07:19:26 Artificial intelligence meets pioneer

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 :

  1. Telephone interview

  2. Task assignment

  3. Live interviews , Including technical 、 A case study 、 Problem solving and so on .

“Ace Data science interviews ” The course details all of these steps (https://courses.analyticsvidhya.com/courses/ace-data-science-interviews?utm_source=blog&utm_medium=6-essential-tips-should-know-day-before-data-science-interview). This course also has a wealth of interview questions , And a lot of useful techniques and techniques . This will greatly increase your chances of winning the next data science interview .

Link to the original text :https://www.analyticsvidhya.com/blog/2020/09/6-points-data-science-interview-preparation/

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