Photo by Moose Photos 
 

When I started learning data science, I was coming from a technology management background. I knew few things about programming, statistics, or machine learning. 

My biggest concern was the lack of a programming language. I always discounted math, statistics, and probability. I even considered machine learning as a trial and error experiment where you combine a bunch of algorithms to improve model performance without knowing how it works or what value I bring to the company. 

In this post, I will share 3 things that I was completely wrong about and how you can use them to navigate the data science career path. 

 

1. You Can’t be Data Science Generalist

 

I used to think that my goal was to become a “Data Scientist”. So, I worked on it and ignored other specialties such as Computer Vision, NLP, time-series, BI, and ML engineering. 

I thought data science was all about data ingestion, cleaning, analysis, visualization, models, and reporting. 

“I was completely wrong”

 

Image by Author
 

In the field of data science, you can’t be a generalist. The companies want people with expertise in a certain field of data science or agile data scientists that can work on multiple tasks such as experiment tracking, business intelligence, reporting, and model deployment and monitoring.  

If you look at any “Data Scientist” job posting, you will see how companies are looking for people with expertise in a specific field. 

For example, A Truck tracking company will always be looking for data scientists who can work with modern ML stack and have experience with complex Computer Vision models.
 

Lesson: You must learn about the entire data science life cycle and find what works best for you and get good at it. 

 

2. You must Understand Statistics and Machine Learning Algorithms

 

Even after several years of working as a Data Scientist, I am constantly learning about new types of algorithms, especially reinforcement learning algorithms. 

But I was not like that.

Lack of knowledge about statistics and machine learning algorithms has caused me embarrassment during job interviews and at work. Moreover, I was walking blind, trying various things to come up with a solution without understanding how to improve the current model or process. 

 

Photo by Andrea Piacquadio
 

I just wish that If I worked on the basics, I could have saved a lot of time and embarrassing situations. Furthermore, I could have gotten better jobs early, as companies are always looking for individuals who have a strong grip on deep learning algorithms and architectures. 

 

Lesson: It is hard to learn all of the basics, but you have to start somewhere. So, when you are starting your career, focus on math and programming. Both are essential for career success.

 

3. Knowing your Worth in the Industry

 

Knowing your worth is the most important thing, and It took me longer to understand where I stand. 

Knowing your worth includes two things: 

• Knowing your skills and skill levels.
• Knowing how much your time is worth.

 

Image by Author

 

Knowing your skills and skill level

 

I know, Imposter syndrome is real. It will stop you from jumping on to better opportunities. You will always think that you're not good enough. So, you better start understating how many skills you have and your expertise. To do that, you can take assessment tests and certification exams, work on an open-source project, or participate in competitions. 

 

Knowing how much your time is worth

 

At the start, I was exploited, I was paid $12 per article, and I thought it was industrial standard until I stumbled upon another opportunity that was paying me $100.  

After understanding your skill level, you need to figure out how much your time is worth. You can work with multiple companies, talk to your peers, talk to professionals in the industry, and read blogs to assess your value. 

It took me longer to understand my worth, and I am still learning new skills to improve my current value. The learning process never stops if you want constant growth in your career. 

 

Conclusion

 

I love sharing my journey of becoming a data scientist and how things could have been better if I knew a few things before starting the career. 

The process is hard and repetitive, and it requires your patients. It is not glamorized at all. It is a stressful job where you are always dealing with unique problems. 

You can ease your journey by focusing on one field of study, learning math behind algorithms, and knowing your worth. 

I am sure anyone from any background can learn data science. It’s all about hard work, persistence, and curiosity. Do follow me on socials if you want to learn about data science, machine learning, MLOps, and my journey. 

 
 
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.