An Introduction to Applied Statistics
Applied statistics is the root of data analysis, and the practice of applied statistics involves analyzing data to help define and determine business needs. With today’s increased access to big data, companies are looking for statisticians, data analysts, data scientists, and other professionals with applied statistics knowledge who can visualize and analyze data, make sense of it all, and use it to solve real-world problems.
Companies have so much data, and properly analyzing it can lead to increased efficiency and profitability. 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.
Data is a huge asset, and its growth has led to the overwhelming demand for statisticians and other professionals with advanced applied statistics skills.
What is the difference between applied statistics and data science?
Data scientists use complex computing techniques, statistical inference, and machine learning (the science of teaching computers to analyze data as humans do), to extract information from huge sets of data to help address trends and patterns, forecast potential future problems, and make business decisions.
Data science is rooted in applied statistics, but is more of an extension of the field – it tends to focus more on machine learning, software programming, and database management, while applied statistics is rooted in statistics. That said, it’s not uncommon to see statisticians and data scientists working on the same team, and job roles and responsibilities can overlap, depending on what a company is hiring for.
Data science is a pretty new career field. Two employees at Facebook and LinkedIn coined the title “data scientist” in 2008. They were building data and analytics teams at the companies and didn’t feel like “business analysts,” “data analysts,” or “research scientists” were what they were looking for. Enter the data scientist, and since then, the position has skyrocketed in popularity and demand. (The Harvard Business Review once called it “the sexiest job of the 21st century.”)
Since it is so new, data science is definitely still an evolving field. You may find eight different job postings for data scientists all asking for a slightly different skillset. That said, a background in applied statistics is a common path toward a career in data science.
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 and how to collect that data; and then they analyze and interpret it using statistical tools, algorithms, and software. 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.
They use data to study the effectiveness of new pharmaceutical products, affect public policy, understand risks and returns in financial investments, predict the potential outcomes of political campaigns, collect market study research on online shopping habits, determine stock market trends, improve manufacturing processes, and so much more.
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 recommend content.
Why are people with applied statistics degrees in such high demand?
In 2009, Google chief economist Hal Varian predicted, “the sexy job in the next ten years will be statistician.” His prediction is coming true. As businesses receive unprecedented access to data – we create 2.5 quintillion bytes of it every day, says IBM – they need statisticians to use applied statistics to turn that data into something meaningful.
According to SmartAsset, statistician is the top job for millennials. In the United States, 44 percent of statisticians are millennials and the median age is 35. Statistician currently ranks as the No. 1 position on the U.S. News and World Report’s Best Business Jobs list. And according to the Bureau of Labor Statistics: statistics-related jobs will grow 33 percent by 2026 – that’s more than 12,000 jobs in the next several years.
If you’re wondering – is a master’s degree in applied statistics worth it? – know that many companies do look for candidates with graduate-level degrees and that studying applied statistics is a solid way to gain the skills needed to transition into a more advanced data analysis role in your desired industry.
Am I a good fit for studying applied statistics?
Those who study applied statistics come from an array of undergraduate backgrounds – mathematics, computer science, engineering, or data analysis and research. You’re a good fit if you’re seeking a career in a data-related field, or you already deal with data on at your job and think you can benefit from a higher-level knowledge of applied statistics.
Many students or professionals interested in entering the lucrative field of data analytics and data science can benefit from studying applied statistics. If you have a passion for math and data, it’s a natural step. If you fit into one of the following four groups, it’s worth learning more about considering 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 a career as a statistician.
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 marketing 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 more advanced applied statistics skills to enter the world of data science. Applied statistics is a fundamental root of data science, and knowledge of it is beneficial.
You come from an engineering background. Software and electrical engineers use applied statistic more than you may think. 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 patterns that matter in huge troves of data. They know how to take big data and figure out the how and the why within it. They’re approaching applied statistics and quantitative analysis with a modern, programming-level approach. They possess high-level applied statistics skills, excellent programming knowledge, strong data visualization, and communications skills, and have solid business acumen.
Beyond the 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. One huge aspect of analyzing data is also 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, and research. Studying applied statistics is a great first step – 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; and more.
An advanced applied statistics program focuses on strategic mindset, technical aptitude, quantitative methods, business acumen, and connective communication. Prerequisites often require pre-calculus and calculus courses, and fundamental math proficiency
What is the job market for those who study applied statistics?
Countless 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
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 program combines statistics theory and methodologies with emerging technologies and teaches students how to take real data and interpret it in an efficient manner.
Courses cover statistical theory, statistical inference, probability theory, data mining and visualization, programming languages, and more. This online, accelerated, and math-focused graduate degree program gives students the flexibility to complete coursework on their own time, in a way that works best for their schedules. Many students study while working full-time, applying skills that may immediately be relevant in their current positions or can help them advance or transition into a new field.
Students must complete 30 credit hours (ten courses) of graduate-level statistics study. At MTU, students may begin the Applied Statistics MS program three times a year – accelerated semesters are seven weeks long. For more information, fill out our online form or schedule a call with an enrollment advisor on your schedule.