Day 4: How I Switched Careers From Mechanical Engineering to Data Science

In this post, I’ll share how I transitioned from mechanical engineering to data science in 2020 while working full-time.

If you’re looking to switch careers, my experience might be useful. Many people make similar mistakes that prevent them from transitioning careers.

Step 1: Have the Right Reasons for Switching

I wanted to leave engineering because my growth had plateaued. I needed something more stimulating. After completing a 365datascience course out of curiosity, I was hooked.

I researched what tasks a data scientist does day-to-day and envisioned myself in that role. I found the balance of creativity and technical ability appealing.

Don’t switch solely for trendiness, salary, or prestige. You must deeply want the change and sacrifice free time.

Step 2: Reverse Engineer Job Search Process and Identify Core Skills

I spent a day researching data scientist job postings on LinkedIn and identified the core tools and skills hiring managers seek - Python, SQL, and dashboarding.

Step 3: Create a Learning Plan and Stick With It

I found online courses from Udemy and Udacity to learn these skills. Don’t get analysis paralysis from endless course options. There are now many great roadmaps to becoming a data scientist.

Step 4: Quickly Learn the Required Skills and Avoid Tutorial Hell

I completed these three courses but don’t necessarily have to. You can learn enough from the courses to tackle personal projects. Avoid tutorial hell by completing course after course. Have a bias towards action and solving projects.

Step 5: Come Up with Interesting Personal Projects and Solve Them

Personal projects are key to landing a role. They differentiate you from other candidates’ resumes. Projects are also what you’ll discuss in interviews.

I identified that churn prediction and customer segmentation were common data science use cases and completed a virtual KPMG internship project.

Step 6: Update Your Resume with Your New Projects

Highlight projects instead of completed courses. Hiring managers don’t care about the MOOCs completed. They care about how you’ve applied those skills.

Step 7: Start Applying and Practice Data Science Interview Questions in Parallel

With projects in your portfolio, start applying. Create a spreadsheet to track applications, job requirements, and progress. Practice data science interview questions (technical and LeetCode) simultaneously. I recommend Anki for retaining technical knowledge.

Step 8: Persevere Through Rejections and Iterate

Data science is an umbrella term and can mean many roles. It may be tempting to apply for all roles, but it’s best to be selective with your efforts.

As my friend Jon said, N is the number of applications required to land your role, and you just need to keep iterating and applying until you reach N.

Bonus Tip

Learn how others have done what you wish to do from blog posts and YouTube videos. Reach out to people who have made the transition and learn from their experiences.