Data Science has been voted as the most popular job of the century. Nobody can deny that working in data science is an excellent choice.
However, many budding data scientists struggle and even give up too soon because they haven’t thought the process through.
If you’re on the path to becoming a data scientist, be aware of and avoid these mistakes:
Learning theoretical concepts but lacking in applying them
it’s good to get a grasp of the theory behind machine learning techniques. But if you don’t apply them, they are only theoretical concepts.
Working on Machine Learning techniques without learning the prerequisites
Most of the beginners in Data science dive into ML techniques like SVM, logistic regression and related algorithms. But without learning the mathematics behind these techniques data science cannot be used to its full potential.
Relying solely on certifications and degrees
In the field of Data Science experience with data is valued more than the degrees and certificates candidates holds.
Kaggle competitions are – unfortunately – not real jobs
This is the biggest misconceptions aspiring data scientists have these days. Competitions and hackathons provide us with datasets that are clean and spotless. But in the real world, data is messy and unclean data.
Miscalculating how much time is involved
It takes a lot of time and a lot of work to become a data scientist. How much really depends on each individual and how much background you already have in the field.
But the reality is that regardless of relevant experience, you will need to spend a lot of hours to get better in this field.
Giving tools and libraries precedence over the business problem
A business problem always precedes the priority over the tools and libraries.
Solid knowledge of tools and libraries is excellent, but it will only take you so far. Combining that knowledge with the business problem posed by the domain is where a true data scientist steps in.