How to Get Into Data Analytics for Engineering

Data analytics plays a critical role in helping organizations across diverse industries make informed decisions. As demand for data-driven insights continues to grow, Fortune Business Insights projects the global data analytics market to reach $496 billion by 2034, representing a compound annual growth rate of 21.5%. By analyzing data, professionals can identify trends, enhance efficiency, manage risk, and improve outcomes in complex environments. 

Data analytics can be especially valuable in fields such as engineering and healthcare. Engineering professionals use data to evaluate system performance and keep projects on schedule and within budget, while healthcare organizations rely on data analysis to improve patient satisfaction, allocate resources, and enhance patient safety. 

For individuals considering careers in these fields, understanding how to get into data analytics can be an important first step — and several master’s degree paths can develop those skills. Some professionals may choose engineering management with a focus on data analytics, while others may pursue a data science or healthcare data analytics program.

What Is Engineering Data Analysis?

In its broadest sense, engineering data analysis involves extracting insights from data that can be used to guide decision-making on an engineering project. For example, engineers can rely on the results of data analysis to do the following:

• Improve work breakdown structures
• Detect anomalies and make prompt corrections
• Minimize project downtime
• Optimize resource allocation
• Prepare meaningful project status reports for stakeholders
• Create data visualizations that stakeholders can use when making project decisions

To conduct data analysis for engineering projects, professionals do the following:

• Work to identify the data needs of the engineering project
• Identify, filter, and prepare the data for analysis
• Perform statistical analysis of the data
• Monitor key performance indicators in the data
• Recognize and interpret patterns and trends in the data
• Create dashboards and other data visualizations for use in decision-making

To perform many of these tasks, data analytics professionals need to be proficient in using data analysis tools and software. For example, being able to use programs such as Tableau, which is used to create dashboards and other data visualizations, and coding languages such as Python, which features relatively easy syntax for data analysis, is beneficial.

Beyond individual engineering projects, these same data analysis practices are applied across industries that rely on complex systems and operational efficiency. The manufacturing industry, for example, relies heavily on data to improve production efficiency, monitor equipment performance, reduce downtime, and lessen error-related costs. These priorities are why many organizations use engineering data analysis to improve manufacturing operations. 
 

Is It Difficult to Get Into Data Analytics for Engineering?

Knowing how to get into data analytics for engineering can make entering the field relatively straightforward.

While data analytics professionals take a variety of paths toward working in the profession, individuals who want to work in data analytics, specifically in engineering, can benefit from earning certain undergraduate and graduate degrees. The availability of online degree programs gives individuals who aspire to careers in engineering data analysis flexibility in earning the credentials they need.
 

Steps to Become an Engineering Data Analyst

To become an engineering data analyst, an individual can generally follow the steps outlined below.

Step 1: Earn a Relevant Undergraduate Degree

Earning an undergraduate degree in engineering, data science, statistics, or a related discipline is an important first step for those interested in pursuing a career as an engineering data analyst. It establishes a solid foundation of expertise needed by every professional in the field.

Step 2: Earn a Relevant Graduate Degree

While earning a graduate degree isn’t always required to work in data analysis, graduate programs offer students many benefits. For example, completing a master’s in engineering management degree program is a great way to learn about using data analysis in engineering project management.

Master’s in engineering management degree programs provide students with critical data analysis skills and crucial knowledge in areas such as working with cross-functional engineering teams, analyzing supply chains, and meeting quality standards in engineering projects.

Step 3: Gain Experience

After completing the necessary education, individuals can start putting their expertise to use as data analysts or engineers. Starting in entry-level positions and progressing on the career path, individuals can benefit from on-the-job opportunities to enhance their skills and take advantage of work-related professional development training.

