What is applied statistics?
Applied statistics is the act of using statistical and mathematical methods to collect and analyze data. As more businesses gain access to huge amounts of data, they’re turning to professionals with backgrounds in applied statistics to make sense of it, find patterns within it, and use it to drive strategy and solve real-world business problems.
The field of statistics has evolved since it originated in the 1700s, made significant over the past four centuries by the likes of Ronald Fisher, Karl Pearson, and Florence Nightingale. As technology evolves, so too does statistics.
Today, applied statisticians are highly in-demand, as public organizations and private companies look for people who can make sense of the complex data at their fingertips. These professionals work as statisticians and data, financial, and quantitative analysts.
At its core, applied statistics is a field of mathematical study, which is why it’s a great fit for mathematicians and statisticians looking to advance their careers or enter the emerging worlds of big data and data science. Those with a foundation in math or statistics study applied statistics to learn the more complex statistical methods, software programming languages, and visualization skills needed to thrive as a data analyst.
Where do people with applied statistics masters’ degrees work?
Any business or organization that collects data can benefit from having a data analyst or statistician on its team, and that spans across practically every industry:
The U.S. government employs thousands of statisticians, mainly within the departments of Health and Human Services, Commerce, and Agriculture. This is because the government collects and analyzes vast amounts of data – the U.S. Census may be the most well known example of applied statistics at work.
But besides the census, government statisticians also analyze everything from unemployment rates and wages to the average level of pesticides in public drinking water and the number of invasive species living in a particular region.
In the finance industry, quantitative analysts use advanced statistical theories and applied statistics methods to help solve financial- and risk management-related problems. They use data to predict loan payment frequency, detect money laundering, figure out which customers to market to, and much more.
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Over in the insurance industry, actuarial statisticians use applied statistics for insurance purposes, be it to analyze future risk probabilities or to help determine and set the proper policy premiums and coverage rates.
Even professional sports teams employ statisticians: Remember the 2011 movie Moneyball? The film told the true story of Oakland As manager Billy Beane, who teamed up with statistician Peter Brand in the early 2000s to build a winning baseball team by analyzing performance data to predict which players would perform well when. Since then, it’s not uncommon to see a statistician in every major baseball clubhouse.
Some statisticians dedicate their work to improving the fields of statistics and applied statistics by identifying existing methods that are inadequate and developing and evaluating new statistical algorithms. This field – typically known as mathematical statistics – sees statisticians continuing to work in research and academia post-graduation.
What skills do statisticians need?
Those looking to excel in the field of applied statistics need high-level statistical skills and an advanced knowledge of statistical modeling theories – this includes linear regression, linear and nonlinear models, predictive modeling, regression analysis, computational statistics, time series analysis, and more.
Applied statisticians need to know their way around software packages such as SAS and programming languages like R and Hadoop – tools often used to collect and analyze big data. They need to be excellent critical thinkers and great communicators: One of a statistician’s most important roles is being able to effectively communicate data findings to executives and other decision makers who may have a limited understanding of statistics. (This communication is often done through data visualization software.)
Those interested in a graduate-level applied statistics program should have a strong background in math and a passion for diving deep into the world of statistics. Students and professionals coming from a mathematics background likely already have advanced quantitative knowledge and great problem solving and critical thinking skills, which will be beneficial when learning software programming and statistical modeling theories.
Most undergraduate math majors have a pure, traditionally math-centric coursework – think linear algebra, statistics theory, and differential equations. It’s a solid foundation for studying applied statistics and pursuing a career as a data analyst or a statistician, but these people can really benefit from an advanced degree in applied statistics.
In an applied statistics master’s degree program, they’ll learn high-level statistical methods; how to understand statistics solutions in a way that relates to business; how technology and software tools are used in data analysis; and they’ll build skills in collaboration, communication, and data visualization.
- Applied Statistics pros
- Programming whizzes
- Machine-learning gurus
- Data-wrangling fiends
- Data visualization masters
- A+ communicators
Why is applied statistics in demand?
Applied statistics is one of the country’s most in-demand career fields – not only just for those with a background in mathematics, but for other professionals too.
As more companies realize the power of big data, professionals with data analytics, statistics, and data science skills are in huge demand: The U.S. Bureau of Labor Statistics predicts that the demand for statisticians will grow 33 percent by 2026.
According to a recent study by the Society for Human Resource Management, 65 percent of agencies surveyed had increased the number of positions they have for employees with data analytics (and applied statistics) skills over the past five years; another 59 percent expect to further increase their need for data analysts within the next five years.
Simply put: Companies want statisticians. Making the leap from a mathematics background to an applied statistics one can end up being a lucrative decision. The median salary for a statistician is $75,600, according to the Bureau of Labor Statistics. (And statisticians with master’s degrees in applied statistics are typically more attractive candidates for more senior – and higher-paying – positions.)
Am I a good fit for studying applied statistics?
