Is making a Good Resume for Data Science Enough?
4 things you must do before any interview.
There is a lot of hustle for making a good resume or CV for any post. And this hustle is justified as your resume is your first impression on the interviewer. It should be neat, clean, readable, and should contain all things you did professionally.
While I was researching how to make a good resume, I found plenty of resources available for making a good one. If you want good resume templates you may log in to overleaf. If you require action verbs for your resume, you may visit here (document from Harvard).
But the question is, is a good resume enough?
Let's see some statistics.
- On average, 10% of job applications result in interview invites. Out of those who land the interview, 20% are offered the job. (Zety)
- Of the 31% who were caught lying on their resumes, 65% were either not hired or fired. (HR dive)
- According to Monster's 2019 State of the Recruiter survey, 85% of recruiters said that candidates exaggerate skills and competencies on their resumes. (Monster)
One reason why recruiters believe that people exaggerate on their resumes is that the candidates are not well-prepared. Imagine you have done some courses or projects or internships for which you can't explain minute details! It undeviatingly indicates that you are not as skilled as you mentioned in your resume. (A big reason to worry.)
It's evident that even if you prepare a good-looking resume, your chances of getting selected are low. You should also know how to explain your resume in an interview. And, this is what this article is all about. So let's begin.
Tip 1: The Art of Storytelling
For any project or internship which you mentioned in your resume, create a story in your mind, from where it started to where it ended. Now, most people get confused when we use story and project together.
Let's see an example.
Above is the workflow for a predictive problem on Big Market Sales Prediction. It starts from "Knowing your Data" followed by Data preprocessing, Model Training, Evaluation Matrix, and vital concepts that I learned.
While you make a workflow, you should keep the following questions in your mind:
- What do you know about the data?
- Which variable types are present? Which data transformation is needed?
- How did I start solving my problem?
- Is Feature Engineering needed? Do I need more data?
- What are the hurdles I faced, and how did I clear them out?
- Unbalanced classification? Violation of Assumptions? Treatment Missing Values or Outlier?
- Why did I choose this algorithm? How did I evaluate that?
- What results did I get? Can I explain my results?
All these questions are crucial for an interview. The recruiter will surely ask these questions to analyze your problem-solving proficiency and your approach to any problem.
Now, let's see the benefits of this storytelling approach:
- Once you make a workflow in your mind, you are confident, and you can explain better what you did in the project. You would have known answers to most of the questions asked by the recruiter.
- The workflow will let you be more organized in your interview. Also, the recruiter will know how serious you are about this role.
- This powerful tool also lets you know which questions can be asked. Once you start describing your project chronologically, you can predict what questions can come at a particular point of time.
- Data Science is all about deriving stories from data. So, this practice will also help you after the interviews, in your professional career.
Similarly, you can use this approach for explaining your internships or professional experience. Start from where you have spotted a problem. What brainstorming you did to find feasible solutions? And how did you find the optimal solution? Then describe how you implemented it and, at last, the results.
Tip 2: Revision
After projects and internships, generally, recruiters ask some technical questions on topics mentioned in your relevant coursework. Relevant coursework may include courses pursued in your university, online learning platforms, or any other source.
If you go through all the topics, it will be difficult for you to spend a lot of time again and again. Hence, when you revise your course for the first time, try to make some short notes.
I have revised algorithms and taught in some online courses. I have tried to make short notes while I was preparing for my interview. It helped me a lot. Once I made these notes and gone through them a couple of times, concepts got cemented in my mind.
In the above picture, I have mentioned all things for the Decision Tree Algorithm. It contains all basic definitions which were vital for the interview.
Once you have done this, search for general questions asked on this topic/course in the interview. You will find plenty of resources. Preparing all this stuff will surely increase your grasp of the topic.
Tip 3: The Timetable - Very Important
My resume contained my courses, professional experiences, projects, extracurriculars, and much more. To prepare a resume, you need some dedicated time.
Making a timetable is not an easy task. You have a couple of weeks to review all topics that you have learned in past years. You may follow the below steps to make a timetable.
- First of all, list all the topics that you want to review.
- Try to sort them according to your strength on each topic. The topics in which you are weak must be at the top. When the interview date comes closer, time flows. Hence, sorting will help you to deal with the fragile topics when you have plenty of time. Even if you could leave one or two string topics, that's ok.
- Choose a fixed time or fixed hours in which you would be revising all the stuff.
- Last yet the more difficult step is to start. So, START revising.
If you plan, you could achieve more in given time.
I started these things 20 days before my interviews along with my regular classes. It was an intensive process. But the best part was, I could create a link between projects and relevant courses. I could generate new ideas using different techniques that I grasped in my later coursework.
Tip 4: Things you mentioned in your Resume but never did
As the statistics show, there are a lot of people who lie in their resumes. I would highly recommend you not to do so in an interview since the recruiter knows everything. Please highlight only those things on which you have worked.
But up to some extent, you may add some things which you have not done. For example, while solving a predictive modeling problem, you may run the xgboost algorithm and mention the results in your resume without having in-depth knowledge of the algorithm.
The recruiter understands that you do not know everything. You can't! What they want to see is that what you have done, you have done it with utmost sincerity. Today, firms need people who are sincere and hard working. Technological aspects can be taught, but not the trait of sincerity.
Job placement is a very intensive process. You have to showcase all you have done for years in a slot of 45 minutes. That 45 minutes could change your life. What I learned from this process is:
- Making a good resume is not enough. You should know every single term in your resume. This enables us to present our whole journey in a limited period of time and with greater impact. This also helps us to prove every aspect of our resume to the recruiter with utmost sincerity.
- With proper planning, you can achieve anything. ANYTHING!
Once you go through this process, I am sure you will feel more prepared and confident about your selection. An interview is not only the game of knowledge but also temperament.
I hope you have learned something or atleast try implement something in your routine. I bet you, these practices will surely give you results.