Data science jobs are popping up in all kinds of fields, from retail to government to environmental organizations are all starting to value their data. So how should you decide where to start? If I were to start over again in data science here is what I would do differently.

I would prioritize inputs from my family:

If I had the opportunity to make some of the decisions again I would prioritize family over career more often.

I would get clear on what my interests in data science are:

I would seriously investigate what I enjoy doing and what I can tolerate. The work that you are doing should hold meaning and value to you, if not, then you are just going to collect a paycheck and not end up happy or satisfied with your role. I would list all of my interests and where I want to make an impact with my work. Many of my clients list their interests in making the world a better place. 

I would know what benefits are important to me:

Health benefits, bonuses and salaries are all important when deciding if a position is right for you. But, when there is more than one job in the running, knowing how the benefits weigh against each other is going to make comparing the roles much easier. 

I would identify the type of work environment that I thrive in:

Personally, I enjoy the freedom of working remotely. I like having my own space and being able to control my surroundings, but for someone who gets distracted easily and enjoys the structure of an office then working remotely would not be a good fit. Knowing where you thrive is going to play a big role in finding a job that is right for you.

I would dedicate more time to hands-on experience from day one:

Early on, I would dedicate more time to hands-on projects. Instead of just absorbing theoretical knowledge, actively applying concepts through real-world projects accelerates the learning process. Kaggle competitions, personal projects, and contributing to open-source initiatives are excellent ways to gain practical experience and build a portfolio.

I would focus on fundamentals:

One of the first adjustments I would make is to prioritize a deep understanding of the fundamentals. While it's tempting to dive headfirst into the latest tools and technologies, a solid grasp of statistics, mathematics, and the underlying principles of machine learning is indispensable. Building a strong foundation ensures a more comprehensive and adaptable skill set.

Starting afresh in data science offers a chance to approach the journey with a more informed and strategic mindset. By emphasizing fundamentals, gaining hands-on experience, networking, managing time effectively, committing to continuous learning, building a diverse skill set, and honing soft skills, one can set a solid foundation for a successful and fulfilling career in data science.