Since beginning my education in data management and analysis as a graduate student, I’ve realized the value in continually expanding and updating my conceptual and technical knowledge within this evolving field. While this has often seemed an intimidating and overwhelming process, I’ve discovered an incredible range of free and low-cost resources available for compiling a comprehensive learning plan aligned with one’s professional interests and aspirations. Following are a selection of courses and tutorials I’ve found helpful in my journey and would recommend to fellow data practitioners and students:
Data Science for Everyone, DataCamp
This free course is a great primer on the data life cycle from raw form to insights able to inform strategy and decision making, outlining the roles each type of data professional (data engineer, data scientist, data analyst, business analyst and business intelligence analyst) play in this process. I would recommend anyone interested in a data-related career take this course to identify which stage(s) of the data transformation process appeal to them most so they can focus their learning and career development on the appropriate concepts and technologies accordingly.
Data Science for Everyone can be completed as either a standalone course or component of DataCamp’s Data Literacy Fundamentals skill track. It’s also a great primer on the DataCamp platform, a fun and intuitive learning tool. Should you find yourself interested in a premium membership upgrade, use this referral link to sign up and your registration will support operation of this site.
Data Analytics Basics for Everyone, edX
Like DataCamp’s Data Science for Everyone, edX’s IBM-developed Data Analytics Basics provides a full overview of the data life cycle, delving into specific processes and tools you will likely encounter along your professional journey. The course is well-designed whether completing in full (a verified certificate of completion is available for $99) or focusing on specific content. The course also serves as the foundation for several IBM professional certificates available through the platform.
Welcome to Codecademy, Codecademy
Once you better understand the data transformation process and have identified areas of interest for further learning, it’s possible you will be learning the fundamentals of coding for the first time. Fortunately, there are many great beginner-friendly coding tutorial providers available. This Codecademy course provides a great overview on what to expect when learning through the site or those of similar providers including DataCamp and DataQuest. It also segues nicely into the Learn How to Code course which further introduces the concepts central to multiple programming languages used in the data life cycle.
Workshops, Short Courses and Full Bootcamp Immersives
Following your mastery of the basics of data literacy and programming, you may discover your interest in pursuing these concepts further through a full certificate or “bootcamp” experience. Such programs are available in a variety of formats and price points from providers including traditional colleges and universities, premium memberships to websites including those featured in this article and the increasingly popular intensives offered through companies like Springboard, Flatiron School and FullStack Academy. Of these providers, I find General Assembly offers an especially rich range of introductory and advanced content, along with informative career panels sharing the perspective of seasoned data and tech professionals able to provide guidance on how they navigate the continually evolving field. I recommend bookmarking the company’s schedule of upcoming events, many of which are offered on a recurring basis at a free or nominal cost, as these sessions provide a great opportunity to network, learn directly from peers and mentors and evaluate whether more extensive learning experiences are a fit given your interests and goals.
These are just a few resources I hope are helpful in your data education and professional development. Share your thoughts in the comments section following this post. I’d love to learn what you’ve discovered in your own data exploration!
