“Job Qualifications: Analytical proficiency. Able to process large amounts of quantitative data. Comfortable with statistical analysis and data visualization.”
Sorry, the “Proficient in Microsoft Excel” listed on your resume is just not going to cut it.
Jobs that are geared for business students are increasingly demanding applicants to have strong analytical skills and a data-geared mindset. Many roles that business people play are being simplified, automated, or improved by machine learning and analytical processes.
The Increasingly Data-Driven Business Skillset
Finance positions are becoming more quantitative and even automated with machine learning. Great marketers (and especially analysts) understand how their customers are segmented and how to identify customer needs with data. Data science is even pioneering development of historically qualitative and behavioral fields like human resources. Even as a manager, salesperson, or consultant, it is increasingly important to be able to communicate with the growing number of data analysts, software engineers and “numbers people” that you will encounter.
Although you might not be the one doing the data analyses, it is important to understand what data is needed for business decisions, and lead teams that include data scientists. You need to be able to speak intelligently about what does and does not make sense for specific types of business decisions and analyses.
Haas’s Evolving Curriculum
Haas and Berkeley in general continue to develop the curriculum to prepare students for this ever-changing business environment.
The core Haas analytics course, UGBA104, attempts to ingrain students with an analytical way of thinking and a set of tools to help them make better decisions across multiple business disciplines. This course, over time, has itself been increasingly changing, adding discussion topics and changing teaching methods to accommodate additional material. The problem with only teaching an analytics class like that is applicable across multiple business verticals is that there is too much information to teach in one class.
New courses for undergraduates looking to gain more analytical experience are being added, such as UGBA147: Business Analytics, which is offered next Spring. According to UGBA104 Professor Thomas Lee, there are also plans to offer an undergraduate marketing analytics course, which is currently only taught at the MBA level.
Innovative Courses Outside Haas
Students looking to gain even more experience can look outside of Haas. The Industrial Engineering and Operations Research department, offers many interesting data science and entrepreneurship courses, such as “Machine Learning and Data Analytics,” which I am taking this semester. Although it is a bit tough and not specifically geared for business, I thoroughly enjoy the course. Similar courses can be found in the statistics department. A new Data Science major is also planned to be available within the year as well, for interested students. Introductory courses, such as Data 8 and DS100 are already available.
The problem is that much of the material is not directly related to what the average business student will need to know how to do. Methods learned in these classes have countless applications in business, but require students to make the connection or do tangential readings. Moreover, Haas graduates are not typically going to be the ones building complex models. What is important is that students understand the vocabulary, opportunities, and limitations of what is possible in data.
You can find more business-related courses taught on websites such as Coursera, which offers courses from universities and organizations from around the world. For example, you can take a “Strategic Business Analytics” course, which is offered from Accenture and ESSEC Business School. The website, as well as numerous others on the internet also offers courses that teach specific tools like SQL and Tableau, which are widely used by companies today.
Data and business go hand in hand. The purpose of a business is to solve problems. Data is essential in identifying problems, prioritizing among problems, creating solutions, and evaluate decisions. When you can understand data, you can make quicker, and more confident decisions. There will always be an increasing demand for those who can interpret, analyze and communicate data in an effective manner. Do not become obsolete!
On a final note, it is probably best to not restrict the need for understanding data to just business decisions. The future of humanity is a very complex mix of mind and machine, of which we are just starting to explore. An ability to look at different situations through a highly analytical lens is an increasingly important way of thinking. Being students at Berkeley, we are in such an opportune position to gain these skills and get ahead of the curve.
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