Aspiring Data Scientist Uses Course Projects to Land Job

Aspiring Data Scientist Uses Course Projects to Land Job

I recently graduated with a Bachelor’s degree in Electronics and Communication Engineering. In my sophomore year of college, I was interested in programming and anything related to computer science and that’s when I started taking courses on Coursera.

Because I’m trying to start my career as a Data Scientist, I’m constantly taking courses online and trying to improve my skills. I’ve completed all the courses in the Data Science Specialization by Johns Hopkins University and Introduction to Probability and Data by Duke University. I’m also currently taking Deep Learning taught by Andrew Ng. Taking these courses helped me lay a solid foundation for data science concepts.

The practical machine learning course I took helped me get my first internship for a team participating in MIT Kumbhathon. I’ve developed a good sense of discipline from taking courses on Coursera and am thankful for the amazing feature where I can switch sessions.

I took Practical Machine Learning and for the course project I made an interactive map for visualizing IPL (cricket league) matches. The internship I applied for also contained a similar project – I had to produce heat maps of the city Nashik based on crowd density. Because of my experience from course projects, I got selected without an interview. My profile listed the MOOCs I’ve taken (7/10 courses on the profile were from Coursera’s Data Science Specialization) and the projects I’ve completed.

Every field of study has to have some sort of data, and where there is data, there is a potential opportunity for the application of data science. Since data collection and storage became faster and cheaper, it has become essential for most business applications to apply data science for making smart decisions. Data science doesn’t need to be confined to business applications alone, it can help you with your daily life decisions. The cool thing that I like about data science is that you get to learn a lot about the domain you’re applying data science too.

The good thing about Coursera is the flexibility: when you take it, where you take it from, what you read and what you discard are all based on your choice, unlike a rigid campus curriculum. Most of the courses I take have hands-on projects. It’s a good way to experience the problems you’ll face before you can develop a real application.

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