If you’re interested in applied statistics, you’ll likely agree with the following statements:
You’re a mathematician or a mathematics undergraduate student with sharp quantitative skills. Maybe you’re a high-school calculus teacher, for example, or you have a strong background in high-level mathematics. You don’t need a statistics degree to study applied statistics, but you should have strong math skills and the often-required calculus prerequisites.
You want to dive into the world of data analysis. You’d like to learn how to solve problems using advanced statistical theories. You’re ready to study applied statistics because you want to learn how to collect data – then harmonize, rescale, clean, and redistribute it. You want to identify, run, and calibrate data models.
Data analysts must be well versed in data mining, which is the practice of taking various statistical methods, data analysis tools, and machine learning techniques to explore and analyze large data sets. Data mining’s goal is to find new patterns and useful information within data that can help a business reach its goal – be it financial, for public policy, or scientific research. Data mining is a big part of an applied statistics program.
You’ve got basic statistics skills, but want to be the complete data analyst or data scientist package. Beyond quantitative skills, today’s successful statisticians, data analysts, and data scientists are proficient in multiple programming and software languages and data visualization, which is all covered in an applied statistics program.
What’s covered in an applied statistics master’s degree program?
There’s quite a big difference between having a basic understandings of statistical theory and being able to apply those theories, inferences, and models in a practical, real world, and problem-solving manner.
An applied statistics master’s degree program teaches students how to take their advanced statistical knowledge and turn it into a must-have asset for a company. Students go beyond the abstracts of math and study real-world statistical conundrums – they’ll use real data and learn how to turn it into valuable insight.
Most applied statistics programs focus heavily on advanced statistical theories first and foremost, including linear regression, probability, hypothesis testing, data mining (identifying patterns in data), predictive modeling (the use of data mining and probability to forecast outcomes), regression analysis, computational statistics (the intersection of statistics and computer science), and time series analysis (or trend analysis).
Throughout the course of studying applied statistics – all while using real-world data sets – students will also learn the programming languages and software tools that statisticians working with big data must have proficiency with, including SAS, R, Python, and Hadoop. They’ll also sharpen their data visualization and communications skills – both crucial when working to influence data-driven change in any organization.
What are some current job opportunities for statisticians?
People with a master’s degree in applied statistics may be solid candidates for an array of statistics-related jobs. These are a few of the most common job opportunities that may be relevant, based on recent online job postings.
Statistician and Applied Statistician
These professionals possess a strong knowledge of applied statistics, and they apply high-level data collection, analysis, testing, and visualization to solve real-world problems.
You’ll find statistician, applied statistician, and senior statistician jobs available in an array of industries, including finance, healthcare, education, professional sports, government, nonprofit, and more. Employers look for statisticians with master’s degrees in applied statistics, extensive knowledge of programming languages and software like R and SAS, and solid communication skills.
These analysts work in the insurance industry, using applied statistics models to collect data and provide insight into insurance-related events – property damage, accidents, product failure, and more. They use probability models to estimate the chance of a catastrophic event and they help determine insurance policy pricing.
Those looking for actuarial analyst roles should have degrees in mathematics, applied statistics, or economics, plus advanced quantitative analytical knowledge, SAS skills, and solid communications chops. (Many applicants are also required to take the entrance exam to become at minimum an associate member of the Casualty Actuarial Society.)
These are statisticians who focus primarily on economics – researching, collecting, analyzing, and forecasting trends as they relate to commerce and business trends. This can include everything from energy costs and interest rates to farm prices and employment statistics.
Most economic analyst jobs are specific to a certain industry, though there are some economics-consulting firms that hire analysts to work with private organizations on a case-by-case basis. Current job postings for economic analysts seek candidates with advanced degrees in applied statistics, mathematics, economics, or computer science, plus knowledge of SQL, R, or Python.
These analysts use statistics to analyze the financial market and help private financial firms (investment banks, private hedge funds, and other trading firms) set prices for their securities. Often known as “quants,” they’re in high-demand on Wall Street, as the world of financial securities becomes more complex and data becomes more accessible.
Ideal quantitative analyst candidates will generally have degrees in mathematics, applied statistics, or engineering; experience working with programming, data analytics, and quantitative modeling; and a strong knowledge of SQL, R, and Python.
Companies Currently Hiring Statisticians
- Bureau of Naval Personnel
- Department of Energy
- Ford Motor Company
- Pratt & Whitney
- State of South Carolina
Applied Statistics at Michigan Technological University
Michigan Technological University’s online Master of Science degree in Applied Statistics gives students the full toolkit needed to pursue lucrative, advanced-level careers as statisticians and data analysts. The math-focused program is a deep dive into statistical theory and statistics software programming.
Throughout this 30-credit-hour applied statistics program, students combine tested statistical techniques with emerging technologies, become familiar with industry software tools, overcome common challenges in real datasets, and build the professional skills needed to be able to communicate data analysis with consistency and clarity. Accelerated, online learning gives students the flexibility to study while working full-time.
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