A bachelor’s degree in data science or a bachelor’s degree in data analytics allows individuals to enhance their skills by exposing them to challenges faced in different industries when working with an extensive amount and variety of datasets.
Because of this, a data science degree is multidisciplinary.
The data science department often collaborates with other departments to provide you with hands-on experience working with data in different domains and foundational classes in data science and data analytics.
Data expertise is in high demand because decision-makers rely on data and evidence to make informed decisions. Examples that support this knowledge include the growth of precision and personalized healthcare that needs support from evidence-based medicines.
Impact assessment is another example that heavily relies on data in decision sciences and nearly all kinds of industries.
As technological innovation advances, it has become increasingly possible to store large amounts of data. Such vast data has provided advantages by increasing the sample size to remove bias.
In the past, any individual with a degree in distinct disciplines such as computer science, operations research, or statistics had the skill set needed to analyze the required data.
However, data science and data analytics programs are becoming increasingly more sophisticated. The “five V’s of data”—velocity, volume, value, variety, and integrity in recent times pose unique challenges.
This has led to the rise of multidisciplinary undergraduate and master’s degrees in data science and analytics programs and opportunities.
Some universities offer a data science undergraduate degree as a standalone program, while others offer it as a majors/minors program. Students enrolled in this program often complete their degrees in four to five years, depending on their enrollment status.
The format of the course also differs from university to university. Some universities offer this program in person; others provide it entirely online. A few others choose a hybrid model conducting in-person classes and online lectures, allowing flexibility to pace the course.
Most universities that enroll students for a bachelor’s in data science will need you to have proof of completing a high school education or a GED. Some universities will also consider your SAT/ACT score and an additional TOEFL exam for international students.
Tuition for a bachelor’s degree will vary whether you consider a degree from a community college or a university. Some degrees may be cheaper than others depending on whether you are an in-state student, out-of-state student, or an online degree.
Data Analytics Bachelor’s Degree Curriculum
There are many different ways to earn credentials in data analytics. The most popular options include data analytics bootcamps, data analytics certifications, and data analytics degree programs.
Universities have started offering data science and analytics degrees in four-year undergraduate programs. Due to the nascency of these programs and variability in the field, there is a considerable variation in the curriculum.
Some programs are oriented toward the business applications of analytics, while others dive deep into the technical aspects of data science.
Since each program is different, students are encouraged to contact the school offering these programs to understand better the course and the fit based on their interests. Courseworks include many projects to get hands-on experience to make students comfortable working with real-world data.
Even though programs vary, foundational courses remain the same and include concepts related to:
- Data Mining and Exploration
- Data mining generally refers to gathering relevant data from multiple structured and unstructured sources. Data exploration refers to extracting descriptive statistics from the mined data.
- Data Visualization
- Data visualization involves studying techniques to represent the outcomes from raw data in readable and visual formats easily understood by the stakeholders.
- Statistical Analysis
- Statistical analysis includes learning statistical methods to draw inferences from the given dataset. These range from essential topics like linear regression, classification, principal component analysis to more specialized topics such as neural networks, time series models, survival analysis, Markov chains, Bayesian statistics, graph models, and spatial processes.
- Data Engineering
- This coursework exposes students to technologies used in building infrastructures to handle large datasets that data scientists and analysts can easily consume.
- Machine Learning
- While statistical analysis teaches how to draw inferences from a population, machine learning is about understanding generalized predictive patterns in the dataset.
- Ethics and Privacy
- Coursework in ethics and privacy help students realize the principles behind using technology with data to maintain individual privacy. This is especially true when working with involving identity data, patient data, or personal finance data.
Data Science Bachelor Considerations
The majority of universities have started offering undergraduate programs in data science on-campus. For individuals looking to pursue a degree in data science but have constraints related to location and other responsibilities, there are online programs to offer flexibility. Continuing with the degree may still differ for in-state students compared to out-of-state students.
- Institution
- Opting for a degree from a well-recognized institution would be helpful for future job searches. Information about the program’s quality is readily available due to the institution’s recognition. Students are advised to reach the universities to learn more details or ask current students and alumni about the curriculum structure, class quality, and career outcomes.
- Job Placement
- Any institution that does not have job placement opportunities should be considered cautiously. It is always good to talk to current students and reach out to the institution’s alumni before enrolling in a program.
- Curriculum Structure
- Not all data science programs are the same. Some programs may be focused on specialized areas such as health care, while others on business analytics. Some programs may include many capstone projects, while others may have more online classes and require you to collaborate with other enrolled students for group assignments.
- Some programs take into account your life experiences in constructing your curriculum. This shortens the time to get a degree and reduces the cost. It would be great to think about what kind of portfolio you would like to create for your future employment.
Career Paths
The jobs mentioned here cover the major career areas that individuals with data science and analytics pursue. The salaries provided here are a national average, and the numbers will differ based on company sector, location, and work experience.
- Data Scientist
- According to Salary.com, the base salary for a Data Scientist is $144,080 per year, which typically falls between the range from $129,780 and $158,440. The Bureau of Labor Statistics has provided a more detailed report on employment and wage statistics for data scientists.
- Data Analyst
- Salary.com says the average base salary is $84,297 per year which typically ranges between $75,449 and $94,306.
- Data Architect
- The average base salary is $164,596 per year. Top-paying locations are San Francisco Bay Area, Washington DC Baltimore Area, New York City Metropolitan Area, Greater Boston, and Greater Seattle Area.
- Business Intelligence Analyst
- The average salary for a business intelligence analyst in the United States is at$90,666 per year which typically falls between $81,206 and $102,560 according to salary.com.
- Data Visualization Developer
- The average salary for the role of Developer in the United States is $119,735 per year, typically ranges from $102,827 to $132,447, according to Salary.com.
- Data Solutions Architect
- The average salary for the role of Solutions Architect in the United States is $129,344 per year. This salary is based on the individually reported data submitted by users from Salary.com. The top-paying locations for this title includes Hawaii, Texas, Arizona, Kansas and South Dakota.
- Data Engineer
- The base salary for a Data Engineer is $116,310 per year (typically falls between $100,514 and $133,601) in the United States based on Salary.com.
- Machine Learning Engineer
- The average salary for a Machine Learning Engineer in the United States is $124,353 per year (typically falls between $111,497 and $138,578) and this salary is based on Salary.com.
Data Science and Data Analytics Degree Outlook
There are many ways to enter a career in data science. However, a bachelor’s in data science or analytics provides a jumpstart to your career in this field, making you equipped with all the necessary skills.
After graduation, data science students can find work in various sectors, including healthcare, education, government, technical services, and research and development. Students take different career paths and gain upward mobility with further experience.
According to the Bureau of Labor Statistics, the employment of data professionals is projected to grow 23 percent from 2022 to 2032, much faster than the average for all occupations.
With the given statistics, it looks like data-related jobs are here to stay, making a bachelor’s in data science a good decision to pursue if you have a knack for solving data challenges.
Frequently Asked Questions
A bachelor’s degree in data science and data analytics is an undergraduate program that combines elements of statistics, computer science, and mathematics to prepare students for careers in data analysis, data management, and information processing.
Typically, applicants should have a strong foundation in mathematics and computer science. High school courses in calculus, statistics, and programming are often recommended.
Graduates can pursue roles such as data analyst, data scientist, business intelligence analyst, and roles in big data technologies, among others.
Yes, many universities offer online programs, providing flexibility for students who cannot attend on-campus classes.
Many programs include capstone projects, internships, and collaborations with industry partners to provide practical, hands-on experience with real-world data.