Study Applied Statistics 

What Is Applied Statistics?

Applied statistics is a uniquely analytical career field. Students who study applied statistics build critical-thinking and problem-solving skills in data analysis and empirical research, preparing themselves for work in a variety of industries — from engineering to healthcare and beyond. If you’re interested in managing, analyzing, interpreting, and drawing conclusions from data, then a degree with a focus on applied statistics might be for you. 

Applied statistics involves analyzing data to help define and determine business needs. While potentially bringing you closer to your career goals, an applied statistics master’s degree program teaches students how to take their advanced statistical knowledge and complex quantitative skills and turn them into must-have assets for companies — big and small. 

Statisticians, data analysts, and other data professionals use applied statistics across industries to solve practical problems in today’s data-centric world. Applied statistics has a wide variety of uses, from studying the effectiveness of new pharmaceutical products to improving efficiency in marketing and sales. Government agencies and nonprofits can even use data to help prevent disease, collect demographic information, and steer political campaigns. And knowing your numbers pays off: according to a 2018 study conducted by the Boston Consulting Group and Google, companies that employed data-driven marketing strategies saw a 20 percent increase in revenue and a 30 percent decrease in spending

As business leaders grow their understanding of the power of data for their companies, the need for statisticians and other professionals with advanced applied statistics skills is becoming apparent. A wide variety of opportunities may be available to graduates with degrees with a focus on applied statistics. Such career paths include:

  • Business
    • Statistician
    • Data scientist
    • Data analyst
  • Engineering
    • Quality engineer
    • Statistical engineer
    • Validation engineer
  • Finance and Accounting
    • Risk analyst
    • Financial analyst
    • Quantitative analyst
    • Actuarial director
    • Financial crimes analyst
    • Compliance officer
  • Information Technology
    • Machine learning researcher
    • Intelligent automation associate
    • Statistical programmer
    • Data architect
  • Marketing
    • Marketing analyst
    • Business analyst
    • Marketing research manager
  • Medical and Healthcare
    • Biostatistician
    • Clinical informatics specialist
    • Health research analyst
    • Statistical scientist
  • Science and Research & Development 
    • Modeler
    • Cognitive AI data scientist

Study Applied Statistics with a Master’s in Data Science

Both data science and applied statistics are rooted in and related to the field of statistics. Applied statistics is the foundation on which data science has been built, and both make big data relevant to businesses and industries. Much of the core courses and training designed for professionals in data science, statistics, and analytics are based on similar statistical education. Fields that involve analyzing data-based findings or results often leverage trained operations research analysts, statisticians, and scientists to interpret and report information. 

Earning a master’s in data science may be a viable choice for professionals interested in learning how to data mine in order to make predictions and data-driven decisions. Data science is an emerging field that continues evolving, and data science technology has the power to differentiate and optimize the way business leaders approach decisions, processes, and the future. Data scientists employ complex computing techniques to extract information from large data sets, help companies forecast potential problems, focus on areas with growth potential, and make strategic, data-driven business decisions. 

A master’s in data science provides students with the ability to recognize patterns in data and to skillfully obtain, continually reorganize, and manage data. While data science is rooted in statistics, applied statistics takes a mathematical approach to analyzing and solving problems with gathered data. Applied statistics is useful for solving real-world problems and drawing conclusions for business decisions. 

Applied Statistics Course

The Master of Information and Data Science (MIDS) program delivered online from the UC Berkeley School of Information prepares data science professionals to be leaders in the field. The WASC-accredited program features a multidisciplinary curriculum that draws on insights from the social sciences, computer science, statistics, management, and law.

While all universities and programs are different, courses are designed to provide students with an understanding of how data science is used to inform decision making in organizations. The MIDS curriculum features a wide range of core and elective courses including:

  • Introduction to Data Science Programming: This course is an introduction to the Python programming language. It highlights a range of Python objects and control structures. 
  • Research Design and Application for Data and Analysis: This course introduces students to the burgeoning data science landscape, with a particular focus on learning how to apply data science reasoning techniques to uncover, enrich, and answer questions facing decision makers across a variety of industries and organizations. 
  • Statistics for Data Science: This course provides students with an understanding of many different types of quantitative research methods and statistical techniques for analyzing data.
  • Fundamentals of Data Engineering: This course delves into the fundamentals of data storage, retrieval, and processing systems in the context of common data analytics processing needs. 
  • Applied Machine Learning: This course covers the rapidly growing field that is machine learning. The goal of this course is to provide a broad introduction to the key ideas in machine learning through intuition and practical examples. 
  • Experiments and Causal Inference: This course introduces students to experimentation and design-based inference. Students are taught to collect data in a way that’s creative and forward looking. 
  • Machine Learning at Scale: This course teaches students how to apply crucial machine learning techniques to solve problems, run evaluations and interpret results, and understand scaling up from thousands of data points to billions. 
  • Data Visualization: This course focuses on the design of visual representations of data in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. Students in this course gain the practical knowledge needed to create effective tools for both exploring and explaining data. 

Degree Information

The multidisciplinary online Master of Information and Data Science program educates burgeoning data science leaders by preparing them to derive insights from real-world data sets, use the latest tools and analytical methods, and interpret and communicate their findings in ways that change minds and behaviors. The core curriculum focuses on research design, data cleansing, data engineering, information ethics and privacy, statistical analysis, and other essential and specialized skills data science professionals typically use in the field. 

With flexible program paths, it’s designed for the working professional’s schedule and can be completed through one of three paths: accelerated, standard, or decelerated:

  • The accelerated path allows students to take three courses per semester to finish the program in as few as 12 months. 
  • The standard path allows students to take two courses a semester and finish the program in 20 months. 
  • The decelerated program allows students to take one course per semester (after the first term) and finish the program in no more than 32 months. 

Last updated November 2021.

Develop the Skills Needed to Become a Data Science Leader at Top Organizations

Earn a Master of Information and Data Science online from UC Berkeley.