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    • Applied Statistics

    Applied Statistics Courses Online

    Understand applied statistics for data analysis and interpretation. Learn statistical methods and tools for various industries.

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    Explore the Applied Statistics Course Catalog

    • J

      Johns Hopkins University

      Statistical Inference

      Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

      4.2
      Rating, 4.2 out of 5 stars
      ·
      4.4K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Analyst

      Skills you'll gain: Data Storytelling, Dashboard, Data Visualization Software, Plotly, Data Wrangling, Data Visualization, SQL, Generative AI, Interactive Data Visualization, Exploratory Data Analysis, Data Cleansing, Big Data, Jupyter, Matplotlib, Data Analysis, Statistical Analysis, Pandas (Python Package), Data Manipulation, Excel Formulas, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      91K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of Colorado Boulder

      Data Science Foundations: Statistical Inference

      Skills you'll gain: Statistical Hypothesis Testing, Probability, Probability & Statistics, Probability Distribution, Statistical Inference, Statistical Methods, Bayesian Statistics, Sampling (Statistics), Data Ethics, Data Science, Statistical Analysis, Quantitative Research, Descriptive Statistics, Statistics, Correlation Analysis

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      322 reviews

      Intermediate · Specialization · 3 - 6 Months

    • J

      Johns Hopkins University

      Data Science

      Skills you'll gain: Shiny (R Package), Rmarkdown, Exploratory Data Analysis, Regression Analysis, Leaflet (Software), Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Data Wrangling, Data Visualization, Plotly, Machine Learning Algorithms, Plot (Graphics), Knitr

      4.5
      Rating, 4.5 out of 5 stars
      ·
      51K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      Duke University

      Data Analysis with R

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Inference, Exploratory Data Analysis, Regression Analysis, Statistical Reporting, Probability Distribution, Statistical Methods, Data Analysis Software, R Programming, Bayesian Statistics, Statistical Analysis, Data Analysis, Statistical Software, Statistical Modeling, Probability & Statistics, Probability, Statistics, Correlation Analysis, Data Literacy

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.5K reviews

      Beginner · Specialization · 3 - 6 Months

    • G

      Google Cloud

      Architecting with Google Compute Engine

      Skills you'll gain: Cloud Infrastructure, Google Cloud Platform, Identity and Access Management, Prompt Engineering, Kubernetes, Application Deployment, Cloud Storage, Infrastructure As A Service (IaaS), Containerization, Cloud Computing, CI/CD, Cloud Computing Architecture, Cloud Security, Cloud Services, Load Balancing, Service Level Agreement, Infrastructure as Code (IaC), Virtual Machines, Microservices, Service Level

      4.7
      Rating, 4.7 out of 5 stars
      ·
      58K reviews

      Intermediate · Specialization · 3 - 6 Months

    • U

      University of Michigan

      Applied Machine Learning in Python

      Skills you'll gain: Feature Engineering, Applied Machine Learning, Supervised Learning, Scikit Learn (Machine Learning Library), Predictive Modeling, Machine Learning, Decision Tree Learning, Unsupervised Learning, Dimensionality Reduction, Random Forest Algorithm

      4.6
      Rating, 4.6 out of 5 stars
      ·
      8.6K reviews

      Intermediate · Course · 1 - 4 Weeks

    • D

      Duke University

      Inferential Statistics

      Skills you'll gain: Statistical Hypothesis Testing, Statistical Inference, Statistical Reporting, Statistical Methods, R Programming, Statistical Software, Statistical Analysis, Probability & Statistics, Data Literacy, Sampling (Statistics), Probability Distribution, Software Installation

      4.8
      Rating, 4.8 out of 5 stars
      ·
      2.7K reviews

      Beginner · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Probability & Statistics for Machine Learning & Data Science

      Skills you'll gain: Descriptive Statistics, Bayesian Statistics, Statistical Hypothesis Testing, Probability & Statistics, Sampling (Statistics), Probability Distribution, Probability, Statistical Inference, A/B Testing, Statistical Analysis, Statistical Machine Learning, Data Science, Exploratory Data Analysis, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      558 reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      IBM DevOps and Software Engineering

      Skills you'll gain: User Story, CI/CD, Istio, Open Web Application Security Project (OWASP), Continuous Integration, Software Development Life Cycle, Linux Commands, Kubernetes, Unit Testing, Cloud Computing Architecture, Application Deployment, Test Driven Development (TDD), Gherkin (Scripting Language), Jenkins, Bash (Scripting Language), Agile Software Development, DevOps, Git (Version Control System), Jupyter, Grafana

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      59K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: Free
      Free
      U

      University of Cape Town

      Understanding Clinical Research: Behind the Statistics

      Skills you'll gain: Biostatistics, Statistical Hypothesis Testing, Statistical Methods, Probability & Statistics, Clinical Research, Statistical Analysis, Quantitative Research, Descriptive Statistics, Statistical Inference, Data Collection, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      3.6K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Michigan

