Why Humbling Yourself Will Improve Your Data Science Skills

We would all love it if we could say we knew everything. Unfortunately, that is not possible. Sometimes I tell myself “I don’t know anything”, to push myself to learn and continuously improve. 

Data Science is one of those sectors which require constant learning and there is always room for improvement. It’s difficult to keep on top of things and feel a sense of accomplishment in the world of Data Science. Once you have finished learning one thing and feel confident about it, you catch yourself finding new topics or areas to learn. 

Nobody can sit here and say they know everything. You have Senior Data Scientists, who have worked in the sector for 10+ years still have to google how to join two datasets. It doesn’t mean they don’t know it, they may have just not had to use that code in a while and they have forgotten. 

Once you start working in the Data Science field, you will be interacting with other Data Scientists, Analysts, Machine Learning Engineers, and more bouncing off knowledge from one another. However, there will come a point where you may not know something that your colleague does, vice versa. However, telling your colleague you know something when you have no idea can be a detriment to your confidence at times. 

It’s great if the task at hand which you are unaware to do can be solved by simply using Google, watching a YouTube video, or looking at Stack Overflow. However, if you continue to keep telling your colleagues or boss that you know something, and you don’t know; you will find yourself drowning in extra learning. Instead, you could have just said “Sorry, but I don’t know how to do this”. This way, your colleagues and boss understand your strength and weaknesses, providing you with the right support/training in order for you to improve in particular areas. 

This also applies to people in Senior roles. If you don’t have the right skill-set to manage and guide a team, you will be overwhelmed, stress levels will increase, and it may make you contemplate your position. 

Your first job is always going to be frightening. You will feel anxious and nervous to speak your own opinion. I will go through a few points that I believe everybody should incorporate into their work and personal life. 

1. Understanding your strengths and weaknesses

 
You don’t have to be exceptional at everything. However, to be in Data Science you need fundamental skills. If you are a Data Scientist who enjoys data wrangling, creating data visualisations but have little experience in building Machine Learning models; this is a weakness of yours that you can work on. Admitting to yourself that you won't be advanced in every skill available at the hands of Data Scientists is the first step of growing as a Data Scientist. 

Once you have identified your strengths and weaknesses, what you like and what you don’t know; you can narrow down your self-development. If you are particularly interested in becoming a Machine Learning Engineer, your skills as a Data Scientist will come in handy. However, you will need to look into learning areas such as Algorithms, Natural Language Processing, Neural Networks, and more. 

You need to understand what skills are beneficial to your career, currently or in the future. If your career plans require you to use Python and R as programming languages, learning another language such as HTML won't be useful. You don’t want to be a rookie in everything, and a master in nothing. 

2. Speak up!

 
If you don’t ask, you don’t get. Data Scientist roles require a heavy ton of technical skills, alongside soft skills. It is unfortunate, but many people will assume that you will know how to do pretty much everything because you applied for a specific role. As we already know, that is not the case. There is always room for improvement and time to learn different skills. 

If there is a hard deadline on a project at work and you have been asked to complete a particular task to fast track the process, however, you don’t know how to attend to it because you don’t possess these kinds of skills. You will find yourself in a pickle. Speaking up and informing your colleagues what you can and cannot do, rather than feeling nervous and ashamed will save you in the long run. You may be allocated another task in which the other team members are aware you are comfortable doing, ensuring everyone will meet the deadline. 

Speaking to your senior in regards to your weaknesses opens up a conversation of self-development. The company may want you to improve on these and have you undergo specific training or allocate you self-development time during work hours to support you. If a company can help you become one of the best Data Scientists, they will. 

On the other hand, you may feel the tasks assigned to you are below your skillset. Instead of spending the day, doing simpler tasks where you could be of benefit in another area is important. This is the easiest way to climb up the ladder. Speaking to your Senior about your strengths and how they could improve the efficiency of the company can resolve many of the business’s issues. It’s a win-win situation. 

3. What other steps can you take?

 
Applying for the right job

It’s no secret that people apply for vacancies that require a specific skill set, however do not possess these skills themselves. You are setting yourself up for failure if you do this. Instead of applying for jobs based on salary, base it on your current skills. 

There is no harm in taking an entry-level job, building your skills, and working your way up from there. Humbling yourself and working within your means is the first step to building your career. The keyword to take from that is ‘building’. It’s not going to be handed to you, so you will have to start from somewhere. It’s better to work from the ground up, rather than falling from the top. 

 
Online Courses

There are a variety of online courses that you can take to improve and increase your skillset. You can take courses through Udemy, Coursera, Udacity, and more. They can range from learning a specific programming language such as Python or C++, or understanding Database management and SQL. 

 
Reading

There are so many reading materials online to help you improve your understanding of a variety of topics. Textbooks, Academic papers are available online as well as platforms such as KDNuggets, providing you with quality resource materials to guide, help you understand, and build your career. 

4. Continuous learning

Continuous learning is your self-motivated and persistent manner to expand your skillset and develop future opportunities, personal or professional. You can decide one day that you are interested in Medicine and want to incorporate your Data Science skills in that field. Or you may want to become a Senior Data Scientist and are aware you lack SQL knowledge. 

Learning never stops. Always say to yourself “I don’t know anything”; it gives you the determination to continue your learning journey. Knowledge is available at everyone’s fingertips, if you don’t take advantage of it, you will remain in the same position. 
Being able to humble yourself and push yourself to always learn will help you boost your profile, stay relevant, open new doors for yourself, and prepare for the unexpected.

 
 
Nisha Arya is a Data Scientist and freelance Technical writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.