Data Science has experienced a tremendous popularity increase in recent years. More and more businesses have been investing in this field to create value out of data.
As a result, lots of people decide to make a career change to break into data science. As much as it sounds appealing and fun, learning data science is an exhausting and time-consuming journey.
It is in your hands to make this journey more efficient. However, there are certain things that may have a negative effect on your performance.
In this article, I will discuss three points that I think may break your motivation while learning data science. I will also try to provide suggestions to overcome these challenges.
Scope fallacy
Data science is such a broad field. Wherever there is access to data, we can talk about data science. Finance, health care, retail, banking, computer vision, and security are just some of the fields that have data science applications.
Although data science is built around common principles, the implementations and applications in different fields may require different techniques or methods.
When I started my data science journey, I had intentions to learn how data science is used in all those fields. It was a big mistake. There is no way one can specialize in so many fields.
It is good to know about how data science is applied in different fields in general. However, if you try to go deeper or specialize in many fields, the huge amount of materials to learn might break your motivation.
I call this the scope fallacy. You may argue that the scope of data science is huge. I totally agree but what one data scientist should and can learn is limited. Otherwise, you will struggle to make decent progress.
You should obtain advanced skills in a particular field. For instance, if you master time series analysis, you substantially increase you chance to land a job in the financial domain.
Tool fallacy
Matplotlib, Seaborn, Altair, and Plotly are some of the Python libraries for data visualization. There are also other Programming languages that have data science libraries such as R and Julia.
Thus, we have numerous tools to learn, practice, and implement data science. It can be both an advantage or disadvantage depending on your approach. If you try to master a large number tools that do the same thing, then the rich selection of tools become a disadvantage.
Everytime I heard about a new tool, I felt like I needed to learn it. As a result, I faced with a mountain of material to learn. I now call it tool fallacy. Matplotlib or Seaborn is enough to create pretty much any type of data visualization. TensorFlow or PyTorch is enough for creating deep learning models. You do not have to master both.
I suggest to pick one or two libraries for a particular type of task, especially at the beginning of your learning journey. You will be able to accomplish most of the typical tasks with any library.
Community fallacy
Data science is not well-established in the traditional education system yet. We mostly learn through MOOC courses, online tutorials, blog posts, and similar resources.
Another highly valuable resource for learning data science is the data science community. People who are already in the field share their knowledge and experience. This is actually a very good thing. However, it may break your motivation if you can’t take your stand wisely.
The large community with experienced people made me feel inadequate at first. I do not mean data scientists with 10 years of experience. In some cases, I felt like I was way behind a data scientist with one year of experience.
It did not take me long to discover that I could catch up with dedication. I suggest to take advantage of what others share. You should not let it make you feel inadequate and break your motivation.
Lots of people sharing their knowledge does not imply that you are way behind. Think of it as an opportunity to learn from others, not a motivation breaker.
Conclusion
Learning data science is a challenging task. It requires time, effort, and optionally money. However, it is in our hands to make this long journey more efficient.
You should not let your guard down because of the demotivating things we have just mentioned. You should always keep in your mind that others also face the same challenges. You can take a step ahead by handling these challenges wisely.
Thank you for reading. Please let me know if you have any feedback.