So you’re looking to break into data science? You’ve come to the right place. Data scientists are a fairly new role in the space, and you may be wondering what makes it different than an analyst or a data engineer…
In Layman’s terms, data scientists take things a step further than just analysis, and focus on building forecasts and other types of predictive models. To move into a data science role, it will be especially helpful to have a foundation in data analysis, either through college coursework or real world experience.
Once you’ve got the basics down, you can jump into some of our recommended resources to start the transition into data science.
1) Data Science Academy from CoRise
We couldn’t put together another learning resource list without highlighting a learning track from CoRise. If you haven’t read our other posts in this series, CoRise offers online learning focused in the data space, with flexible part time courses and learning tracks.
In this track, you’ll focus on three courses covering statistics, making data-driven decisions, and causal inference for data science. After the 12-week program, you’ll feel confident about taking the next steps into a new role in data science.
2) Learning Tools from Kaggle
If you’ve started looking into the data science space, you may already be familiar with Kaggle. Their learning page is dedicated to providing free self-guided courses with tutorials and practical exercises.
There are a number of courses available that you can complete in just a few hours, which is helpful if you can’t commit to a learning track that lasts for a number of weeks. Upon completion of each course, you’ll get a certificate you can add to your LinkedIn page to share your progress. And did I mention they’re all free?
3) Introduction to Data Science Specialization from Coursera
Another powerful and easy to use online platform is Coursera. They offer structured but flexible learning through individual courses, and specializations that walk you through a series of courses building off of each other.
In this specialization, you will be learning from one of the biggest names in technology: IBM. You’ll work your way through four courses over the recommended time frame of four months to understand what data science is, what tools data scientists use, and the methodology behind what projects these teams typically work on.
4) Understanding Data Science course from DataCamp
Many of the resources we’ve looked at so far are great fits if you already have an idea of the direction you’d like to go. On the flip side, this course from DataCamp offers you an introduction to data science with no coding involved. This means you can get a free look into the role to make sure if it’s the right fit for you, before diving into some of those more involved courses and exercises.
5) Complete Machine Learning & Data Science Bootcamp from Udemy
Machine learning is something that you’ll frequently see data scientists talk about. It can be a key piece of their role to help them more quickly work through large datasets and find the answers or trends they’re seeking.
This bootcamp by Udemy offers you the best of both worlds: lectures in both data science and machine learning. It’s an exciting way to see how ML comes into play for data scientists, and what kinds of problems and models you’d be working on in this type of role.
While these resources primarily focus on online learning tools, there are also a number of universities that offer both in person and online Master’s programs in Data Science. If you are looking for a more formal classroom learning structure, it might be time to head back to school!
In the meantime, please consider subscribing to our guided tour through data roles led by people doing the jobs. The series is called "Captain's Compass" and you can learn more about it and sign up for alerts by following this link.
