Data science is a growing field, with thousands of job openings and a shortage of qualified professionals. With a median annual salary of $98,000, it’s well worth the effort to learn the core skills and competencies required for this complex role.
As a starting point, we put together a list of fundamental skills data scientists need to remain competitive and relevant in this constantly evolving industry.
A Techradar top-rated online learning provider
Udemy is a business oriented video streaming education platform, with over 185,000 videos for professional development. Click here to get started.
1. Communication and presentation skills
When we think of data science, skills like programming and model accuracy development come to mind. However, if you want to pursue a career in data science, these core skills won’t put you ahead of the curve. They will simply put you on it. Every data science bootcamp graduate should know the techniques that maximize accuracy on a model, and will have a solid grasp of several programming languages. The scientist who knows how to present their resultant findings in layman's terms, to a management team with little or no technical knowledge, is the one that stands out from the pack.
Consider the development of presentation skills a solid, essential, and permanent investment in yourself. The programming language you recently learned could be obsolete within the next 10 years. On the other hand, the ability to wow management and clients with your excellent communication skills will be just as valuable as it is now.
2. Programming skills
In terms of technical skills, programming is your bread and butter as a data scientist. Although the specific language(s) you use will vary by organization, here’s a shortlist you should be comfortable with:
This list isn’t exhaustive. As the industry continues to evolve, the prevailing programming languages will also change.
In addition to general-purpose programming languages, you should familiarize yourself with more specialized tools of the trade, such as TensorFlow and MATLAB. Be sure to stay up to date with the most relevant tools in your field. If someone introduces a new tool or technique, or a particular language falls out of favor, try to keep up with the trends. Doing this will keep your skill set relevant for years in the rapidly evolving data science industry. This brings us to the next important facet, which is …
3. Continuous learning
Despite explosive growth in the data science industry, experts predict that in the next 10 years, all the current professional roles in this field will no longer exist. This doesn’t mean all current data scientists will lose their jobs. Instead, the fast pace of advancing technology and innovation in the sector will mean that the roles of existing data scientists will entirely change.
It’s essential to update your skills constantly to remain valuable to your organization in the face of such fast-paced innovation. Take the time to learn new skills online, subscribe to the latest journals in the fields of AI, machine learning, and data science, and make a habit of attending industry networking events to stay ahead of the curve.
If you’re not sure where to begin, consider your career path. If you’re looking to break into the field, brush up on SQL, data analysis, and data visualization. If it’s not your primary area of expertise already, learn Python, as it’s one of the most commonly used programming languages in data science.
As an established data scientist, consider your career progression. If you want to move into a management role, find people management courses, job shadow, and attend networking events to expand your skill set. If you wish to remain in a technical role but perhaps move from generalist to specialist, adopt technical and soft skills in your particular field.
4. Focus on real-world applications
How will this model be used in the real world? As a data scientist, this should always be the first question before creating a model or modifying an existing one. Regardless of the quality of its design, a model is unlikely to be accurate if the data sets used for training it don’t match the real-world situation it faces on deployment.
A 2018 paper on this subject, Datasheets for Datasets, provides a set of questions you can ask to help vet your data set for accuracy and usefulness before using it to train your model. Make use of resources like this to help you and your team stay focused on real-world goals throughout the process of gathering data, creating a model and training it. That way, you can catch issues that might deviate from the real-world scenario as, or even before, they come up.
Data science is a vast and complicated field. Remaining useful or relevant to an organization while being a jack of all trades is nearly impossible. Try to find a specialty that genuinely interests you and become an expert in that area. This could be something technical, such as an aspect of machine learning or data mining, or an industry specialization like finance or retail.
Consider this: A recruiter for the data science division of a major bank is more likely to pick an expert in financial modeling over a generalist, regardless of how technically knowledgeable they may be. It could even mean the difference between having your application dismissed, or landing the crucial interview.
To develop your skills as a data scientist, the key takeaway is to always be learning. As a data scientist, you exist in a rapidly evolving industry. Online courses such as the Complete Data Science Bootcamp from Udemy are a great way to brush up your skills. Keeping up with the latest trends, such as how visual programming powers complex data science, will also help you get ahead. Find some particular area of data science you love, and learn everything you can about it.