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1) Switch your major from marine engineering to something more aligned with data science (Computer Science, Statistics, etc.). This may be difficult to do depending on your school
2) Complete your marine engineering degree, but develop data science skills to pivot to that field
3) Complete your marine engineering degree, and follow it up with a masters in analytics / data science
#1 would be the most straight forward IF you can do it without having to, say, repeat a year (which will cost a lot more money).
In the case that #1 is not feasible, #2 should be a viable route. As someone who's worked in data science, your major matters, but the skill set and specific experience is more critical. Since data science is still a developing field in its modern sense, people from all sorts of quantitative majors pivot to data science: CS, stats, physics, all types of engineering, etc.
What you'll need to do is demonstrate to employers that you can do well in an analytics / data science role. You'll be able to do that via:
1) Developing core skills relevant to data science
2) Applying those skills in a practical way
For #1, the first thing you'll want is a solid foundation in stats and computer science. If you've already taken these courses as part of your engineering degree, great. If not, no worries - try to enrol in them at your university. If that's not possible, then you can still just do them on Coursera or MIT Opencourseware online in your spare time! Another course I'd recommend is basic database management (ex. relational database theory). These are foundational courses for the field.
Then, you'll want to start picking up tools. Python is perhaps the most commonly used language in data science. Once again, there are many resources online that you can leverage to learn Python for data science & analytics. R is also common. SQL is another tool that'll be helpful (this is used for querying data sets).
Once you have the skills and knowledge: apply them! The easiest way to do this is through personal projects. That is, pick a problem / data set that you want to analyze and make a solution! Kaggle is a great source for clean datasets, so it's great for beginners. They'll even state what the "challenge" is for that dataset, so it's all well defined for you.
As you progress, I encourage you to start doing original projects. That is, make up your own problem statement and find and cleanse your own data. The data collection, cleansing, and engineering process is *much* harder than it seems and takes up most of the time. However, this is what real data science looks like. For example, suppose you're a basketball fan. Maybe you could try to build a model that will predict whether a rookie will be a future all-star or not? It's best to take this project completely end-to-end: from the raw data all the way to a simple interactive tool for the model that users can play with.
Overall, you should be able to get into data science / analytics with hard work, even from marine engineering.
Herman recommends the following next steps:
- Develop core knowledge (stats, CS, databases) via online learning (Coursera, MITOCW)
- Learn relevant tools and languages (Python, SQL, R, Excel)
- Apply your knowledge and skills to simple projects (Kaggle competitions)
- Apply your knowledge and skills to personal projects created by you end-to-end
- Use these projects to create your portfolio, and try to secure an internship in data science / analytics
100% of 1 Students