Data Science & Machine Learning

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Few common problems with lot of resumes: 1. 𝐈𝐫𝐫𝐞𝐥𝐞𝐯𝐚𝐧𝐭 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧. I understand that there are a lot of achievements that we are personally proud of (things like represented school/clg in XYZ competition or school head/class head etc), but not all of them are relevant to technical roles. As a fresher, try to focus more on technical achievements rather than managerial ones. 2. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬. Many resumes have the same common projects, such as: Creating just the front-end using HTML and CSS and redirecting all the work to an open-source API (e.g., weather prediction and recipe suggestion apps). Most common projects are: - Tic-tac-toe game. Sorting algorithms visualizers. To-do application. Movie listing. The codes for these projects are often copied and pasted from GitHub repositories. Projects are like a bounty. If you are prepared well and have quality projects in your resume, you can set the tempo of the interview. It is one of the few questions that you will almost certainly be asked in the interview. I don't understand why we can spend 2 years preparing for data structures and algorithms (DSA) and competitive programming (CP), but not even 2 weeks to create quality projects. Even if your resume passes the applicant tracking system (ATS) and recruiter's screening, weak projects can still lead to your rejection in interviews. And this is completely in your hands. I feel that this topic needs a lot more discussion about the type and quality of projects that one needs. Let me know if you want a dedicated post on this. 3. 𝐋𝐚𝐜𝐤 𝐨𝐟 𝐪𝐮𝐚𝐧𝐭𝐢𝐭𝐚𝐭𝐢𝐯𝐞 𝐝𝐚𝐭𝐚. For technical roles, adding quantitative data has a big impact. For example, instead of saying "I wrote unit tests for service X and reduced the latency of service Y by caching," you can say "I wrote unit tests and increased the code coverage from 80% to 95% of service X and reduced latency from 100 milliseconds to 50 milliseconds of service Y."

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