Let’s start with a definition of applied statistics: applied statistics is the root of data analysis. The practice of applied statistics involves analyzing data to help define and determine business needs. Modern workplaces are overwhelmed with big data and are looking for statisticians, data analysts, data scientists, and other professionals with applied statistics knowledge who can organize, analyze, and use data to solve real-world problems.
Properly analyzing company data can lead to marked increases in efficiency and profitability. In fact, studies show that companies employing data-driven marketing strategies are seeing up to 20% increases in revenue and 30% decreases in spending. Government agencies, nonprofits, and other organizations can use data to help prevent disease, collect important demographic information, steer political campaigns, and test potential life-saving pharmaceutical products.
As business leaders grow their understanding of the power of data for their companies, the exponential demand for statisticians and other professionals with advanced applied statistics skills will only continue to increase.
Applied Statistics vs. Data Science
As the root of data analysis, the study of applied statistics prepares professionals for careers as statisticians, data scientists, data analysts, and more. Applied statistics is a foundation upon which data science has been built. Through statistical methods, analysis, and an emphasis on real-world data, applied statisticians seek concrete solutions to tangible problems. Individuals with a strong background in applied statistics may then become data scientists, but the relationship doesn’t work inversely—those who study data science exclusively would not necessarily be prepared for careers as applied statisticians.
Depending on a company’s approach to statistics and data, they may position a data scientist and applied statistician on the same team or delegate their job responsibilities in overlapping ways. Applied statistics is anchored by the statistics themselves.
Data scientists, on the other hand, employ complex computing techniques, statistical inference, and machine learning (the science of teaching computers to analyze data as humans do) to extract information from large data sets. As data scientists observe trends and patterns, they can equip their companies to forecast potential problems, focus on areas ripe with growth potential, and make strategic, data-driven business decisions.
Data science is an ever-evolving field. You may find eight different job postings for data scientists and discover that all of them ask for a slightly different skillset. While you’ll want to bear in mind the type of position you’re interested in as a data scientist, a background in applied statistics can set you up for success in the field.
Applied Statistics vs. Statistics
Pure statistics focuses primarily on the numbers, math, and problems themselves. Applied statistics on the other hand, can be thought of as “statistics-in-action” or using statistics with an eye toward real-world problems and what their solutions might be. Statistics alone can be used pragmatically. However, in general, the emphasis of applied statistics tends to be more oriented toward practical benefits.
What Do Statisticians Do?
Statisticians use applied statistics to solve practical problems in today’s data-centric world. They decide what data they need to collect, determine how to collect that data, and then analyze and interpret the data using statistical tools, algorithms, and software. In short: statisticians take data and turn it into action.
Statisticians, data analysts, and other data professionals use applied statistics across a myriad of industries including business, marketing, media, finance, insurance, government, healthcare, manufacturing and engineering.
So how do these professionals employ applied statistics in such varying industries? Consider these examples of the multitude of ways applied statistics can be useful:
- studying the effectiveness of new pharmaceutical products
- affecting public policy
- understanding risks and returns in financial investments
- predicting the potential outcomes of political campaigns
- collecting market study research on online shopping habits
- determining stock market trends
- improving manufacturing processes
The friend recommendations you see on Facebook and the product suggestions that pop up on Amazon? Those are all generated using applied statistics. Google once used applied statistics to predict flu outbreaks based on search data; statistician Nate Silver analyzed data to come up with his election forecasts, and Netflix and Hulu analysts use viewership data to create algorithms that generate recommended content. From engineering to entertainment, applied statistics show up strong.
What Are Career Opportunities for Applied Statistics?
A wide variety of professional opportunities exist for students with applied statistics master’s degrees. The following list is just a selection of the many career paths available:
- Data Scientist
- Data Analyst
- Quality Engineer
- Manufacturing Engineer
- Statistical Engineer
- Validation Engineer
Finance and Accounting
- Risk Analyst
- Financial Analyst
- Quantitative Analyst
- Actuarial Director
- Financial Crimes Analyst
- Compliance Officer
- Machine Learning Researcher
- Intelligent Automation Technology Associate
- Statistical Programmer
- Data Architect
- Marketing Analyst
- Business Analyst
- Marketing Research Manager
Medical and Health
- Clinical Informatics
- Health Research Analyst
- Statistical Scientist
Science and Research & Development
- Cognitive AI Data Scientist
Why Are People with Applied Statistics Degrees in Such High Demand?
In 2009, Google chief economist Hal Varian predicted that “the sexy job in the next ten years will be statistician.” In the years since, Varian’s prediction has come true and continues to stretch even beyond the decade he foresaw.
