Pacing career in Data Science is not an easy task when a lot of things are to be kept in mind regarding Data and insights. A beginner learns from different sources, goes through various platforms and gather all the information to achieve success in the field.
Gaining useful and updated information, required skills always work the best for you but it is also mandatory that you should be aware of the mistakes as well that you might unknowingly commit which could hamper your growth while moving ahead in the career. The article basically draws attention to some of the common mistakes that every beginner commit while pacing ahead in Data Science and must keep a regular check on himself to help him avoid these silly mistakes. After all, you are moving in a career that requires detailed attention from you.
Not Focussing on Projects
It does not matter how much theoretical information you have, without practical knowledge everything seems a waste. Data Science is purely a practical field where you have to deal with real-time data. Indeed your theoretical understanding of the concepts will help you a lot in gaining a better understanding of the various aspects of your data but you should be excellent in practical approach as well. For the same thing, beginners should committedly focus on projects related to Data Science that will hone their skills and develop their precision over handling data at real time. As a beginner, you could choose any of the Data Science projects that you might be interested in by exploring and finding out the relevant topic which requires attention.
Confusion over Learning different Tools
There are a number of platforms available online offering different certifications and courses. A number of tools you will find across various platforms on the internet that it becomes very confusing that which tools should be mastered. Aspiring candidates often try to then learn multiple skills and tools at the same time in desiring for the best. This is obviously not a good step taken. It is a common mistake committed by every beginner. Beginners are required to analyze the companies they desired to get recruited in and should note down the skills and tools companies are searching among the potential candidates. This will help you in choosing one tool among many. Gain mastery over that and then move to the next.
An Incomplete Resume
For any company, you want to get recruited your resume is the first thing that the company looks and judge you based on that. An incomplete resume without the necessary projects and skills mentioned will have the most probability of being getting rejected. Always build your Resume with your projects and certifications mentioned in an articulate way. You need to tell your employer a lot of the practical skills you have developed over the years for the data scientist role. Build your profile on LinkedIn as well. It will further impress your employer of you getting social and have a presentable profile as well.
Ignoring Communication Skills
Beginners often ignore Communication and take it for granted while it is the fact that communication is the key factor involved in any Data Scientist career where he needs to tell a lot about his insights to everyone for the effective decisions of the company. Do not stand low on communication and start to improve your communication from today because you need to tell a lot to the people about your findings in the organization and having a low communication skill will definitely not make the way for a successful career ahead in Data Science.