Breaking into data science is a long and winding road that requires technical and soft skills and a lot of resilience. We spoke to renowned industry experts and well-liked names in the data science community to discover how and when they knew they had what it takes for a career in the field. They shared their stories and the qualities they developed to succeed.
Interview with George Firican
Would you please tell us a bit about your background and journey into data?
I've been working with data all my professional life. Even though I started my journey as a software and web developer, I handled data to build products like financial software and e-commerce and membership sites that all had databases to work with. But I became more aware of the importance of data quality, data governance, and data management when I transitioned to roles as a business analyst and project manager. Only then did I understand what happens when data is not treated as an asset, which increased my interest in the field.
I'm now a seasoned data governance and business intelligence professional, currently ranked among the Top 5 Global Thought Leaders and Influencers on Digital Disruption and Big Data and the Top 15 on Innovation. I had the pleasure of working with excellent teams receiving international recognition through award-winning programs and project implementations in data governance, data quality, business intelligence, and data analytics.
Unsurprisingly, two of my passions are to help organizations from the data industry become more brand aware and create informative and practical content, such as my online courses on data governance and data management.
I’m passionate about data, often speaking at conferences and events. As the founder of LightsOnData, I advise organizations on how to treat data as an asset and share practical takeaways on my LinkedIn profile, YouTube channel, through the Lights On Data Show podcast I co-host, and various publications and industry sites, such as 365 Data Science.
When did you know you have what it takes?
It’s important to note that I'm constantly learning and growing in my role as a professional, so I don't necessarily see my data science skills and abilities as something that are ever fully developed. But I did have a moment early on in my career that gave me confidence and reassurance that I was on the right track.
I worked for a large organization to establish a data governance program from the ground up. This involved working with stakeholders across different departments to understand their needs and ensure that the company's data was managed securely and compliantly, aligning with business objectives.
As I put together this program, I saw the positive impact of our team's efforts on the organization. We established a data culture that improved data quality, reduced risks, and ultimately helped the company make better-informed decisions.
Seeing the success of this project and the value that data governance brought to an organization gave me confidence in my abilities as a data science professional. But I also recognize that this is a constantly evolving field; there’s always more to learn and new challenges to overcome.
As a data professional, one needs strong analytical thinking and a lot of technical know-how. What skills did you start with, and how did you develop them to meet the role requirements?
It all started with my bachelor’s in computer science and a master’s in management. These degrees gave me the data science fundamentals I could apply to various roles. But there was nothing like stepping into the field and working firsthand to solve specific business and technical problems. Having support from my direct managers helped immensely as they encouraged me to learn from others, attend industry conferences and seminars, join online communities, network with other professionals, and learn from their experiences and approaches to problem-solving. I was also very fortunate to work with incredible, talented, and skilled colleagues I could always learn from.
Being able to communicate insights to others effectively is an essential aspect of a data scientist’s soft skills. Practice presenting concisely—don't be shy to record yourself. Work on translating complex technical concepts into understandable language for non-technical stakeholders. This is an important asset to have.
Learning is a lifelong process—especially in a field that’s as dynamic as data science. Is there something new you’d like to learn more about or become better specialized in?
I'm always looking to learn more about emerging trends and technologies in the field. One area that I'm particularly interested in is machine learning, specifically deep learning, as I believe it will become crucial for solving complex business problems and developing more advanced models.
I'm looking forward to how it could revolutionize change management and data management through data refining, natural language processing, emotional intelligence, and more.
Overall, being up-to-date with the latest trends and technologies is essential for any data professional looking to stay ahead of the curve and continue to deliver value to their organization.
As an expert in the field, you must have an ample idea of the qualities a person needs for data science. What would you say those are?
Data science is a vast field, and the qualities a person needs can differ according to the role’s specifications.
But overall, a successful data scientist should possess a combination of technical and soft skills, have a curious and creative mindset, desire to tackle challenges from multiple angles and with different teams, and have the ability to communicate complex technical concepts effectively to non-technical audiences.
As the industry develops, the demand for skilled data scientists grows exponentially. What gives an aspiring data professional a competitive edge in 2023?
In my opinion, there are three key areas that an aspiring data professional should focus on to gain a competitive edge in 2023:
- Technical skills
- Soft skills
- Business acumen
Firstly, technical data science skills are crucial. Any data professional must have a solid foundation in statistics, programming languages like Python or R, and knowledge of various data analysis tools and techniques. To demonstrate these skills, I recommend having an online portfolio that goes over the business problem and what skills were applied to solve it.
Secondly, soft skills like communication, collaboration, and adaptability are critical for data science. The ability to work effectively in cross-functional teams and quickly adapt to changes in the business landscape and technologies is vital in today's fast-paced data-driven environment.
Finally, business acumen continues to be increasingly important. Organizations highly value the ability to understand and communicate business problems while understanding data management and data governance roles will enable a better data program overall. Job shadowing, volunteering, training, or being embedded in a specific business unit can help.
By focusing on these three areas, aspiring data professionals can set themselves apart from the competition and position themselves for success in 2023 and beyond.
We want to thank George for taking the time to provide valuable insights into what it takes to be a data scientist. Check out his website, LightsOnData, and his other channels where he shares resources and advice on how to be successful in the field.
Our aim at 365 is to equip you with the tools and give you the necessary guidance to succeed. Designed never to let you give up, the 365 Data Science platform has what you need to develop your qualities and skills.
Go from a beginner to a skilled professional who creates data-driven value. Study the statistics and probability theory behind data science with online courses led by our renowned instructors, and master sought-after skills like Excel, SQL, and Tableau. Prove you’ve got what it takes with industry-recognized certificates.