4 Ways to Tell That You Need to Upgrade Your Big Data Skills


The fundamentals of data science never change. Statistics, probability and a basic knowledge of Python or R coding are typical requirements for a data scientist. But as technologies improve and user demands grow in complexity, data scientists find that a big data course or courses keep their skills sharp.

How do you know if you need to sharpen your skill set? If you can check yes to these four statements, then you just might want to take a big data course or two.

1. Insufficient Data Confuses You

Data might be big, but that doesn’t mean it holds all of the information needed to answer a particular question. Skilled data analysts make meaningful interpretations from incomplete data sets. Skills such as R and Python fluency produce accurate results despite a paucity of data as does an understanding of clustering, optimization, regression and other statistical maneuvers that allow data scientists to identify patterns and construct predictions.

2. You Lack a Cross-Section of Skills

Companies now expect data scientists to interact with users as they move from the ideation stage to the creation stage to the implementation stage to the interpretive stage of a data project. This means that data professionals who are aware of industry-specific processes, whether financial, medical or even retail, are better able to meet users’ end-to-end needs.

3. You Have Never Prepared Data for Artificial Intelligence Scenarios

If you are a programmer with expertise in a specific industry, you might want to upskill your resume with a solid understanding of AI. Companies are leveraging their data stores to provide customers with chatbots, recommendations and streamlined, automated services. These services yield a positive return on investment, making AI development an in-demand skill. Knowledge of software engineering, data preparation and statistical methodology enables a data science to optimize data and implement AI solutions.

4. You Have Difficulty Explaining Your Processes

Developing a useful model is the end goal of pretty much every data scientist. But before a data professional can get to the nuts and bolts of a model they have to get buy-in from stakeholders and managers. They also need to work alongside business professionals and tease out the questions that will yield the kinds of answers C-suite executives need. To do this, data scientists must learn how to explain their processes in a manner that non-data people can understand and appreciate. A course in public speaking, narrative writing or business communication can help you brush up on your soft skills.

Programming expertise is vital, but so are transferable skills, like the creation of visual data displays and data-driven narratives. Courses in programming, business, statistics and even graphic design will bolster your skill set and increase your marketability.