Companies in all sectors of industry have greater access to data than ever before, and they need professionals skilled in applied statistics to turn that data into something meaningful. The Bureau of Labor Statistics (BLS) explains that, as industries like business, healthcare, and even policy turn to statistical analysis to inform their decision making, statistics professionals will be in high demand. The increase in data collection from online consumers also creates a need for people with applied statistics degrees to make sense of that data, helping companies leverage it in useful ways. To put it simply, the world has access to more data than we ever have. Now, we need professionals who can put that data to work.
Take a look at the U.S. News & World Report rankings for applied statistics careers:
- Data Scientist: #2 in Best Technology Jobs, #6 in Best Stem Jobs, and #8 in 100 Best Jobs overall.
- Statistician: #2 in Best Business Jobs, #5 in Best STEM Jobs, and #6 in 100 Best Jobs
- Operations Research Analyst: #5 in Best Business Jobs, #13 in Best STEM Jobs, #30 in 100 Best Jobs
- Computer Systems Analyst: #5 in Best Technology Jobs, #15 in Best STEM Jobs, #47 in 100 Best Jobs
The median salary for a statistician is $110,860 and the projected growth for the position is 35% between 2019–2029, according to the Bureau of Labor Statistics. For a data scientist, the BLS reports a salary of $98,230 and projected employment growth of 31%. For operations research analysts, it’s $86,200 and 25%. Computer systems analysts come in at $93,730 and 7%.
If you’re not quite sure whether or not an applied statistics degree is worth it for you, know that many companies look for candidates with graduate-level degrees. Studying applied statistics is a marketable way to gain the skills needed to transition into a more advanced data analysis role in your desired industry.
Is Studying Applied Statistics Right for Me?
Those who study applied statistics come from an array of undergraduate backgrounds, including mathematics, computer science, engineering, or data analysis and research. You’re a good fit for applied statistics if you’re seeking a career in a data-related field, or you already deal with data at your job and want to increase your skills and knowledge.
Many students or professionals interested in entering the lucrative field of data analytics and data science can benefit from studying applied statistics. If you fit into one of the following four groups, consider a master’s degree in applied statistics:
You have a background in mathematics. Mathematicians, math teachers, and those who have studied math in an undergraduate degree program are excellent candidates for transitioning into careers as statisticians.
Your background is in research and analytics. Data analysts are in demand in marketing, research, and more. Scientific researchers can segue into a career as a biostatistician, and market research analysts can increase their applied statistics skills.
You’re interested in becoming a data scientist. Students with a background in computer science or software programming often need to advance their applied statistics skills in order to enter the world of data science.
You work as an engineer. Software and electrical engineers use applied statistics with a high degree of regularity. An applied statistics degree can be helpful when pursuing advanced statistical engineer positions.
What Makes a Great Statistician in Today’s Data-centric World?
A great statistician uses programming languages and advanced applied statistics models to hunt for meaningful patterns in huge troves of data. They know how to take big data and figure out the how and the why within it. They approach applied statistics and quantitative analysis with a modern, programming-level approach and possess high-level applied statistics, programming, data visualization, communications, and business skills.
Beyond quantitative skills, great statisticians are also great collaborators. They may work alone while analyzing data, but they’re often part of a larger team of statisticians and data analysts. A key to data analysis: being able to explain that analysis to stakeholders who may not have as much applied statistics knowledge.
How Do You Become a Statistician or Data Scientist?
Statisticians and data scientists typically have degrees or professional backgrounds in mathematics, applied statistics, computer science, engineering, programming, or research. Studying applied statistics is a great first step as most applied statistics degree programs cover the essentials of data analysis: probability testing, statistical testing, hypothesis testing, parameter estimation, regression analysis, computational statistics, time series analysis, and forecasting, data mining, predictive modeling, and more.
Beyond learning applied statistics theories and methods, you’ll also need:
- data visualization skills
- knowledge of programming languages like R and Python
- experience with the SAS software suite
- an understanding of SQL database languages
An advanced applied statistics program focuses on developing a strategic mindset, technical aptitude, quantitative methods, business acumen, and connective communication. Prerequisites often require pre-calculus and calculus courses, and fundamental math proficiency.
Applied Statistics at Michigan Technological University
The Masters in Statistics online program at Michigan Technological University prepares students to meet today’s surging demand for data experts. The innovative program combines statistics theory and methodologies with emerging technologies and teaches students how to take data, interpret it in an efficient manner, and apply it to the real-world.
Coursework includes statistical theory and inference, probability theory, data mining and visualization, and programming languages. This 100% online, accelerated, and math-focused graduate degree program gives students the flexibility to complete coursework in a way that works best for their schedules. Many students study while working full-time, which provides them with the opportunity to apply new knowledge and skills immediately while setting their sites on career advancement or transitioning into a new field.
Students must complete 30 credit hours (ten courses) of graduate-level statistics study. Michigan Tech offers three start dates per year to accommodate busy professional schedules, and our accelerated semesters are just seven weeks long.
To take the next step, schedule a call with an enrollment advisor to discover how Michigan Tech could elevate your career.