There is a lot of fuss about writing a good resume or resume. And this hustle and bustle is justified because your resume is your first impression of the interviewer. It should be neat, clean, legible and include everything you do professionally.
As I researched how to make a good resume, I found plenty of resources to do good. If you want good sequels, you can log in on the reverse side. If you need action verbs to continue, you can visit here (Harvard document).
But the question is, it is good to continue?
- Average, 10% from job applications leads to interview invitations. Of those who interview, 20% offered work. (Zety)
- Of them 31% who remain lying on their CVs, 65% either not hired or fired. (Hrdive)
- According to Monster’s 2019 Recruiter State Survey 85% recruiters said candidates exaggerate their skills and competencies in their resumes. (Monster)
One reason recruiters believe people are exaggerating in their resumes is that the candidate is not well prepared. Imagine that you have completed courses, projects, or internships for which you are unable to explain the exact details. It invariably shows that you are not as skilled as you mentioned in your resume. (Great cause for concern.)
Obviously, even if you prepare a good-looking resume, your chances of getting elected are slim. You should also be able to explain your resume in an interview. And this is this article. So let’s get started.
Create in your mind a story about any project or internship you mentioned in your resume, where it started and where it ended. Most people get confused when we use a story and a project together.
Let’s look at an example.
Above is the workflow for the predictive problem in Big Market Sales Prediction. It starts with “Knowing Your Data,” followed by data preprocessing, model training, an evaluation matrix, and important concepts I’ve learned.
During the workflow, you should keep in mind the following questions:
- What do you know about the data?
What types of variables are present? What data conversion is required?
- How did I start solving my problem?
Is feature design needed? Do I need more information?
- What are the obstacles I face and how were they overcome?
Unbalanced rating? Violation of defaults? Missing values of treatment or exclusion?
- Why did I choose this algorithm? How do I rate it?
- What results did I get? Can I explain the results?
All of these questions are important to the interview. The recruiter will be sure to ask these questions in order to analyze your problem-solving skills and approach to all problems.
Let’s now look at the benefits of this storytelling approach:
- Once you’ve done the workflow in your mind, you’re confident and can better explain what you did in the project. You would have known the answers to most of the questions asked by the recruiter.
- The workflow allows you to be more organized in the interview. The recruiter also knows how serious you are about this role.
- With this powerful tool, you can also know what questions can be asked. As you begin to describe your project chronologically, you can predict what questions may arise at a particular stage.
- Data Science is about storytelling information. So this practice will also help you after the interviews, in your professional career.
Similarly, you can use this approach to explain your internship or work experience. Start where you found the problem. What brainstorms were needed to find workable solutions? And how did you find the optimal solution? Then describe how you implemented it, and finally the results.
After projects and internships, recruiters usually ask technical questions on topics that will be mentioned in your coursework. Relevant coursework may include courses completed at your university, e-learning platforms, or other sources.
If you go through all the topics, you will have a hard time spending a lot of time over and over again. Therefore, when reviewing the course for the first time, try to make some short notes.
I have checked the algorithms and taught in some online courses. I have tried to take short notes in preparation for the interview. It helped me a lot. After making these notes and going through them a couple of times, the concepts were cemented in my mind.
In the image above, I have mentioned all the things for the decision tree algorithm. It contains all the basic definitions relevant to the interview.
Once you have done this, look for questions on general topics / courses in the interview. You will find a lot of resources. Preparing all of these things will certainly increase your understanding of the subject.
My resume included my courses, professional experiences, projects, curricula, and more. You will need some time to prepare to continue.
Making a schedule is not an easy task. You will have a couple of weeks to look at all the topics you have learned in recent years. To schedule, follow these steps.
- First, list all the topics you want review.
- Try to sort them according to your strength for each topic. Topics where you are weak must be at the top. As Interview Day approaches, time flows. So sorting helps to deal with fragile topics when you have a lot of time. Even if you could leave one or two strings, that’s ok.
- Choose a fixed time or fixed hours to check everything.
- The last even more difficult step is to get started. So, start reviewing.
If you plan, you can achieve more in a certain amount of 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 that I could create a connection between the projects and the relevant courses. I could generate new ideas using different techniques that I grabbed later in my coursework.
As the statistics show, there are a lot of people who lie on their resumes. I recommend that you don’t do that in an interview because the recruiter knows everything. Highlight only the things you have worked on.
But even some scope, you can add some things you haven’t done. For example, when solving a predictive modeling problem, you can run the xgboost algorithm and mention the results in your resume without thoroughly knowing the algorithm.
The recruiter understands that you may not know everything. You can not! They want to see that what you have done, you have done it very sincerely. Today, companies need people who are sincere and hardworking. Technical aspects can be taught, but not sincerity.
Job placement is a very intensive process. You have to present everything you’ve done in years, within 45 minutes. That 45 minutes can change your life. What I learned from this process is:
- Making a good resume is not enough. You should know every term on your resume. This allows us to present our entire journey for a limited time and with greater impact. This also helps us to prove every part of our resume to the recruiter with the utmost sincerity.
- With proper planning, you can achieve anything. ANYTHING!
Once you’ve gone through this process, I’m sure you’ll feel more prepared and confident about your choice. The interview is not only an information game, but also a temperament