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Data science is a popular and growing field, one often linked with computer science, AI, machine learning, and other STEM subfields. It is also a popular path for students who want to go into those fields, but want to pick a major strategically, and aren’t sure computer science is the right choice for them.
That said, there is a lot of misinformation floating around about data science, and how best to get involved with and take advantage of the opportunities it offers. In this guide we’ll break down the possibilities found within the field, the paths you can take to get there, and what you can do with data science. Let’s get started!
Data scientists focus on collecting, processing, analyzing, visualizing, and making predictions based on data. In data science, the focus is on building models that can extract insights from data. Required skills for data scientists include: programming, data visualization, statistics, and coding. Data scientists are instrumental in every industry, using their skills to identify medical conditions, optimize logistics, inform city planning, fight fraud, improve shopping experiences, and more.
That said, data science is less of a true field unto itself, and more of a practical application of math, statistics, and computer science. Any students interested in data science will need some grasp of programming, and will need some skill with math. While data science is applicable to a wide variety of fields, it is its impact in tech fields which is currently receiving the most focus.
Machine learning and AI research both make use of data science techniques to review, format, and process the data sets used to train these new models. The goal is for these programs to be easily able to analyze vast amounts of data, and to make connections based on data they have seen previously. These fields are not strictly the same as data science, but many students interested in data science are doing so to get into AI, so we will be returning to it regularly throughout this guide.
There are numerous paths available to study data science, not all of which are of equal value. At many universities, data science majors offered are actually not the ideal path, as the skill set they give you is not broad enough, and they will not position you for the careers most students want when studying data science.
Instead, the ideal path gives you both a needed core skill, and a domain of focus. Core skills are computer science, applied math, engineering, or other technical fields, while areas of focus are where you want your eventual career to head, such as environmentalism or AI.
Here is a list of possible paths into data science, optimized for students who wish to become involved with machine learning and AI. Note that if these fields are your goal, you will need to learn Python specifically, and have a good understanding of computer programming and structures.
The easiest way to get a solid grounding in data science and position yourself to work with AI is to major in computer science. Taking the core of CS classes while focusing on electives in machine learning and AI will give you the background you need. Many CS programs also have data science specific courses, specializations, or minors. With this path you are fully qualified to begin work with just a bachelors, or can continue for a master’s in CS or an MBA in the future.
This is similar to the above path, though you will have less flexibility in your coursework, and will have more required courses to take. The key is to choose a subdiscipline within engineering that aligns with your ultimate aims; computer engineering is more likely to be helpful than petroleum engineering for instance.
Applied math is not enough on its own; you will need to take many courses focusing on machine learning and CS in order to be qualified for a job right out of undergrad. Picking up a minor in computer science is often helpful in this approach. The advantage of this path is that applied math is a far less competitive major for admissions than either computer science or engineering, and still enables you to gain the core skills needed to master data science. In other cases, you may need a further master’s degree to gain the needed CS knowledge.
Statistics works well when combined with math or computer science. Statistics courses on their own will not give you the depth of technical knowledge needed to begin work in data science immediately. Even more than applied math, you will need to supplement your course load to gain the needed technical skills for a career in data science. Otherwise you will need a masters degree in computer science for these goals. An exception is Carnegie Mellon University, which has a joint CS + Stats program which gives you all the tools you will need.
This is so far down on the list because most colleges with separate data science majors do not provide much support for students interested in machine learning or AI directly, and will not do a very good job of preparing you to enter those fields. Some colleges have strong enough data science programs on their own to make up for this, but otherwise you should only pursue the data science major if the school has sufficient machine learning resources outside of it to give you the grounding you need. The schools with data science majors we recommend on their own are: NYU, Columbia, UC Berkeley, UChicago, and UMichigan.
Most data science programs give you a good breadth, but do not offer the needed depth on machine learning and AI; instead focusing on data engineering and analysis.
The paths outlined above are designed to give you the necessary technical background to begin a career in machine learning and AI directly out of undergrad. If you are unable to meet all of the requirements, you may find it necessary to complete an additional master’s degree in CS.
On top of that, students interested in joining tech start ups (which are currently major players in this field) often return for an MBA after a few years in the workforce. WHile this is not necessary if you are purely interested in the technical side, we recommend it if you are interested in business as well. This approach is also advisable over doing data science and business during undergrad at the same time, unless you are part of a program meant specifically to explore the intersection of those fields, such as UPenn’s M&T program.
Preparing for data science in high school is similar to preparing for other STEM fields. Gaining experience with research, taking advanced math courses, and beginning to explore programming languages all help give you the baseline of technical skills you will need.
Your high school will likely offer some courses on computer science; we recommend taking all that are available to you, and exploring coding on your own as well. In addition to this, you will need a strong background in math, and should take courses through at least calculus, if not further, depending on what your school offers.
Finally, completing research in high school is a good way to stand out when applying to these programs, especially the more competitive ones. Note that data science and machine learning are some of the easiest areas for high school students to conduct significant research in, and these projects will also help you ascertain whether these fields are something you want to explore further.
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