Online Bachelor's in Data Science

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What Is a Data Science Degree?

Our modern world contains more data than ever. According to a 2020 TechJury article, humans produce 2.5 quintillion bytes (a quintillion is a 1 followed by 18 zeros) of data every day. This impressive statistic will only grow as we continue to generate information. Organizations and businesses increasingly rely on data to make decisions, predict trends, market to consumers, and scale their operations. Those with a data science degree can transform raw information into usable reports, graphs, and dashboards.

Our modern world contains more data than ever. According to a 2020 TechJury article, humans produce 2.5 quintillion bytes (a quintillion is a 1 followed by 18 zeros) of data every day. This impressive statistic will only grow as we continue to generate information. Organizations and businesses increasingly rely on data to make decisions, predict trends, market to consumers, and scale their operations. Those with a data science degree can transform raw information into usable reports, graphs, and dashboards.

Exactly what is a data science degree? It is a program, such as a bachelor’s degree in data science, that enables graduates to use their expertise in computer science and mathematics to tackle unstructured data, solve multifaceted problems, and make data-driven recommendations. With the rise of big data, these data scientists are valued by businesses and organizations across a wide variety of fields. Because there are so many potential applications for data science in such an array of industries, data scientists often have a wealth of exciting career opportunities.

Data scientist analyzes a KPI dashboard that displays data charts and analytics

The Skills of a Data Science Major: AI, Machine Learning, and Deep Learning

In data science degree programs, students generally learn a number of highly useful skills, including data analysis and computer programming as well as predictive modeling, statistics, calculus, and economics. In addition, data science students often discover how to present their findings and data-driven recommendations in easy-to-understand formats for their colleagues to use. A data science curriculum also typically lays the groundwork for a student’s understanding of artificial intelligence (AI), machine learning, and deep learning.

Artificial Intelligence

Artificial intelligence is the high-level process of training machines and computers to act, think, decide, or reason on their own. While the term is often associated with robots, its application is much broader, including self-driving cars and the automatic purchase and sale of stocks. Due to its growing use in emerging technologies, AI has become a central topic in many data science major courses.

Machine Learning

A type of AI in which data scientists equip computers with self-correcting algorithms that enable them to teach themselves, machine learning makes for sophisticated and adaptable systems. When put in place, these systems leverage digital neural networks to course correct, make decisions, and learn. It is through machine learning that computers recommend movies, write music, process visual images, and diagnose illnesses.

A key concept in the study of machine learning is deep learning, which leverages neural networks with multiple feedback levels to make more complex determinations. Deep learning enables machines to discern for themselves, follow nested decision paths, and find nuances in datasets.

To implement any of these advanced techniques, data scientists must provide machines with corrected, complete data whenever possible and apply programming languages to fully understand and use this wealth of information.

Applications of Data Science: Predictive Analytics and Metadata Management

Students interested in learning more about what a data science degree is should know that graduates are often employed in a variety of professional settings. For example, some graduates are hired to apply predictive analytics to business, whereas others are tasked with creating data-mining solutions. By leveraging competency in machine learning, statistics, and algorithms, data scientists make intelligent predictions about the future.

Practical uses of predictive analytics include anticipating consumer behaviors and purchasing trends, streamlining operations, maximizing profits, identifying cases of fraud, and mitigating risk. Industries currently leveraging predictive analytics include finance, manufacturing, healthcare, technology, retail, education, government, energy, and insurance.

An important component of data is metadata, which is information about data. This includes who created it, when and where it was created, who has updated it, and its size and storage location. Metadata is important because it provides context to users, helps maintain data quality, and clarifies definitions. Data scientists and their colleagues are critical to maintaining and managing metadata; in their work, they complete tasks such as designing secure repositories, correcting metadata, and ensuring that technology can access the metadata as needed.

Average Salary for Data Science Graduates

According to July 2020 data from PayScale, the annual salary for data scientists in the lowest 10% earnings category is about $66,000, while the median annual salary is about $96,000. Earners in the top 10% report annual earnings of more than $134,000.

Salary ranges vary based on a variety of factors, including the industry of employment, where the position is located, and an employee’s experience, education level, and additional certifications. Examples of additional certifications include IBM Data Science Professional Certificate, SAS Certified Data Scientist, and Microsoft MCSE: Data Management and Analytics.

