What is Data Science?
Data science is an interdisciplinary field that combines aspects of statistics, mathematics, computer science, and industry-specific knowledge to derive meaningful insights from raw data. Data scientists employ the skills of a statistician to effectively model and summarize data with the skills of a computer scientist to efficiently design and implement algorithms to uncover patterns, trends, and relationships in data. They then use this information to inform business decisions, develop new products or services, or gain a competitive edge in the marketplace.
For example, a data scientist at Netflix might:
- Use data to help identify new shows and movies that will be successful with viewers
- Analyze user behavior and preferences to personalize recommendations and improve user retention
- Develop algorithms and models to optimize the content delivery pipeline, ensuring that users receive high-quality streams without buffering
- Work on A/B testing experiments to optimize the user interface and improve user engagement
- Collaborate with cross-functional teams such as product managers, engineers, and designers to develop new features and products based on data-driven insights.
What Tools Do Data Scientists Use?
The most popular programming language for data science tasks is Python. It is lauded for its readability and active communities that constantly update and innovate new tools that enable creativity from the data cleaning and aggregation stages to the data visualization stage. Moreover, it provides a wide array of machine learning algorithms to choose from to solve business problems. Similar languages such as R and Java can also tackle the same problems, but Python is quickly becoming the industry standard and looks to be more “future-proof” than R or Java.
While Python can act as somewhat of a panacea for data science solutions, other software such as Excel, Tableau, PowerBI, and SQL can provide more powerful solutions for specialized data science needs. For example, Excel is renowned for its easily implemented pivot tables; Tableau and PowerBI: summarized data visualizations; SQL: pre-processing and cleaning of insurmountably large data sets. To be sure, Python can accomplish all of these tasks, but it always helps to be well-rounded (as great data scientists are!)
More Resources to Learn About What Data Scientists Do
How to Get Started in Data Science in College
There are many different ways to build up your data science skill set and experience at UT while you are completing your undergraduate degree. The list below shows some ways you can get started!
Data-Related Degrees in CNS
You don't necessarily need a particular degree to go into the field of data science, but these are the most relevant undergraduate majors in the College of Natural Sciences for a career in data science:
Data-Related Certificates in CNS
Any degree at UT can be supplemented by the following data-related certificate programs:
Teach Yourself Data-Related Skills
You can also level up your data skills on your own by participating in professional development courses free to all UT students:
How to Gain Useful Soft Skills
Outside of developing your technical skills, it's important that you are able to communicate rigorous technical materials to those inside and outside of your industry. Companies love to see well-rounded candidates that have useful interpersonal and professional skills. Some key skills that employers look for are collaboration/teamwork, time management, communication, and organization. These skills are equally valuable as the scientific or technical skills that can be learned by training at the company and should be displayed on your resume. Here are a few ideas about how to develop these soft skills. Don't feel pressured to do these specific activities if you are not interested in them. Find ways to get involved on campus that reflect your own hobbies and interests!
- Teaching: become a Sanger Learning Center tutor or a teaching/learning assistant
- Mentorship: mentor other students as an FRI mentor, FIG mentor, honors cohort mentor, or resident assistant
- Leadership: join a club on campus and contribute to a cause
- Community Involvement: volunteer, do DEI-related work, or become mental health ambassador
Resources Outside of CNS
While the College of Natural Sciences has many tools to help its students be successful, they are not exhaustive and there any many more resources outside of CNS that can aid in preparation for becoming a leader in the field of data science.
Kaggle
Kaggle is the premier data science platform for those looking to complete personal data science projects and build up real industry skills. Kaggle features an endless stream of notebooks for you to follow along and experiment with algorithm implementation and visualization techniques on real data!
With Kaggle being the hub for data science online, the importance of pursuing personal projects to further your data science experience cannot be understated. Projects give you an opportunity to not only practice your skillset but also to showcase your own personal interests. They also provide valuable experience that can be highlighted on your resume and discussing the who, what, and why of the project can make all the difference in a technical interview. Check out this data science glossary for project ideas and relevant skills to be implemented in the industry!
Youtube
YouTube is also an excellent (and sometimes underrated) source of data science material as many industry leaders put their communicative skills to use through informative videos about the industry. Some channels that are especially relevant to learning more about the field and what it entails quantitatively and qualitatively are:
Preparing Your Technical Resume
The resume is your gateway into applying to internships and jobs in the industry. Some key sections that distinguish a data science resume from a normal one are having a skills section with technical and data visualization skills at the top of your resume and having a projects section if you have any personal projects that you'd like to highlight. These projects don't have to be extensive, small data-related Python or SQL coding projects are perfect! These projects can help you land your first internship or full-time job, as industry experience is the gold standard in this field. Feel free to check out the resume resources on our CNS Career Services website for more resources and inspiration.
Add Personal Coding Projects to Your Resume
Projects are a good way to practice your coding skills, learn how to use different technical tools, and impress employers before you've landed your first technical internship. Check out our guide to developing your own technical projects to show off your skills on your resume!
If you've already completed some technical projects in class or on your own but you don't know how to add them to your resume, check out our resume resources above for help adding them to your resume properly.
Preparing for Technical Interviews
Data science interviews are designed to assess candidates' quantitative and qualitative prowess and as such typically consist of a behavioral/situational interview and a technical interview. Our Tech Peer Coaching Team also provides Technical Interview Prep appointments during the Fall and Spring semesters. Check out our Interview Prep Guides for our top tips, tricks, and resources to help you prepare.
Finding Jobs and Internships
Beginning to look for jobs and internships can be challenging in its own right but the College of Natural Sciences has numerous resources to make the process as straightforward as it can be. If you are having trouble finding postings that are a good match for you, make an appointment with a coach!
Helpful Data Science Related Keywords for Your Job Search
You can also try combining these keywords with other related terms, such as specific industries, software tools or programming languages, to narrow down your search results. For example, you could search for "data analyst SQL retail" or "marketing analyst Tableau Python".
- Data scientist
- Machine learning engineer
- Data analyst
- Business intelligence analyst
- Data analyst
- Business analyst
- Financial analyst
- Marketing analyst
- Operations analyst
- Customer insights analyst
- Product analyst
- Sales analyst
Is Graduate School Necessary for a Career in Data Science?
While many entry-level Data Analyst jobs are open to people with bachelor's degrees, it should be noted that more advanced positions in the field of data science will almost certainly require an advanced degree (Master's or Ph.D.). While this industry precedent is slowly changing as the field looks to be more accessible to qualified professionals that might not have the ability to pursue an advanced degree, it is nonetheless a norm in the industry to look out for. If you are curious about whether grad school is the right step for you, make an appointment with a career coach!