Why Software and Electrical Engineers Can Benefit from Studying Applied Statistics
What is applied statistics?
Applied statistics is the foundation of data analysis. With more access to data than ever before, companies are looking for data-driven leaders who can make sense of this treasure trove of information and turn it into something meaningful.
Data engineers, quality control analysts, and other engineering-related professionals use applied statistics methods and theories to understand real-world data, solve production-related problems, monitor quality control, and more. Professionals across all fields of engineering (electrical, civil, mechanical, chemistry, and software) are performing experiments, testing processes and products, and analyzing data on a daily basis.
Quality assurance is the crux of an engineer’s role – and using applied statistics to analyze quality is a powerful way to create a successful product, program, or process.
Engineers looking to move up in their respective fields may want to study applied statistics: A strong understanding of applied statistics can help professionals gain the knowledge needed to analyze observed data. These skills are crucial to have when applying for an array of senior-level statistical engineer and data engineer positions.
Where do people with applied statistics master’s degrees work?
After pursuing a master’s degree in applied statistics, many professionals work as data analysts, data scientists, statisticians, and more. There are myriad of job opportunities across multiple industries for professionals with an applied statistics background. This includes the world of engineering, where applied statistics comes into play often.
Engineers working in electronics, chemicals, aerospace, pollution control, construction, and more all use applied statistics to help with quality assurance: They may be finding new ways to minimize waste, for example, or to predict product life and potential problems. Electrical engineers working in consumer electronics use reliability theories during product design to reduce a product’s risk of failing or being problematic.
Other engineering professionals use applied statistics to figure out ways to harness the power of sustainable and renewable energy. Industrial statisticians use applied statistics to help develop and test new manufacturing products, And when it comes to differentiating between random prototypes when designing a new auto part or software program, applied statistics can help analyze the data from each of the models.
Meanwhile, having applied statistics expertise is beneficial for software engineers, as more and more data becomes accessible when designing new software products. Software engineers with a background in applied statistics are becoming more sought after.
Here’s one powerful example of applied statistics at work in engineering: Sandia National Laboratories recently performed a Power Electronics Reliability Analysis, where they collected field maintenance data and used it to calculate reliability metrics. They used applied statistics to create an advanced model of equipment failure, and were able to trace back equipment failure to a specific time and part.
This use of relational databases and other applied statistics methods to define the relationship between failure and maintenance is a time-saving, cost-cutting tool for many companies; they can estimate future costs, provide accurate downtimes for when equipment fails, and ultimately, even catch problems before they occur.
What skills do statisticians and data engineers need?
The number one skill a successful statistician, data engineer, or statistical engineer needs? A mastery of applied statistics. These professionals take large sets of data and figure out how that information can improve processes, cut costs, increase efficiency, and more. As more companies harness the power of leading with data, they’re looking for statisticians who can take the wheel with confidence.
A solid understanding of applied statistics involves being able to use advanced statistical theories and models (linear regression, predictive modeling, hypothesis testing, and more); understanding the software packages and programming languages used in data analysis (R, Python, Hadoop, and SAS, for starters); and being proficient in the data visualization skills used to help communicate data findings to key stakeholders.
Electrical and software engineers already think a lot like statisticians do: They’re both conducting experiments and gathering data to test how a product may perform during the design and development process. An applied statistics study introduces engineering professionals to the idea of variables and uncertainty.
Engineering majors usually take only one or two applied statistics courses, scraping the surface and only getting a quick overview of things like probability concepts, random variables, hypothesis tests, confidence intervals, linear regression, and inference.
An applied statistics master’s degree offers engineering professionals a deeper, more in-depth look into applied statistics theories and methods, advanced programming languages, and data visualization skills – essentially, everything that’s needed to create well-rounded statisticians, data analysts, and statistical engineers.
Why is applied statistics in demand?
Professionals with a robust understanding of data and high-level applied statistics skills are in high demand – not only for those interested in lucrative careers in software or electrical engineering, but for other industries too.
Businesses, public and private organizations, and other companies all want analysts, researchers, statisticians, and engineers who can help make data-driven decisions. According to the U.S. News and World Report, the second best job in the U.S. for 2019 is statistician, with a median salary of $84,060.
According to salary analysis website PayScale, data engineers make a median of $92,000. For software engineers, the field of data science is the top engineering job of 2019, says NewEngineer.com. Bottom line: The demand for professionals who can understand data will likely continue to grow as our access to data increases.
Am I a good fit for studying applied statistics?
If you’ve thought about studying applied statistics in the past, one of these statements about your professional or educational background may ring true:
You want to advance your engineering career. Maybe you’re interested in a more senior-level role, or a position as a statistical or data engineer. And perhaps you studied in an engineering-heavy undergraduate program, and you didn’t have the chance to dive into applied statistics, but you know it can help you be a better engineer.
