FSU GRADUATE SEEKS GUIDANCE ON HOW TO TRANSITION INTO A MORE FOCUSED DATA SCIENCE ROLE

#engagementstory

By MAX LEISTEN

Pivoting in one’s career can be a powerful step toward personal and professional growth. Making a shift allows individuals to build on existing skills while exploring new challenges and opportunities. For those in data analytics, a pivot to data science can open doors to more specialized, high-impact roles where advanced analytics and problem-solving are essential. Transitioning can also boost one’s adaptability, a valuable asset in today’s dynamic job market, while broadening the scope of potential career paths.

Seeking mentorship during this time can be particularly beneficial, as it provides guidance on skills development, networking, and navigating industry-specific demands. This approach not only supports a smoother transition but also fosters connections that could lead to valuable opportunities and a more fulfilling career.

Keep it simple,

Max

The "ask"

I am a 2022 graduate of FSU’s College of Business. I am currently working in HRIS data analytics and am interested in pivoting to data science. My current skillset includes Oracle Cloud Analytics, SQL, Excel, and a SAS certificate from FSU. I am seeking mentorship and guidance on how to effectively transition into a more focused data science role. I would appreciate any advice on how to develop my skills, network with professionals in these fields, and identify potential career opportunities.

Thank you very much for your time and assistance.

The "give"

Dear graduate,

I hope this message finds you well. I’m thrilled to hear about your interest in pursuing a career in data science! It’s a dynamic and rewarding field that blends curiosity, creativity, and analytical thinking.

To help you on this journey, I’d like to share some key pieces of advice that I’ve found invaluable:

*Ensure a strong foundation in mathematics (statistics, linear algebra, calculus) and programming (Python). These are the building blocks for success in data science.

*Familiarize yourself with tools for data wrangling and cleaning(Like Exploratory Data Analysis(EDA)). Real-world data is often messy, and your ability to make sense of it will set you apart.

*Dive into machine learning algorithms and frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). Also, focus on learning cloud learning environments (Azure, databricks, AWS, GoogleCloud)

*Apply your skills to real-world problems, whether through personal projects, internships, or competitions on platforms like Kaggle.

Remember, the journey to becoming a data scientist is a marathon, not a sprint. Celebrate your progress, embrace challenges, and never hesitate to ask for help along the way.

If you ever need further advice or resources, feel free to reach out—I’d be more than happy to assist.

Wishing you the best of luck as you embark on this exciting path!

All best,

Data Scientist

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