Why is becoming a data scientist so difficult?

First of all, data science is an interdisciplinary field; it involves two or more disciplines into one activity. It uses theories, techniques, methods, designing scientific algorithms and more to extract insights from data. Talking about methods, it is not just about data visualization; it is also about data mining, machine learning etc. Overall, Data science, as a field, is an amalgamation of both hard as well as soft skills. 

Coming back to your question, becoming a data scientist is indeed difficult because of the skillset it demands, the time it consumes (it’s slow and daunting), the energy and perseverance it needs in mastering. You need to possess the much-needed skills and knowledge to become a successful data scientist. 

Moreover, you need to know that the difficulty level depends entirely on what type of data scientist you’re eyeing to become and your grasping ability too. There are different kinds of data scientists such as Data Scientist as machine learning scientists, Data Scientist as business analytic practitioners, Data Scientist as mathematician and more. 

Another reason why starting a career as a data scientist is challenging for many is because of the lack of professional coding and big data manipulation. Let’s say it is because of not enough practical knowledge. Classroom theoretical knowledge is important in building a strong foundation but it isn’t enough to start a career. Data science is an applied field, and you need practical experience/knowledge. 

Last but not least, it’s true that employers across the globe are hunting for professional data scientists, and this career is rewarding financially so, many are fascinated and switching their career as data scientists. But, they often failed because they overlooked the importance of mastering the fundamentals when it comes to becoming a professional data scientist.