While the world today is floating in data and organizations from across the world are looking to leverage its full benefits, the demand for skilled professionals who can do the job well is exceptionally high. Industries, particularly data science and artificial intelligence, are leading this transformation. Employment in both these industries is expected to grow by 30% and 32% respectively by 2030 (according to US Bureau of Labor Statistics). Not only that, but also the data science jobs, and AI jobs have been ranked the fifth and first fastest-growing jobs respectively by WEF employment report 2023.
But what if you have already worked in a different field for quite some time now, maybe in your 30s or 40s? Do you still have the chance to grow in this ever-evolving field of data science and AI? Well, there’s good news. There are several ways to advance your career in data science.
Why change to data science career?
Before we tell you how you can switch your career to this domain, you must know why. Data science and AI is undoubtedly a glamourous and attractive field of work. According to GrandView Research, global AI adoption is expected to grow at a CAGR of 37.3% between 2023 and 2030.
This number indicates how rapidly organizations across industries and around the globe are actively integrating data science and AI technology into their business operations. And hence, the demand for skilled data science professionals is going to soar. This field is also expected to be the largest occupations providing industry as well.
Additionally, if we talk about salaries, then professionals in these fields get huge salary packages globally. According to Glassdoor, the annual average salary (in 2024) of Data Scientists in the US is $156,627 and AI engineers get $130386. These reasons are enough to get you tempted towards this career path.
Taking advantage of the experience
Now, when you are determined to change your career to data science and AI, the first thing you should look for is taking advantage of your experience. Career transition can feel overwhelming, but experience can prove to be very valuable in such cases.
So, identify your transferrable skills. You can use your current skill sets like problem-solving, analytical thinking, communication skills, and others for your career in data science. Highlight these in your resume and back them up with relevant stories during interviews.
Next, domain expertise is highly important in data science. If you have industry-specific knowledge, you can use it to understand business problems clearly and find insights relevant to the problem statement you are looking to solve.
Upskilling for Data Science and AI
This is the most exciting part. When you are transitioning career, you don’t often have all the skills needed to grow in the desired career path. For a rewarding career in data science, you must be good at the following technical skills that you must consider mastering:
1. Programming languages
This is the must have data science skill which is needed to perform data wrangling, manipulations, analysis and visualization. Python is the most popular and widely used programming language. You can learn and master its fundamentals including popular libraries like Pandas and NumPy. R is another great option to consider.
2. Statistics and mathematics
They are considered the backbone for data science and AI as they are used for data analysis and building machine learning algorithms. So, brush up on your foundational skills in probability, statistics, linear algebra, and other mathematical concepts.
3. Machine learning
You should have a thorough understanding of machine learning concepts including supervised learning, unsupervised learning, and some other concepts like machine learning algorithms consisting of decisions trees and random forests. These are essential for all data science and AI models.
4. Data wrangling
Data collected by organizations from various sources are raw and often not structured. As a data science professional, you will need to clean and pre-process them for analysis. There are various tools and Python libraries available to assist you in the task.
5. Data visualization
Data visualization is another important component of data science. This refers to transforming complex insights into easy-to-understand visuals. So, it ensures the insights gained from data analysis and findings are presented in beautiful visuals and dashboards so that even non-technical stakeholders can understand it properly.
How to learn these skills?
There are several resources to help you learn the fundamentals of data science and AI. You can enroll in top data science certifications for beginners that will empower you with all the knowledge and data science skills to start your career in data science.
There are data science courses both online and offline to understand the concepts of data science. You can find data science programs on edX, Coursera, Udemy, and other online learning platforms. Reputed institutes like Harvard, USAII®, and MIT, also offer online data science certifications and courses that can be beneficial for your career.
Additional Steps
· Gain practical experience and build a portfolio of practical projects to showcase your employers
· Kaggle competitions, personal projects, contributing to open-source projects are some ways you can hone your practical data science skills
· Network with other data science professionals via LinkedIn, and other data science forums.
· Attend data science meetups and conferences
· Customize your resume according to the job you are applying for.
Conclusion
Remember, data science is a growing field and has opportunities for everyone. Transitioning to this career path may present an age concern if you are in your late 30s and 40s, but they are not a disadvantage. Focus on your career goals, prepare yourself, upgrade yourself with the latest skills, validate your knowledge and expertise with top data science certifications, network, and start job hunting. You will get the right opportunity to succeed in this highly rewarding career path.
