There are many people from a variety of fields and backgrounds wanting to dive deep into the data science realm. This may be due to interest in the area, the excitement of something new, or the draw for a more lucrative position to work in. Part of the beauty of the data science industry is working with people who have a variety of strengths and backgrounds to allowing people to use each other's strengths for optimal results.
One of the issues that sometimes arises is some people think data science is just learning how to program cool machine learning algorithms and having the computer tell people what to do. Instead, there a variety of facets that go into making data science the truly interesting and insightful field it is. One key area of data science that often gets left behind is understanding mathematics.
Data science professionals do not need a fancy mathematics PhD to succeed in the field, but having a good base understanding of mathematics is key to really flourishing in the role. This good foundation of mathematics usually encompasses an understanding of algebra, calculus, probability, statistics, and linear algebra. Generally a surface level understanding of these areas is the minimum needed, but diving deeper into the applied and pure side of the mathematics never hurts to deepen one's understanding.
One of the main reasons understanding mathematics is important is being able to fully interpret the data being analyzed. This way the data scientist is using the analysis tools whether it be descriptive statistics, visualizations, or machine learning algorithms to provide them the information they need versus having the tools tell them what to do. Then the data scientist takes time to analyze what the computer says the results are and assemble an actionable analysis to present and/or implement. For example, many people can learn how to program an SVM machine learning algorithm using something like sklearn in Python, but not everyone fully understands how the computer is structuring the algorithm, what equations it uses, and how it gets to the results. This deeper understanding will improve both comprehension of the data scientist with what the data is showing along with providing them better explanations for their audience.
Another reason understanding the mathematics is important is when it comes to building the analysis. If the data scientist understands the mathematics, then it is much easier for them to spot mistakes or odd looking figures when building their analysis versus just assuming the computer is correct. It also provides them an array of tools and tests they can utilize that might not be as obvious for the machine learning tutorials such as formatting the data with normal distribution formulas, looking at measures other than accuracy such as precision, or utilizing different measures of central tendency or spread. These are just a few of the reasons of why having a good understanding of mathematics is so useful when it comes to being a data scientist and providing insightful outcomes to projects.