10 answers
Genevieve
Student
Cedar Rapids
78
Questions
Asked
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Best major to pair with data science?
I would like to either double-major or pair a minor with a degree in data science. What degree would be the best option: math, applied statistics, accounting, finance, marketing?
Other suggestions are welcome!
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10 answers
Jamie Meyer
Strategist. Methods Analyst. Interdisciplinary Specialist. Social Architect.
12
Answers
Brisbane, Queensland, Australia
Updated
Jamie’s Answer
Hello Genevieve, I am going to make an out of the box suggestion. I work with a lot of people in the Data Science/Tech realm. A degree or study in the area of Communications would drastically add and expand your value in Data Science. It would allow you to translate Data situationally, explain Data insightfully and communicate how the Data works and is accumulated. It would enhance your ability to work in the Data Science realm and collaborate with external organizations and teams. Wishing you much success!
Thanks for the suggestion Jamie! I would have never thought of it! :)
Genevieve
Abby Lupi
CareerVillage.org TeamData Analyst at CareerVillage.org
55
Answers
Rochester, New York
Updated
Abby’s Answer, CareerVillage.org Team
Hi Genevieve! This is a great question.
As a data analyst, I want to offer a huge +1 to Jamie's suggestion of communication! A big part of working in a team is figuring out the best way to present and communicate my findings in a way that data AND non-data minded people can understand. I do this almost every day.
However, it also depends what kind of work environment / style works best with your brain. Do you want to work in a small team? A science lab? A small company? A nonprofit? A think tank? Do you want to work remotely or in person or a hybrid? Your choice might differ depending on where you want to go.
If you want to double down in the technical space (think machine learning, programming, advanced visualizations, development) I'd recommend computer science or similar.
If you're interested in data storage, transfer, and management (especially as it pertains to large databases), you could look into data engineering or information technology!
I myself studied physics in college and found that many of the skills I learned in problem solving, analysis, and lab reports transferred wonderfully into data science. I was fortunate enough that my choice of major was entirely guided by what I enjoyed. So if you have that opportunity, go with what you're most interested in! It will make the college experience that much brighter :)
As a data analyst, I want to offer a huge +1 to Jamie's suggestion of communication! A big part of working in a team is figuring out the best way to present and communicate my findings in a way that data AND non-data minded people can understand. I do this almost every day.
However, it also depends what kind of work environment / style works best with your brain. Do you want to work in a small team? A science lab? A small company? A nonprofit? A think tank? Do you want to work remotely or in person or a hybrid? Your choice might differ depending on where you want to go.
If you want to double down in the technical space (think machine learning, programming, advanced visualizations, development) I'd recommend computer science or similar.
If you're interested in data storage, transfer, and management (especially as it pertains to large databases), you could look into data engineering or information technology!
I myself studied physics in college and found that many of the skills I learned in problem solving, analysis, and lab reports transferred wonderfully into data science. I was fortunate enough that my choice of major was entirely guided by what I enjoyed. So if you have that opportunity, go with what you're most interested in! It will make the college experience that much brighter :)
Thanks for taking the time to help Abby! :)
Genevieve
Terrell Turner
Firm Partner
3
Answers
Pineville, North Carolina
Updated
Terrell’s Answer
If you are studying data science, that will give you a really good grasp of understanding data and analyzing information so I good complement would be business, accounting, or marketing. Each of these fields of study will give you exposure to how businesses and business models work so you can learn how to use your knowledge of data to make decisions that impact the way an organization operates, makes money or establish growth. Being able to combine all of these together can put you on a pretty good path toward leadership and promotions within an organization or entrepreneurship if you choose that route.
Thank you Terrell! :)
Genevieve
Adit Bhiday
Software Engineer
11
Answers
Gibson City, Illinois
Updated
Adit’s Answer
Data science is a highly interdisciplinary field, and there are many majors that can pair well with it to create a strong skill set for a career in data science. Here are some majors that can complement a degree in data science:
Mathematics or Statistics: A major in mathematics or statistics can provide a strong foundation in the mathematical and statistical techniques that are essential for data science. This can include topics like probability theory, linear algebra, and multivariate calculus, which are all important for understanding the mathematical models that underlie many data science algorithms.
Computer Science: Computer science is another natural pairing with data science, as it provides a strong foundation in programming languages, algorithms, and data structures. A major in computer science can also provide a deeper understanding of databases, distributed systems, and software engineering principles, which are all important for building scalable and efficient data systems.
Economics: Economics is a social science that provides a strong foundation in statistical analysis and econometric techniques, which are essential for many applications of data science. Economics majors can also gain a deeper understanding of microeconomic theory, which can help with understanding how markets and consumers behave.
