The Path to Data Scientist role is paved with rewarding experiences
Data Science is a team sport and there are different roles and different naming associated with this field (i.e. Data Analyst, Data Engineer, Data Architect, Business Analyst, Citizen Data Scientist, ML Researcher etc.). If you are interested in this area, it is important to start the journey and find what fits you better on top of your current strengths.
Experience#1: Initial load
Understanding the Data Science concepts, such as area of coverage, market trends and tools, benefits, why everybody is talking about it and other similar aspects is the first step to be done. Considering the complexity of this field, it is a challenging move, but in the same time a rewarding one once you discover the beauty and the potential of this promising paradigm.
There are different resources to be used, both virual and real life experiences, such as keynotes and speeches from conferences, university classes, tutorials, blogs, social media.
Experience#2: The power of communities
After taking the time to understand and learn the basics, the next level is to identify the right communities, to be empowered by being part of those communities and actively contributing to them. You can find different type of communities: Meetups on Data Science, the internal ML community at your job, a HQ or regional team if you are part of a multinational company. You'll discover that the others have similar projects and challenges and you can inspire each-others and one's deliverable and work can represent a quick win for somebody else.
Experience#3: Try the "strong essence fragrance"
Reading research papers will bring you to the next level. There are several research conferences covering various Data Science topics or having focus on different industries. In the last years interest towards these conferences increased significantly both in number of researchers and also the participants. Recordings from such conferences are available and can be easily accessed by interested parties.
By reading the papers and listening the related talks you'll understand the importance of the discovery in Data Science nowadays and how this will be applied shortly in real life.
Experience#4: Be THE expert in a specific area
Identify one area of your choice within Data Science and overspecialize in it. For example, it can be one class of algorithms, how to define the business problem in the right way or a specific process in ML life-cycle. Once identified, learn everything you can about it and then practice, practice, practice. You can do it as a member of a team working on Data Science projects or participating in online competitions on topics related to the field. Practice is the best way of learning by doing and thus gaining the expertise.
Experience#5: It’s time to give back
When you feel comfortable with the level of your Data Science knowledge it's time to give back to others. You can do this towards your job colleagues, you can contribute back to the communities which inspired you, be a speaker in a meetup event or at regional level in your organization. You can also share your knowledge by publishing articles on blogs and social media. And why not, to share your knowledge towards academic bodies or during national and international conferences on the matter. If you feel you have a great and innovative idea, a research conference and a paper work is the way to go.
Experience#6: Stay tuned
Data Science is a very fast changing field, thus you have to stay tuned. Again we can say it's like in sport and you execute regular training by being connected to communities, reading about latest innovations, learning from others and sharing with others. At the end of the day, this effort will pay back as you really feel you are part of the change and your contribution matters.