Data science has become a complex field that is understood differently by different groups of people, which can lead to confusion and misunderstandings. Here are key myths to remember to help understand data science more clearly. 


  1. Data Scientists are Engineers

While a data scientist can be an engineer, many of them are not and instead have experience in Statistics, Programming or Machine Learning. Data scientists work on business problems and arrive at custom solutions for clients. They don’t necessarily work on building networks or applications, although many data scientists have knowledge of SQL and maintaining databases. 

It’s also a myth that all data scientists are data analysts, which are professionals who interpret business metrics. Data analysts have been around much longer than data scientists. 


  1. Deep Learning and Machine Learning are the Same

Deep learning may sound like it has something to do with machine learning but these processes are very different. Machine learning is based more on algorithms and information theory. It’s useful for computer vision and speech recognition but should not be overused for prediction problems. Deep learning is best understood as a subset of machine learning, which is a subset of artificial intelligence. Data science is a field for developing expertise in constructing predictive models for businesses. 

Today’s data scientists should understand which metrics and formulas are appropriate for certain situations.


  1. Data Science Can Be Learned Quickly

Becoming a successful data scientist takes plenty of training. Writing a proper sequence of steps for creating a model is important in coding, which cannot be learned overnight. In order to be an effective data scientist, a person needs to learn how to use certain tools for measuring business performance. But learning specific tools alone does not make an individual a data scientist. They must learn how analytics are applied to specific situations. 


Data scientists are typically not hired based on their expertise of specific tools. They are usually hired for a combination of mathematical, programming and business skills. Gaining certification at the SAS Institute, which signals credibility in understanding and applying analytics, can be challenging. Even though data science is involved with automation there will still be a need in the future for data scientists to program and monitor machines.