Biology: Biology majors can provide a unique perspective on data science, as they often have a strong foundation in biology, chemistry, and other natural sciences. This can be valuable for applications like bioinformatics, genomics, and personalized medicine, where understanding biological systems is essential.
Overall, there are many majors that can complement a degree in data science, and the best major to pair with data science will depend on the individual's interests and career goals.
Mathematics or Statistics: A major in mathematics or statistics can provide a strong foundation in the mathematical and statistical techniques that are essential for data science. This can include topics like probability theory, linear algebra, and multivariate calculus, which are all important for understanding the mathematical models that underlie many data science algorithms.
Computer Science: Computer science is another natural pairing with data science, as it provides a strong foundation in programming languages, algorithms, and data structures. A major in computer science can also provide a deeper understanding of databases, distributed systems, and software engineering principles, which are all important for building scalable and efficient data systems.
Economics: Economics is a social science that provides a strong foundation in statistical analysis and econometric techniques, which are essential for many applications of data science. Economics majors can also gain a deeper understanding of microeconomic theory, which can help with understanding how markets and consumers behave.
Biology: Biology majors can provide a unique perspective on data science, as they often have a strong foundation in biology, chemistry, and other natural sciences. This can be valuable for applications like bioinformatics, genomics, and personalized medicine, where understanding biological systems is essential.
Overall, there are many majors that can complement a degree in data science, and the best major to pair with data science will depend on the individual's interests and career goals.
Adit, thank you so much for the in-depth and well-written answer!
Genevieve
Micheal White
Reimbursement and Quality Metrics Management
18
Answers
Panama City Beach, Florida
Updated
Micheal’s Answer
I would totally look at computer science. And depending on what subcategory of data science you wanted to be in because they're a lot health care, finance, cyber blockchain. There's a ton. And no they're all data science. They have their own nuances. I would also expect to look towards an MS for sure in the end. Like I said before, get on GitHub. Get on Kaggle look it over. Ask some questions and you'll find out which direction you want to go in. And plan to do internships.
Thanks Michael!
Genevieve
jie peng
financial services
3
Answers
Roselle, Illinois
Updated
jie’s Answer
These are all excellent choices, and the “best” option depends on several factors:
• Your future inspirations: are you interested in pursuing research/academia, or working in industries such as technology, accounting, finance, and marketing?
• Your strengths: are you more adept at theoretical concepts or communication skills?
• the curriculum offered at your institution: does the data science curriculum focus on underlying theories or practical applications?
If it’s challenging to decide, I’d recommend taking/auditing an introductory course in each of the listed fields to gauge your level of interest and aptitude. This should help you determine what fits you the “best”.
• Your future inspirations: are you interested in pursuing research/academia, or working in industries such as technology, accounting, finance, and marketing?
• Your strengths: are you more adept at theoretical concepts or communication skills?
• the curriculum offered at your institution: does the data science curriculum focus on underlying theories or practical applications?
If it’s challenging to decide, I’d recommend taking/auditing an introductory course in each of the listed fields to gauge your level of interest and aptitude. This should help you determine what fits you the “best”.
Thanks for the advice Jie! :)
Genevieve
Benjamin Rust
tech
3
Answers
Atlanta, Georgia
Updated
Benjamin’s Answer
In order to truly expand and enrich your knowledge, it is essential to delve into a wide variety of subjects, transcending your current areas of interest. Exploring disciplines such as history, philosophy, linguistics, and the humanities will not only broaden your intellectual horizons, but it will also foster critical thinking, creativity, and problem-solving skills. By embracing diverse perspectives, you will gain a better understanding of the world around you and the people within it.
As we continue to progress into an era of increasing automation, many of the technical aspects of work and problem-solving will be handled with ease, freeing individuals to focus on higher-order thinking and exploration. In this context, having a well-rounded knowledge base will become even more crucial, as it will equip you with the necessary background to make informed decisions, contribute meaningfully to various domains, and apply your expertise in building increasingly complex models and strategies.
To achieve this, consider pursuing courses, reading books, attending seminars, or engaging in discussions related to subjects outside your field of expertise. You may find that insights from history can lend perspective to current issues, or that philosophical debates can sharpen your analytical capabilities. Moreover, knowledge gained from linguistics can help deepen your understanding of communication and human thought processes, while the humanities can teach you about culture, art, and social dynamics.
In summary, the key to broadening your knowledge is to embrace diversity in your learning journey. Adopting a multidisciplinary approach will not only enhance your mental acuity but will also enable you to draw upon a rich reservoir of information when building models and solving complex problems in an increasingly automated world.