Careers for Data Scientist Majors

Data scientists are not just employed at big tech companies such as Apple, Amazon, Facebook, and Google. Data scientist majors can find work across a variety of industries, including automotive, healthcare, telecommunications, and energy. In-demand data science career paths include the following:

  • Applications architect. Applications architects help design and analyze all major aspects of various software applications. PayScale reports that as of July 2020, the annual bottom-range salary for professionals in this field is about $83,000; the median annual salary is about $113,400.
  • Business intelligence developer. Business intelligence (BI) developers create BI reports and tools. They also design and develop data-mining solutions. PayScale reports that as of July 2020, the annual bottom-range salary for professionals in this field is about $56,000; the median annual salary is about $79,900.
  • Data engineer. Data engineers develop the computer algorithms that collect and prepare big data that is analyzed by data scientists. PayScale reports that as of July 2020, the annual bottom-range salary for professionals in this field is about $65,000; the median annual salary is about $92,200.
  • Enterprise architect. Enterprise architects are responsible for making sure that an organization is using the proper technology to achieve its goals. PayScale reports that as of May 2020, the annual bottom-range salary for enterprise architects was about $93,000; the median annual salary was about $130,200.
  • Machine learning engineer. Machine learning engineers design self-running software used to create predictive models. Each time the application runs, its predictive models become more accurate. PayScale reports that as of July 2020, the annual bottom-range salary for machine learning engineers is about $75,000; the median annual salary is about $111,900.

Job Outlook for Data Scientist Majors

The U.S. Bureau of Labor Statistics (BLS) puts data scientists under the category of mathematicians and statisticians. The BLS projects that overall employment in that field will grow by 30% between 2018 and 2028, which is much faster than projections for the average occupation.

Data scientists are in high demand due to the proliferation of information and many industries’ increasing reliance on information technology (IT) and data to make decisions. In a data scientist role, professionals tend to perform activities such as:

  • Managing data and leveraging their analytical abilities to turn raw information into understandable, actionable takeaways for lay audiences
  • Making data-driven recommendations to solve complex problems for businesses and organizations of all types
  • Following data science trends and remaining up to date on best practices and emerging technologies in software, AI, and analytics
  • Leveraging knowledge in mathematics, statistics, and computer programming to maximize efficiency, identify patterns, increase profits, and draw useful conclusions from data
  • Communicating with colleagues in the IT and business sectors to ensure that findings are clear and actionable

Because they are often equipped with specialized technical knowledge, data scientist majors can graduate from bachelor’s degree programs with a wealth of exciting career opportunities available to them.

Consider a Major in Data Science

Data scientists play an invaluable role in their organizations, and they enjoy work that keeps their minds engaged and requires them to use problem-solving skills. Their services are also in high demand due to a national shortage of data scientists. A strong employment outlook, coupled with a versatile skill set, could lead to a number of excellent opportunities for those who major in data science. Enroll in Maryville University’s online Bachelor of Science in Data Science and take the first step toward a fulfilling, rewarding career with a number of opportunities. 

Recommended Reading

The Top Data Science Skills to Develop for Business

Big Data and Data Mining: The Role Data Mining Plays in Big Data

The Most Popular Programming Languages and Their Applications 

Sources

CIO, “15 Data Science Certifications That Will Pay Off”

Data Science Central, “Artificial Intelligence vs. Machine Learning vs. Deep Learning”

Forbes, “Bridging the Data Scientist Talent Gap Starts with Defining the Current Role”

PayScale, Average Application Architect Salary

PayScale, Average Business Intelligence (BI) Developer Salary

PayScale, Average Data Engineer Salary

PayScale, Average Data Scientist Salary

PayScale, Average Enterprise Architect Salary

PayScale, Average Machine Learning Engineer Salary

SAS, Predictive Analytics: What It Is and Why It Matters SAS, What Is a Data Scientist?

TechJury, “How Much Data is Created Every Day in 2020?”

TechTarget, “The Benefits of Metadata and Implementing a Metadata Management Strategy”

U.S. Bureau of Labor Statistics, Mathematicians and Statisticians