Want to be a data scientist? Here are 5 fundamental skills to master

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 learning the basic skills and competencies required for this complex role.

As a starting point, we’ve compiled a list of fundamental skills data scientists need to stay competitive and relevant in this ever-changing industry.

1. Communication and presentation skills

When we think of data science, skills like programming and developing model accuracy come to mind. However, if you want to pursue a career in data science, these basic skills won’t give you a leg up. They will just put you on it. Every graduate of a data science bootcamp should know the techniques that maximize model accuracy and master multiple programming languages. The scientist who knows how to present their findings in simple terms, to a management team with little or no technical knowledge, is the one who stands out from the pack.

Think of developing presentation skills as a solid, essential and lifelong investment in yourself. The programming language you recently learned could be obsolete within the next 10 years. On the other hand, the ability to impress 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 daily bread as a data scientist. Although the specific language(s) you use will vary by organization, here is a shortlist that you should be comfortable with:

  • Python
  • R
  • SQL
  • C/C++
  • Scala
  • Julia.

This list is not exhaustive. As the industry continues to evolve, the dominant programming languages ​​will also change.

In addition to general-purpose programming languages, you should be familiar with more specialized off-the-shelf tools, such as TensorFlow and MATLAB. Make sure you stay up to date with the most relevant tools in your field. If someone introduces a new tool or technique, or if a particular language falls out of favor, try to follow the trends. This will allow you to keep your skills relevant for years in the fast-paced data science industry. This brings us to the next important facet, which is…

3. Continuous learning

Despite the explosive growth of the data science industry, experts predict that within the next 10 years, all current professional roles in this field will no longer exist. This does not mean that all current data scientists will lose their jobs. Instead, the rapid pace of technological advancement and innovation in the sector will mean that the roles of existing data scientists will change entirely.

Constantly updating your skills is essential to remain valuable to your organization in the face of such rapid innovation. Take the time to learn new skills online, subscribe to the latest journals in AI, machine learning, and data science, and make a habit of attending industry events. industry networking to stay ahead.

If you’re not sure where to start, think about 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 already your primary area of ​​expertise, learn Python, as it’s one of the most commonly used programming languages ​​in data science.

As a senior data scientist, consider your career progression. If you want to move into a leadership role, find people management courses, job shadowing, and attend networking events to broaden your skills. If you want to stay in a technical role but perhaps transition from generalist to specialist, adopt both 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. No matter how well designed, a model is unlikely to be accurate if the datasets used to train it do not match the actual situation it faces during deployment.

A 2018 article on this subject, Datasheets for datasets (opens in a new tab), provides a series of questions you can ask to check the accuracy and usefulness of your dataset before using it to train your model. Use resources like this to help you and your team stay focused on actionable goals throughout the process of collecting data, creating a model, and training it. This way, you can catch issues that might deviate from the actual scenario as they arise.

5. Specialization

Data science is a large and complex field. Staying useful or relevant to an organization while being a jack-of-all-trades is nearly impossible. Try to find a specialty that really interests you and become an expert in that field. It could be something technical, like some aspect of machine learning or data mining, or an industrial specialization like finance or retail.

Consider this: a recruiter for the data science division of a large bank is more likely to choose a financial modeling expert over a generalist, regardless of their technical knowledge. It could even be the difference between your application being rejected or getting the crucial interview.


To develop your skills as a data scientist, the key to remember is to always learn. As a data scientist, you operate in a rapidly changing industry. Online courses like Comprehensive data science training (opens in a new tab) of Udemy (opens in a new tab) are a great way to hone your skills. Follow the latest trends, such as how visual programming powers complex data science (opens in a new tab), will also help you move forward. Find a particular area of ​​data science that you love and learn all you can about it.

Sean N. Ayres