As we continue to progress into an era of increasing automation, many of the technical aspects of work and problem-solving will be handled with ease, freeing individuals to focus on higher-order thinking and exploration. In this context, having a well-rounded knowledge base will become even more crucial, as it will equip you with the necessary background to make informed decisions, contribute meaningfully to various domains, and apply your expertise in building increasingly complex models and strategies.
To achieve this, consider pursuing courses, reading books, attending seminars, or engaging in discussions related to subjects outside your field of expertise. You may find that insights from history can lend perspective to current issues, or that philosophical debates can sharpen your analytical capabilities. Moreover, knowledge gained from linguistics can help deepen your understanding of communication and human thought processes, while the humanities can teach you about culture, art, and social dynamics.
In summary, the key to broadening your knowledge is to embrace diversity in your learning journey. Adopting a multidisciplinary approach will not only enhance your mental acuity but will also enable you to draw upon a rich reservoir of information when building models and solving complex problems in an increasingly automated world.
Thanks again!
Genevieve
Paul Kerl
Senior Director
4
Answers
Boston, Massachusetts
Updated
Paul’s Answer
I am going to say something different than others here...
Pair Data Science with an application area that you are interested in! Whatever that is. Are you interested in Marketing? Pair it with that. Are you interested in finance? Then pair it with that. Interested in the stars and astronomy? Then Astronomy or Physics! Data Science has a wide, wide range of applicability in almost any area that has data - almost anything nowadays.
Data Science is a set of tools that will help you answer questions using data, and using the scientific method. That said, you will always have very interesting areas that you can apply those tools in almost any discipline.
That can help guide you into thinking what major to pair data science - and it is OK not to know too! That is normal (and what I ran into as well) - expand your horizons by trying some classes out, and ask others (like you are here!) about how they chose their major, and how they reflected on that choice too.
Pair Data Science with an application area that you are interested in! Whatever that is. Are you interested in Marketing? Pair it with that. Are you interested in finance? Then pair it with that. Interested in the stars and astronomy? Then Astronomy or Physics! Data Science has a wide, wide range of applicability in almost any area that has data - almost anything nowadays.
Data Science is a set of tools that will help you answer questions using data, and using the scientific method. That said, you will always have very interesting areas that you can apply those tools in almost any discipline.
That can help guide you into thinking what major to pair data science - and it is OK not to know too! That is normal (and what I ran into as well) - expand your horizons by trying some classes out, and ask others (like you are here!) about how they chose their major, and how they reflected on that choice too.
Great answer, thanks Paul!
Genevieve
PwC Impact
575
Answers
Orlando, Florida
Updated
PwC’s Answer
Business or a similar degree makes a great pair with Data Science! This is a popular combination of students at UC Berkeley
Thank you!!
Genevieve
James Constantine Frangos
Consultant Dietitian & Software Developer since 1972 => Nutrition Education => Health & Longevity => Self-Actualization.
6075
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Dear Genevieve,
Choosing a Major in Mathematics
Mathematics is a perfect match for data science, offering a robust foundation in quantitative analysis, linear algebra, and calculus. A mathematics degree can sharpen your analytical skills, problem-solving capabilities, and detail orientation, all of which are crucial in the realm of data science.
Here's why mathematics is a great choice for data science:
Solid mathematical foundations: Mathematics offers a profound understanding of key concepts like probability, statistics, and linear algebra, all of which are fundamental in data science.
Problem-solving abilities: Mathematics fosters critical thinking and logical reasoning, indispensable for tackling complex data science problems.
Computational skills: Mathematics classes often incorporate programming and computational thinking, which are highly applicable to data science tasks.
Versatility: A degree in mathematics can be utilized in various data science areas, including machine learning, statistical modeling, and data visualization.
Choosing a Major in Applied Statistics
Applied statistics is another outstanding choice to complement a data science degree. A degree in statistics concentrates on applying statistical methods to real-world challenges, directly aligning with data science. Here's why applied statistics is a suitable choice:
Statistical methodology: Applied statistics offers a robust foundation in statistical methods, including hypothesis testing, regression analysis, and experimental design.
Data analysis: Applied statistics underscores the practical use of statistical methods to analyze and interpret data.
Computational skills: Similar to mathematics, applied statistics classes often include programming and computational thinking, which are vital for data science.
Data-driven decision making: Applied statistics equips students with the skills to make informed decisions based on data, a key skill in data science.
Specialization: Applied statistics provides various specializations that can be customized to specific data science areas of interest.
Choosing a Major in Accounting or Finance
While not as directly linked to data science as mathematics or applied statistics, accounting and finance degrees can offer valuable skills for data scientists in these fields. Here's why accounting or finance could be a suitable choice:
Financial analysis: Accounting and finance degrees offer a solid foundation in financial analysis, crucial for understanding financial datasets and making data-driven finance decisions.
