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One of the reasons why business data analysts are so highly sought after is their ability to harmonize between the two worlds of interpreting complex information at mass scale and their consequences, use, and meaning to profit, efficiency, supply chains, and more. One fascinating thing about the implementation of these tools and techniques is the blend between centuries of mathematics and statistics with some of the newest concepts and research. And one of the first steps to understanding both, is to understand the various stages in the data science lifecycle, and the methods and tools implemented at each.  

Data Analysis Methods and Tools

Data science projects pass through various stages, beginning with the initial collection and extraction of data to storage, cleaning, refining, visualization, analysis and implementation. These stages each have their own unique techniques, implemented through various data tools, many of which didn’t exist a decade ago. Understanding each of these tools (the specific software used for analyzing certain data needs), techniques (the operations of those tools), and the right time to get the most out of their use, is crucial to a data analyst’s job. 

A few of the subject areas to apply these tools and techniques include a required understanding of:  

• Probability and Statistics
• Distribution
• Regression analysis
• Hypothesis testing
• Linear Regression
• Logistic Regression
• Neural Networks
• Decision Trees
• And so much more.

One example of some of them at work include, for example, Amazon, which employs many techniques including the above to optimize their supply chains and keep everything running smoothly. For example, supply chain optimization uses probability and statistics, decision trees, distribution and so much more to optimize sites for warehouses to minimize distribution costs, to select routes, find the most economical gas purchases for delivery trucks and minimize delivery time. They also use  inventory forecasting to determine how much inventory of each product they should have in stock at a particular warehouse. 

In addition to core business courses like finance, accounting, and business law, Saint Vincent business data analytics majors take data skills and technology courses starting with Introduction to Python and diving deeper into methods courses like DS300 Methods of Data Science and Analytics, DS350 Data Mining, and EC360+361 Econometrics with R lab, and in-depth statistics courses. 

Even with all of these tools, the greatest skillset of a business data analyst is knowing what the information problems that need solved actually are, what tools and techniques to employ to best solve them, and how to interpret the results in meaningful ways that lead to organizational change.