How I Self-Study Anything as a Data Scientist

The job landscape is undergoing a seismic shift. A McKinsey report predicts that about 40% of current American jobs will vanish by 2030. But as some doors close, others open. The future job market will not only create new roles but also roles beyond our current imagination. This brings us to an intriguing question: how do we prepare for careers that don’t yet exist?

The Inevitable Career Pivot

Many of us will face significant career changes, possibly in our 40s, 50s, or even 60s. Perhaps you’re already in the midst of such a transition, eyeing opportunities in data science and tech-related fields. What makes these fields particularly enticing is the vast expanse of knowledge available for self-study. In today’s digital age, learning is no longer confined to traditional education paths.

1. Belief Systems in Learning:

The journey into data science or any new field starts with the right mindset. Overcoming self-doubt — thoughts like “I’m not good at math,” or “I can’t code” — is crucial. We often carry limiting beliefs from our childhood into adulthood. These beliefs can shape our educational and career choices, sometimes leading us away from our true interests.

2. Identity and Learning:

More profound than mindset is the concept of identity. Transitioning into fields like data science might require a shift in how you see yourself. Letting go of the “non-tech person” identity and embracing a new self-image can significantly ease the learning process.

3. A Five-Step Framework for Self-Learning:

In a world awash with information, the challenge is not finding content but effectively processing it. Here’s a five-step framework for self-directed learning:

1. Understanding Your ‘Why’: Start by asking why you want to learn something. This intrinsic motivation is key to sustaining your learning journey. 2. Choosing the Right Materials: Blend video courses, books, and online resources. Platforms like Coursera and Skillshare offer a wealth of knowledge across various topics.

3. Absorbing the Material: Dive deep into the concepts. Create stories, join study groups, and engage in discussions to reinforce your understanding.

4. Retaining and Documenting: Keep track of what you learn. Documenting helps in revising and gives you a sense of accomplishment.

5. Applying What You Learn: Put theory into practice. Work on projects, experiment with new ideas, and engage in deliberate practice.

4. Sharing and Showcasing Your Work:

The final step in the learning process is to share and showcase your work. Platforms like Medium, YouTube, Twitter, or LinkedIn are excellent for displaying your projects and engaging with the community. Remember, there’s no such thing as a project that’s too simple. Each step you take is a milestone in your learning journey.

5. Managing Energy for Effective Learning:

As we juggle various responsibilities, managing our energy becomes as important as managing our time. Creating a conducive environment for learning, minimizing distractions, and finding the right balance between work, hobbies, and personal life are essential to maintain focus and drive.

Conclusion

The future of work is a canvas of possibilities, and self-learning is the brush with which we can paint our career paths. Whether it’s transitioning into data science or any other field, the power lies in our hands. Embrace the journey, keep learning, and remember — the only limits that exist are the ones we place on ourselves.