Regulatory knowledge: Accounting and finance degrees often cover financial regulations and compliance, useful when dealing with financial datasets that must comply with specific regulations.
Business acumen: An accounting or finance degree can enhance understanding of the business environment where data science projects are executed, leading to better decision-making and improved stakeholder collaboration.
Data storytelling: Both accounting and finance highlight effective communication of complex financial information to stakeholders, which is highly applicable to data storytelling in data science projects.
Specialized applications: Data scientists in finance or insurance may find an accounting or finance background beneficial due to these industries' unique datasets and regulatory requirements.
Choosing a Major in Marketing
Marketing may appear as an unconventional choice for a data science degree, but it can offer valuable insights into consumer behavior and market trends. These insights can be used for data-driven decision-making across various industries. Here's why marketing could be a suitable choice:
Understanding customer behavior: Marketing degrees offer insights into consumer behavior and preferences, useful for data-driven decision-making related to product development, marketing strategies, and customer segmentation analysis.
Marketing analytics: Marketing courses often cover marketing analytics tools and techniques applicable to various data science fields, such as predictive analytics for customer segmentation or churn analysis.
Communication skills: Marketing emphasizes effective communication of complex ideas to diverse audiences, which is crucial when presenting data insights or visualizations to non-technical stakeholders within an organization.
May God bless you!
James Constantine Frangos.
Choosing a Major in Mathematics
Mathematics is a perfect match for data science, offering a robust foundation in quantitative analysis, linear algebra, and calculus. A mathematics degree can sharpen your analytical skills, problem-solving capabilities, and detail orientation, all of which are crucial in the realm of data science.
Here's why mathematics is a great choice for data science:
Solid mathematical foundations: Mathematics offers a profound understanding of key concepts like probability, statistics, and linear algebra, all of which are fundamental in data science.
Problem-solving abilities: Mathematics fosters critical thinking and logical reasoning, indispensable for tackling complex data science problems.
Computational skills: Mathematics classes often incorporate programming and computational thinking, which are highly applicable to data science tasks.
Versatility: A degree in mathematics can be utilized in various data science areas, including machine learning, statistical modeling, and data visualization.
Choosing a Major in Applied Statistics
Applied statistics is another outstanding choice to complement a data science degree. A degree in statistics concentrates on applying statistical methods to real-world challenges, directly aligning with data science. Here's why applied statistics is a suitable choice:
Statistical methodology: Applied statistics offers a robust foundation in statistical methods, including hypothesis testing, regression analysis, and experimental design.
Data analysis: Applied statistics underscores the practical use of statistical methods to analyze and interpret data.
Computational skills: Similar to mathematics, applied statistics classes often include programming and computational thinking, which are vital for data science.
Data-driven decision making: Applied statistics equips students with the skills to make informed decisions based on data, a key skill in data science.
Specialization: Applied statistics provides various specializations that can be customized to specific data science areas of interest.
Choosing a Major in Accounting or Finance
While not as directly linked to data science as mathematics or applied statistics, accounting and finance degrees can offer valuable skills for data scientists in these fields. Here's why accounting or finance could be a suitable choice:
Financial analysis: Accounting and finance degrees offer a solid foundation in financial analysis, crucial for understanding financial datasets and making data-driven finance decisions.
Regulatory knowledge: Accounting and finance degrees often cover financial regulations and compliance, useful when dealing with financial datasets that must comply with specific regulations.
Business acumen: An accounting or finance degree can enhance understanding of the business environment where data science projects are executed, leading to better decision-making and improved stakeholder collaboration.
Data storytelling: Both accounting and finance highlight effective communication of complex financial information to stakeholders, which is highly applicable to data storytelling in data science projects.
Specialized applications: Data scientists in finance or insurance may find an accounting or finance background beneficial due to these industries' unique datasets and regulatory requirements.
Choosing a Major in Marketing
Marketing may appear as an unconventional choice for a data science degree, but it can offer valuable insights into consumer behavior and market trends. These insights can be used for data-driven decision-making across various industries. Here's why marketing could be a suitable choice:
Understanding customer behavior: Marketing degrees offer insights into consumer behavior and preferences, useful for data-driven decision-making related to product development, marketing strategies, and customer segmentation analysis.
Marketing analytics: Marketing courses often cover marketing analytics tools and techniques applicable to various data science fields, such as predictive analytics for customer segmentation or churn analysis.
Communication skills: Marketing emphasizes effective communication of complex ideas to diverse audiences, which is crucial when presenting data insights or visualizations to non-technical stakeholders within an organization.
May God bless you!
James Constantine Frangos.