How To Become A Full Stack Data Scientist In 2024
Who is a Full Stack Data Scientist and How Do You Become One In 2024?
2024 is here and Data Science still remains the sexiest and among the highest paying jobs.
In 2022 and years before that, Data Science saw a quick spike in growth, especially during the peak of the Covid 19 Pandemic, and many industries have jumped on the power of Data Science to draw the most value to their products.
The advent of OpenAI’s ChatGPT and medical robots using the power of Artificial Intelligence is shifting the hiring and maintenance of employees in many companies. Many tech companies have laid off employees in many departments to be able to accommodate more data science skill personels that can drive innovations and creativity. Microsoft is willing to invest $10 billion in OpenAI as reported by the New York times. Why do you think they are so much interested in investing such a huge money in OpenAI? What is OpenAI doing? using data science to create innovation and creativity.
Many industries hired more people with Data Science and Analytical skills more than any other in any department.
Not only did companies chased Data Scientist but many people also jumped on the trend of becoming a Data Scientist. Some changed their profession entirely from one domain to Data Science domain like one of my students, Evelyn who was a Marketing Manager(salary: $62,710) and now a Data Scientist(salary: $123,444).
People often ask me: is Data Science going to continue to be attractive in 2022 and the up coming years?
The answer is YES!!
And it’s gonna be very seriously in demand. I even dare to say that no matter what department you will be working, whether as a HR manager, marketing personal, sociologist, psychologist, etc. if you don’t have data science or analytics skills you are prone to being laid off easily.
Considering the recent innovations that Data Science through the help of Artificial Intelligence is creating in our society such as self-driving cars, ChatGPT, robust product recommendation systems as seen by Netflix, virtual realities and remote tasking, etc. the need for more people to handle large amount of data will continue to be surging. More and more companies are leveraging their insights from their data to provide better customer service which in turn is spiking their profits.
Taking for instance, e-commerce platforms and market places such as ebay, amazon, meesho uses their recommendation engines to up sell other complimentary products to customers when they buy one item. These makes the companies sell more products and thereby increasing their profits.
Data Scientist who have the required skills to help companies drive the most out of their data highly in demand and are highly paid in companies. For example one of my students, Evans , who worked for an insurance company as a Data Scientist was paid twice higher than his supervisor. Weird right? I couldn’t believe it at first too. But think about it, Evans working as a Data Scientist was mostly what brings in 80% of the profit the company makes, so why won’t they pay him higher? The supervisor is there to see the progress Evans is making, apart from that what?
Although the field of Data Science is very lucrative, there are 1000s of people who know that and are trying to get into the field but few really make it and land a good job with good salary.
In order to be a sought after after Data Scientist in 2022 and beyond, you need to consider becoming a Full Stack Data Scientist.
Who is then a Full Stack Data Scientist?
A Full Stack Data Scientist is someone who knows the “end-to-end” of a data science project. When I say end-to-end, I mean right from getting the data, doing feature engineering, model building and model optimization as well as model deployment.
Most people who try to enter the field of Data Science just learn half way and then struggle to even crack data science interviews. Few who manage to get job offer also get stuck and struggle when faced with real world data science projects.
As a Data Scientist at Microsoft and a mentor who have helped 1000s of students to successfully become Data Scientists through my course, here is my personal advice for you to become a Full Stack Data Scientist.
The learning Phase:
- Get a Mentor(not a must but it helps)
- Master the essential Statistical concepts
- Master ONE programming language(Python recommended)
- Don’t forget your SQL and Excel Skills
- Master Machine Learning
- Master Machine Learning model Deployment
- Master One or Two, not more than that, Data Visualisation tools(Tableau or Power BI will get the job done in most cases)
- Learn Presentation and Storytelling.
The Practice Phase: Most Importantly
9. Get a Data Science Internship (paid or unpaid)
10. Participate in Hackathons (Kaggle recommended)
11. Write about your projects on Medium or any other platform(the more you write, the more you understand the concepts)
12. Make Sure Your Github account has all your projects in order(you will need this during interview stage)
The Final Phase: This phase can come before or after you get a job:
13. Start to master only one area of Data Science and become good at it (e.g, Natural Language Processing(NLP), Computer Vision(CV), etc)
These steps I have given you are exactly what we have been using to achieve tremendous results for our students.
If you are interested in becoming a full stack data scientist, feel free to check out the Full Stack Data Scientist A-Z™ BootCamp, I can also personally guide you there.
Note: Most “good” companies have data science teams where each team member works on different aspects of the data science project. That’s, eventually, you are likely to work on a single aspect of the data science project. For instance here at Microsoft, my team consists of 6 Data Scientist and last two months we started a project from one of our clients, each one of us is doing different thing on this single data science project. Even though we know what the others are doing, each one is an expert in a particular area that we are working on and we know how to do our part to help others complete their part as well. However, in order to get in initially depends on how you express your overall understanding and knowledge of working on a data science project. Additionally, it helps when you know what the beginning and the end goal of a data science project is. It helps you to do your part accordingly.
On the other hand, if you find yourself in a Start-Up that is not well established and does not have a data science team, you will be a Spider Man Data Scientist trying to save the planet. You will do everything from Data Engineering to Cloud Engineering to whatever you can think of, because to them you said you are a data scientist and they have hired you, so get the job done, period!!
That’s why knowing the end-to-end of a data science project is crucial.
Last month I spent good amount of time talking to about 20 Data Scientist from IBM, American Express, Forbes and Fractal Analytics. These experts shared their insights on how to become a Data Scientist which can be found in this book.
Last words.
The field of Data Science is going to be attractive but it takes the Full Stack Data Scientist to reap the benefits.
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