University vs Bootcamps - DSBoost #49
What kind of Data Scientist is in demand for 2024?
Podcast notes 🎙️
In this episode, Ken talked to Immanuel who had a hard journey to Data Science. Everyone can learn from his story, so here are our notes:
Here is Immanuel’s road:
He did a Bootcamp where they sent over the CVs to employees but had no success.
He did some courses online and added the takeaways to his resume.
He applied to companies for almost 2 years (sent 600 applications) and only got 3 responses.
Feeling like he had some gaps in his knowledge, Immanuel decided to hit the books again. He went for a tech MBA. University isn’t just about what you learn; it’s about who you meet. Networking is gold in the tech world.
He did a tech MBA program.
He got an internship and then a full-time role.
Note: Everyone’s journey is different and Bootcamps work for some while it’s a waste of energy and money for others. The biggest takeaway here is to never give up and hard work will pay off.
The is no right or wrong way to DS! You can lend a DS job without an advanced degree, but you will not land a job easily! Hard work is required on all roads.
The University Debate: Worth it or Not?
You will see a lot of people online discrediting universities. Immanuel’s story shows that listening to those people is not always the best strategy.
The biggest advantage of universities is marketability. Getting a job is not about your skills, but how you can present yourself.
Here is a crazy story Ken shares: Sometimes, just being enrolled in a master's program can make your resume shine. Some folks even landed jobs and then left the program. (It's not Ken’s recommended strategy, but it worked for some.)
The other big advantage is that you have access to a great network and you can develop connections that will be beneficial in the future.
They can also give you real-life experience through internships! In some cases, companies directly go to the universities and interview some students for a role and hire maybe 4-5 of them.
The other side is the prices of these programs. The cost can be higher than the reward in some cases, but with scholarships, it can be easier.
💼What kind of Data Scientist is in demand for 2024?
Here are some skills that could be beneficial for a Data Scientist in 2024 according to the previous Reddit post:
Python Skills: Python is one of the most popular programming languages for data science. It’s used for data analysis tasks and has powerful libraries such as pandas, NumPy, and matplotlib for data manipulation, statistical analysis, and data visualization. Python is also dominant in advanced data science subdomains, including machine learning and deep learning.
SQL: Knowing SQL allows you to retrieve, manipulate, and analyze data stored in relational databases.
Data Analysis: Data scientists often need to collect, analyze, evaluate, review, organize, and visualize data. They organize the data and perform statistical calculations to find trends that can solve problems for a client or their employer and inform important business decisions.
Versatility: Data scientists often work on tasks typically done by data engineers, data analysts, or software engineers. Embrace these opportunities to diversify your skill set and increase your value in the job market.
Leadership Skills: Given the nature of their responsibilities, data scientists require a balanced set of technical skills and leadership skills. This includes the ability to communicate effectively, manage projects, and lead teams.
MLOps: 5 years ago, there was more focus on training Machine Learning models and obtaining insights. This is not enough nowadays, companies want the models to be automated and deployed in a pipeline and want Data Scientists to retrain models when a data drift occurs.
Now some additional advice taken from comments based on work experience in the same post:
Career Progression: The demand for data scientists is high. It might seem daunting if you're just starting, but with perseverance and continuous learning, you can aim for these roles.
Entry-Level Competition: Be prepared for stiff competition at the entry-level. Make your application stand out by gaining practical experience wherever possible and showcasing your unique skills and projects.
Versatility: As a data scientist, you might find yourself working on tasks typically done by data engineers, data analysts, or software engineers. Embrace these opportunities to diversify your skill set and increase your value in the job market.
Job Market: Navigating the job market can be challenging, even for those already in the field. Networking, staying updated with industry trends, and continuous learning can be beneficial.
Remember, these are general observations and experiences shared by users on Reddit and may not reflect the entire industry's situation. For the most accurate information, it's recommended to conduct further research or consult with industry professionals.
Networking, staying updated with industry trends, and continuous learning can be beneficial.
Good luck on your data science journey!