Step 4: Develop Key Skills for Engineering Data Analysis

Through earning an advanced degree and gaining experience in the field, individuals who work in engineering data analysis benefit from cultivating skills in areas such as:

• Data analytics tools, software, and programming languages
• Critical thinking
• Organization and the ability to work on multiple assignments
• Data visualization
• Problem-solving
• Attention to detail

Step 5: Consider Earning Certifications

While earning certifications isn’t always required to work in engineering data analysis, obtaining certifications demonstrates an individual’s credentials and dedication to learning new skills.

Examples of common certifications in data analysis include:

• Associate certified analytics professional (aCAP) certification from the Institute for Operations Research and the Management Sciences (INFORMS)
• CompTIA Data+ certification from CompTIA
• Certified advanced analytics professional certification from SAS
   

What Is Healthcare Data Analytics?

While data analytics in engineering focuses on system efficiency and performance, healthcare data analytics centers on using data to improve patient safety, streamline operations, support evidence-based decision-making, and identify trends. Medical facilities collect and analyze health-related data, such as patient surveys and billing codes, to inform, modify, and implement policies. 

Healthcare data analysts may perform tasks such as:

• Analyzing hospital infection and readmission rates
• Evaluating treatment effectiveness
• Identifying trends in public health data
• Monitoring patient quality metrics 
   

Steps to Become a Healthcare Data Analyst

Individuals interested in healthcare data analysis may follow a pathway similar to that of other data analytics careers. 

Step 1: Earn a Relevant Undergraduate Degree

By earning an undergraduate degree in data science, statistics, computer science, health information management, or a related discipline, individuals can learn how data analysis and healthcare operations intersect. 

A master’s degree isn’t typically required, but it can support advancement into leadership positions and demonstrate specialized knowledge in healthcare data analytics. In competitive job markets or for more advanced roles, graduate programs, such as a master’s degree in data science or healthcare data analytics, can help candidates build deeper technical expertise and stand out to employers.

Step 2: Gain Healthcare Data Experience

Healthcare data analysts typically need at least two years of relevant experience. Common entry-level roles include medical office administrator, data assistant, and operations analyst. These positions help individuals build familiarity with medical coding and billing systems, office operations, regulatory requirements, and electronic health record systems. 

Step 3: Pursue a Certification

After gaining relevant professional experience, analysts may be eligible for the certified health data analyst (CHDA) credential from the American Health Information Management Association. This credential recognizes expertise in data interpretation and reporting, business needs, and healthcare data analytics. 
 

Data Analyst Salary and Job Outlook

Data from the U.S. Bureau of Labor Statistics (BLS) reveals attractive salaries and job prospects for individuals who want to work in healthcare or engineering data analysis. The BLS reports the following for data scientists:

• Median annual salary: $112,590 as of May 2024
• Projected job growth: 34% from 2024 to 2034 (which significantly exceeds the 3% growth projected for all occupations)

Individuals may prefer to use their data analytics skills while working primarily as engineering project managers. The BLS reports the following for architectural and engineering managers:

• Median annual salary: $167,740 as of May 2024
• Projected job growth:  4% from 2024 to 2034

Similarly, individuals interested in healthcare may choose to apply their data analytics skills in related roles. The BLS reports the following for health information technologists and medical registrars: 

• Median annual salary: $67,310 as of May 2024
• Projected job growth: 15% from 2024 to 2034

Learning About Data Analytics Can Lead to a Rewarding Career

Acquiring data analytics skills can equip individuals to pursue a fulfilling and meaningful career path across industries. From improving engineering project performance to supporting better patient outcomes in healthcare, data analytics helps organizations make more informed decisions.

Individuals interested in advancing their education can explore a range of graduate programs aligned with their career goals. For example, the online Master of Science in Engineering Management degree program at Tseng College at California State University, Northridge offers options to focus on engineering management or add a data analytics concentration. 

Students may also consider CSUN’s online Master of Science in Healthcare Data Analytics or online Master of Science in Data Science, which help develop advanced analytical skills for healthcare, technology, and other data-driven careers. 

Start advancing your data analytics knowledge with CSUN today.