Reflections on a Year of Self-Guided Learning

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During 2021 I invested much effort toward self-guided learning. Propelled by objectives to explore new career avenues and free time the COVID pandemic provided, I sought to enhance my skills in programming, data science methods and technologies and marketing techniques. As I proceeded in discovering affordable resources and compiling these into a custom curriculum, I often found my time working through these materials to be a high point of my day, even forgoing evenings of Netflix to complete tutorials well into late night hours.

By the end of the year, the set of courses I’d completed was quite extensive and I found it enlightening to chronicle my learning journey into the following list, organized by topic and inclusive of links to the source materials. I encourage you to review the course pages and share your feedback on resources you’ve found helpful in your own learning!

Principles of Programming and Computer Science

Prior to delving into any specific platform or coding language, it can be helpful to gain an understanding of the underlying logic powering each of these tools. Technical and non-technical users alike can benefit from developing an intuition of how technologies they interact with daily function and operate, and these are some great primers with which to start:

Principles of Data Science and Analytics

I wrote in a previous post about these courses offering a conceptual overview into data science and analytics, available here. The materials will be valuable to anyone seeking insight from the expanding network of data points intersecting all components of our lives:

Python for Data Science and Analytics

In July I completed a course in Python for Data Analytics as part of the Southern Connecticut State University BioPath Bootcamp Series. The course was offered free and open to all through a joint initiative of Southern Connecticut State University, New Haven Innovation Collaborative and CTNext. I highly recommend following universities and nonprofits within your community for offerings like these which are cost-effective and provide opportunities to build connections within fields of interest. The bootcamp’s synchronous learning environment provided the support necessary to overcome preliminary obstacles in my independent learning, enabling me to move forward with more advanced courses like those in Dataquest’s Python Basics for Data Analysis series.

Marketing and Web Analytics

Grow with Google and Content Marketing Programs, TechFWD Digital Skills Navigator, Stamford Partnership

Like Python for Data Analytics, I found both TechFWD offerings through a community development organization, writing about my experience with the Content Marketing program in this previous post. The Grow with Google series provided a helpful complementary perspective, particularly where the courses’ materials aligned with a focus on search engine optimization and organizing online content for prime traffic and visibility.

Publisher’s M.O. Masterclass, Publio

I was offered the chance to pilot this course developed by a marketing services firm I connected with through the TechFWD Content Marketing program (whose curriculum they also facilitated). Their masterclass builds upon that curriculum (available free via HubSpot and Semrush academies), introducing their signature “North Star” methodology and providing templates for applying concepts from both courses toward participants’ own unique business objectives. It was great having the opportunity to navigate the course in its development stages and provide direct feedback to be incorporated into its subsequent iterations.

As we begin a new year, I look forward to continuing my professional learning and chronicling my journey accordingly. I invite you to join me in thinking through your own educational goals and work toward achieving these in 2022!

Getting Started with Data Learning Resources Online

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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!