Technical Skills for a Career in Data Science

Once the innovation skills have been mastered, there are some fundamental technical skills that will be invaluable for data science professionals. These skills are essential for facing technical challenges and offering effective solutions in the industry.

Assigning Tools to Problems

Once a specific area for improvement has been identified and the commitment of those involved has been secured, the fundamentally technical phase begins, starting with the simple question: what are the best tools for the job? This is not a trivial question. Matching the right tool or set of tools to your problem requires a global knowledge of the uses and limitations of various techniques, as well as whether the organization has the personnel, resources, or willingness to pursue each path.

Fixing Dirty Data

It is true that there are programs that claim to solve this problem, but in many cases, the ability to locate and correct corrupt or imperfect data is one of the most important and time-consuming skills in the field of data science. Some surveys suggest that data scientists can spend up to 80% of their time on this task. This skill is highly important in smaller companies, where incorrect data can significantly affect their results. This skill set can include locating and replacing missing values, correcting format inconsistencies, and changing timestamps.

Good Understanding of Basic Statistics, Calculus, and Algebra

To accurately understand and use most data science tools, some fundamental skills in statistics are needed. For example, data scientists need to be familiar with maximum likelihood estimators, statistical tests, distributions, calculus, and algebra. It may seem strange that a data science professional needs to know how to perform calculations and algebra, since many of the programs and software used today can do all of that and more. However, new businesses, whose products are defined by data, may require small continuous advances to remain competitive.

Data Visualization Skills

This subset of skills is of particular importance for companies that are just learning to make data-driven decisions. Narrative visualizations are an accessible way to tell the story of data and establish a foundation from which future predictions can be employed.

Conclusion

Technical skills are crucial for any professional in the field of data science. These skills not only make one more competent in handling technical problems, but also provide the ability to adapt and grow in a field that is constantly evolving.