The job opportunities for data scientists are numerous because the data science world keeps on growing. It’s not surprising that most ambitious data scientists who have just graduated or they have changed their career are getting recruited by many companies.
With amateur data scientists taking up the roles, there is an inflow of mistakes happening in businesses as they are adjusting to the new workplaces. Here are 10 common mistakes that amateur data scientists are always doing.
Focus on theory
Amateur data scientists have a great grasp of a theory which provides a good foundation when you are working. However, most of them don’t know how to apply this theory resulting in people who have plenty of information which has no use.
Problem-solving approaches require data scientists to find solutions to practical situations. Learning never stops and it’s always important to have enough time for practicing the concepts. Every time you learn a new aspect of data science, ensure to implement that knowledge by either working on a data set or solving a problem.
Less focus on data
Most amateur data scientists are putting less emphasis on data. If you are working, it means you know the algorithms and the importance of data which has to be used to get the best results.
Analyze the data and run the algorithms to have a good range of metrics. Also, ensure that there is no flaw and risks in the future. Some risks, such as data leakage and overfitting can cause serious repercussions in the future. So, while creating a model, focus on the data which will eventually determine the quality of the model.
The knowledge base
Most people pursue a data science career path because of attractive high salaries. They might have done so well in other subjects such as mathematics and believe they can do equally great in data science.
It’s a pity that they rush into problem-solving work instead of attaining the proper education. In the end, such amateur data scientists don’t grasp the basic concepts.
It’s recommended that while you are still at high school, focus on taking the proper combination of courses and master them. Here are some fields you need to learn:
- Probability and Statistics
- The graph theory
- Linear Algebra
- Programming languages such as Python.
Successful teams ensure that there is an open-door policy for their team members. However, some amateur data scientists find it difficult to share their experiences with their fellow workers. They might not be sure about certain issues and yet they are afraid of being criticized for lack of proper knowledge.
To be a better data scientist, you will have to communicate with your team members and your HR system, if you work in a company, regardless of the type of feedback you can get. After all, feedback will help you to improve on your weak areas, ensuring that you can solve issues on your own.
Complex issue solver
A data scientist carries out challenging tasks which deal with using codes, advanced mathematics and statistics. They know how a company works and it sometimes gets into amateur data scientist’s head to impress their clients.
So, they would rather use complex statistical and computer science methods to solve the problems. This is always a bad tactic as it will cost you effort and time. A data scientist should spend most of their time only exploring data and visualizing unless you are requested to perform other duties.
A complicated technical resume
Most amateur data scientists create resumes that have complex jargon from their areas of specialization. They outline all the tools and techniques that they use in their practice.
Unfortunately, most hiring managers and recruiters are only interested in finding out your skills which should be presented in an easy to understand way. Consider that the person who is going to read it is not a data scientist and it might be difficult for them to understand most of data science terms.
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Neglecting simple work
Every type of work that you are requested to do is important for the business. Most of the times, clients just require a few guidelines and actionable insights from your perspective.
These insights have huge consequences on their sales, and you won’t be successful at your work if you can’t do them. But some data scientists are in the habit of dilly-dallying on such simple tasks, thinking it could be done at a later time.
Lack of communication
Employers and clients will trust and enjoy working with you if you communicate your ideas with them at all times. They want to know that you are doing a good job on their requests.
Keep the communication channels open so that they can trust you with their work. Ensure that you start communicating from the onset, meaning let the employer know each step of the analysis. Don’t wait to present the final detailed report for them to know what you had been doing.
Lack of a plan
With tons of available data, amateur data scientists begin working without a plan and defined questions. Data science should always begin with specific objectives and questions.
Having a structure will help you to have a direction which has a purpose of what you want to achieve. The starting point is to develop hypotheses which will assist you in coming up with the final objective.
It’s easy for amateur data scientists to get frustrated at their workplaces because of the work environment. Some data scientists expect to find easy data to work with, but the reality is that there is pretty messy data out there.
This work can be time-consuming, less fun, and there is no accuracy when you are new on the job. You will need to ask for a way out from your fellow co-workers and read a lot more to gain the required experience.
Working as a data scientist provide opportunities for you to learn how to apply your knowledge and skills in different situations. It is something that you can’t get anywhere else, and you have to make the right decisions to learn while working. The 10 mistakes discussed above need to be cut out to enable you to improve your life and the company you are working for.