In general, folks with technical background, rightfully, focus solely on the technology aspect of the application or tool development, whereas business leaders are more concerned about the business value that can be generated by deploying the tool.

Certifications are an excellent way to showcase your talent. It also works as a motivator to stretch yourselves a little bit into unchartered territory and expand your knowledge base.

On the flip side, few certifications and courses, especially in the field of Data Science are unreasonably expensive and unbound or shallow in scope.

For an organization to meet the demands of the future, it is critical to facilitate a rapid adoption of data science into the delivery pipeline.

People involved in the delivery chain will need a shift of mindset, the building Process must integrate data science into the project plan and the final Product will have to adopt the innovative toolset by moving to the cloud.

Project management approaches vary from predictive techniques on one end to empirical iterative practices on the other.

Standard practices are geared towards application development with stakeholder participation. These techniques either Waterfall or Agile do not fit well with data-driven projects (AI, ML, Data Science).

For data-driven projects, it is the learning from data analysis & ML models that have to be infused into the application, therefore need a different approach.

Each project management technique has a valid use case.