Self learning is a process by which individuals take the initiative, with or without the assistance of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning and evaluating learning outcomes.
Wikipedia compares self-learning to Autodidacticism which it then defines as education without the guidance of masters (such as teachers & professors) or industries (such as schools).
Learning never really ends in Data Science. Right from the very first day one starts to learn Data Science, till gaining some proficiency and eventually a job in Data Science, learning still continues. As one gets deeper in the art, the kind of questions asked, interests, etc. may change, requiring also a change in the channel/method of learning.
Self learning offers a large pool of possibilities and flexibility while learning which the traditional way of learning may not offer. With the recent influx of so many online courses and schools, the internet has made self learning even richer in what it is able to offer.
In my view, self learning is an important skill that everyone who has a career in tech should embrace. Here are some reasons why:
- Data Science is constantly evolving
Whether it is in simplifying data science processes or algorithms or the evolution of new ideas, everyday Data Science is evolving. Technology frameworks change and get updated with time, from little details of possibilities in a newer version of a library on Python/R to new algorithms that make predictions/regression better or faster, one can easily be left far behind and lacking information about new developments in Data Science.
Due to the pace at which Data Science is evolving, significant skill gaps are appearing quicker than ever, placing pressure on Data Scientists to constantly upskill. Thankfully, the internet provides a vast amount of resources such as online courses which are readily available for one’s consumption.
2. Flexibility and Time Management
Flexibility makes learning enjoyable, relaxing and less intense. With a lot of online courses and e-schools now offering flexible learning patterns this makes self learning even easier. Whether one is reading a book, or reading an academic paper, or taking online classes, all these can be done at one’s pace and free time.
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I would also like to state that this flexibility should not be abused. One problem is how to stay motivated from start to finish. This article gives some advice on how to address this.
3. Cost Effective
Compared to being taught, where one often has to pay for the services of a teacher, self learning is often cheaper or even free.
For people who want to switch careers into Data Science, I often recommend self learning to learn the basics. If something is unclear, you can ask a friend who works in the industry, or interact with others on public forums for explanations.
I have to say that even though self learning offers some interesting advantages, from my personal experience, being taught by an experienced teacher also gives speed to learning, and you are likely to be taught some details which your teacher has learnt from his/her experience which one may not get through self-learning. It is also said that being able to put a face to what is being taught also helps some people’s assimilation.
My aim of writing this article is to encourage learning and show some of the reasons why asides being taught, we must also be deliberate to teach ourselves. Let me know what you think in the comments.
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How Important is Self Learning in Data Science? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.
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