Engineers more than likely already have pretty strong math backgrounds, but many senior-level engineering jobs look for graduate-degree-level candidates. An accelerated, math-focused applied statistics program allows for the time needed to study the concepts required to excel in data analysis.
You want to work on your data analysis and presentation skills to open up more job opportunities. Successful statisticians, data analysts, and data engineers must be able to analyze data, yes, but they’ve also got to be able to present that analysis to stakeholders who may not understand applied statistics as well. Data visualization and communications are both often covered in applied statistics programs.
You’re interested in the growing fields of data analysis and data science, but you need more quantitative skills. A background in software engineering gives you the edge of already having an understanding of programming languages like R and Python. And you likely already have a pretty solid mathematics background, which also helps here.
That said, most engineering students only take one or two statistics courses in their undergraduate programs. Through courses in probability, statistical inference, and regression analysis, students will learn the applied statistics skills they need to be solid candidates for data analysis roles. (You’ll also sharpen your programming, data visualization, and communication skills too, which doesn’t hurt.)
What’s covered in an applied statistics master’s degree program?
At its core, an applied statistics master’s degree program is focused on the quantitative: Courses revolve around high-level applied statistics topics such as linear regression, predictive modeling, regression analysis, computational statistics, time series analysis, and statistical data mining. Students use real-world, complete, and incomplete sets of data to perform practical data analysis and experiments.
Applied statistics programs also introduce computationally intensive statistic methods such as resampling and Monte Carlo simulation. They also tend to focus a solid amount of time on predictive modeling – the practice of using data mining and probability to forecast outcomes. Students learn how to use programming languages and software like R, Python, Hadoop, and SAS to contribute to their data collection and analysis.
Students should leave an applied statistics program with the confidence to approach a huge set of data, choose a statistical method that is appropriate for analyzing it, justify that choice theoretically, apply the method, and draw appropriate conclusions from it.
They’ll become familiar with the design, conduct, and analysis of statistical studies; be able to develop techniques that will help them work with the complexities and challenges of real data; and know how to communicate their results to non-statisticians.
What are the current job opportunities for data analysts and engineers?
Applied statistics master’s degree grads may be solid candidates for several statistics-, data analysis-, and quality analyst-related jobs. A background in engineering and applied statistics is a lucrative one. Here’s a look at a few opportunities based on recent online job postings.
With a background in both engineering and applied statistics, this professional uses their data analysis skills to solve manufacturing, production, and business problems, be it optimizing a production process or improving business workflow. (Think of this position as a data analyst, but for the electrical engineering or manufacturing industry.)
Statistical Software Engineer
These professionals use their applied statistics and software engineering backgrounds to control and improve the quality of software best practices. A current job listing seeks candidates who are comfortable performing statistical-testing models on current fraud identification software models to identify weakness and recommend improvements.
Quality Control Engineer/Quality Control Analyst
These professionals work to ensure the quality of a manufactured product by devising and defining quality tests, collecting data on product performance, and working to come up with solutions that will fix any issues or problems that arise. Quality Control Engineers tend to come from an engineering background, but advanced knowledge of applied statistics is beneficial in senior positions.
A validation engineer manages, inspects, calibrates, and tests production processes in highly regulated engineering industries, such as pharmaceutical or medical device manufacturing. They report and communicate their findings to ensure that the highest quality and safest products are being produced. For example, a current job listing for a validation engineer wants candidates who can use statistical models to analyze clinical studies to help in the manufacturing of commercial gene therapy products.
Skewing more towards data analytics and IT than engineering, these professionals work to ensure an uninterrupted flow of data between servers and applications. They track a company’s data retrieval, storage, and distribution processes and practices. Database management knowledge, SQl and Hadoop skills, and communications chops are desired. Data engineers use their applied statistics knowledge to analyze big data and build algorithms that improve access to data. A background in software engineering, computer science, and statistics is ideal.
Companies Currently Hiring Data Engineers
- Capital One
- The CIA
- First Republic Bank
- Mercedes Benz Research & Development
- Warner Bros.
Applied Statistics at Michigan Technological University
The online Applied Statistics Master of Science degree at Michigan Technological University sets out to give students access to a complete advanced-level statistics toolkit, preparing them to leave the 30-credit hour program as a data analysis pro. Throughout this program, students will combine statistical techniques with emerging technologies, familiarize themselves with industry software tools, and work with real data.
Students will also build the communication skills needed to present data findings to stakeholders – something employers don’t overlook. (Because although data analysts, data scientists, and data engineers should be applied statistics pros, they should also possess business acumen, be solid collaborators, and have great communication skills.)
Since MTU’s Applied Statistics is a completely online program, students and working professionals receive the flexibility they need to earn a graduate degree in a manner that makes the most sense for their own demanding schedules.