      Python 3 Programming

      Skills you'll gain: Unified Modeling Language, JSON, Object Oriented Programming (OOP), Software Design, Debugging, Object Oriented Design, Data Processing, Web Scraping, Unit Testing, Programming Principles, Data Import/Export, Restful API, Python Programming, Image Analysis, Data Manipulation, Jupyter, Maintainability, Data Structures, Software Engineering, File Management

      4.8
      Rating, 4.8 out of 5 stars
      ·
      23K reviews

      Beginner · Specialization · 3 - 6 Months

    Applied Statistics learners also search

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    1…567…197

    In summary, here are 10 of our most popular applied statistics courses

    • Statistical Inference: Johns Hopkins University
    • IBM Data Analyst: IBM
    • Data Science Foundations: Statistical Inference: University of Colorado Boulder
    • Data Science: Johns Hopkins University
    • Data Analysis with R: Duke University
    • Architecting with Google Compute Engine: Google Cloud
    • Applied Machine Learning in Python: University of Michigan
    • Inferential Statistics: Duke University
    • Probability & Statistics for Machine Learning & Data Science: DeepLearning.AI
    • IBM DevOps and Software Engineering: IBM

    Frequently Asked Questions about Applied Statistics

    Applied statistics is the use of statistical techniques to solve real-world data analysis problems. In contrast to the pure study of mathematical statistics, applied statistics is typically used by and for non-mathematicians in fields ranging from social science to business. Indeed, in the big data era, applied statistics has become important for deriving insights and guiding decision-making in virtually every industry.

    The increased reliance on data and statistics to help understand our world has made the careful application of these techniques even more essential; too often, statistics can be used erroneously or even misleadingly when methods of analysis are not properly connected to research questions. Thus, a major aspect of applied statistics is the accurate communication of findings for a non-technical audience, including specifics about data sources, relevance to the problem at hand, and degrees of uncertainty.

    That said, the statistical approaches used in this field are the same as in the study of mathematical statistics. Rigorous use of statistical hypothesis testing, statistical inference, linear regression techniques, and analysis of variance (ANOVA) are core to the work of applied statistics. And, as in other areas of data science, Python programming and R programming are often used to analyze large datasets when Microsoft Excel is not sufficiently powerful.‎

    Demand for data-driven insights is growing fast across all fields, making a background in applied statistics the gateway to a wide variety of careers. Financial institutions and companies of all kinds rely on business analytics to guide investments and operations; political candidates and advocacy groups need to conduct surveys and understand public polling data to understand popular opinion on today’s issues; and even sports teams are increasingly hiring experts in applied statistics to make decisions regarding personnel as well as in-game strategy.

    While many jobs in applied statistics may require only a bachelor’s degree in fields such as mathematics or computer science, high-level roles often expect a master’s degree in statistics. According to the Bureau of Labor Statistics, professional statisticians earn a median annual salary of $91,160 as of May 2019, and these jobs are expected to grow much faster than average due to the need to analyze fast-growing volumes of electronic data.‎

    Yes, with absolute certainty. Coursera offers courses and Specializations in applied statistics for business, social science, and other areas, as well as related topics such as data science and Python programming. These courses are offered by top-ranked universities and leading companies from around the world, including the University of Michigan, the University of Amsterdam, and the University of Virginia, and IBM. Regardless of whether you’re a student looking to learn more about this exciting field or a mid-career professional upgrading their skill set, the combination of a high-quality education and the flexibility of learning online makes Coursera a great choice.‎

    It's very helpful to have strong math skills, analytical skills, and experience solving problems before starting to learn applied statistics. It's also good to have experience and a good comfort level with technology and computers. Previous experience in statistics is also helpful, although not required. You may also benefit from having prior experience using Excel spreadsheets as you begin to learn applied statistics.‎

    People best suited for roles in applied statistics are analytical thinkers. They enjoy problem-solving by taking available data and analyzing it to arrive at solutions. They also have effective communication skills so that information can flow clearly to all stakeholders within an organization. Organization and multitasking come easily to people best suited for roles in applied statistics because these individuals need to deal with large amounts of information and manage their time and resources efficiently. People well suited for these roles also pay close attention to detail to make sure the outcomes they're tasked with delivering meet or exceed expectations.‎

    While the use of applied statistics can be found in almost every industry, learning applied statistics may be especially interesting to you if you're seeking a career in the insurance, web analytics, or energy sectors. These are some of the top industries that currently utilize applied statistics. However, a person in any position in which data is gathered and analyzed to create solutions, innovations, or improvements would benefit from learning applied statistics, from coaches and hospital administrators to bloggers, data scientists, and bankers. If you would like to know how to ensure you're collecting the right data, how to analyze data correctly, and how to effectively report your findings so they can be applied in real-world situations, learning applied statistics may be right for you.‎

    Online Applied Statistics courses offer a convenient and flexible way to enhance your existing knowledge or learn new Applied Statistics skills. With a wide range of Applied Statistics classes, you can conveniently learn at your own pace to advance your Applied Statistics career skills.‎

    When looking to enhance your workforce's skills in Applied Statistics, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.‎

    This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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