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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

    • N

      New York Institute of Finance

      Risk Management

      Skills you'll gain: Credit Risk, Operational Risk, Risk Management, Risk Management Framework, Business Risk Management, Risk Modeling, Risk Mitigation, Financial Market, Enterprise Risk Management (ERM), Risk Appetite, Risk Control, Derivatives, Governance, Portfolio Management, Risk Analysis, Capital Markets, Investment Management, Financial Analysis, Market Data, Key Performance Indicators (KPIs)

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.4K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Washington

      Machine Learning

      Skills you'll gain: Regression Analysis, Applied Machine Learning, Feature Engineering, Machine Learning, Image Analysis, Unsupervised Learning, Artificial Intelligence and Machine Learning (AI/ML), Predictive Modeling, Classification And Regression Tree (CART), Supervised Learning, Bayesian Statistics, Statistical Modeling, Artificial Intelligence, Deep Learning, Data Mining, Computer Vision, Statistical Machine Learning, Predictive Analytics, Text Mining, Machine Learning Algorithms

      4.6
      Rating, 4.6 out of 5 stars
      ·
      16K reviews

      Intermediate · Specialization · 3 - 6 Months

    • U

      University of Pennsylvania

      Fundamentals of Quantitative Modeling

      Skills you'll gain: Mathematical Modeling, Statistical Modeling, Regression Analysis, Business Modeling, Financial Modeling, Business Mathematics, Markov Model, Probability, Predictive Analytics, Process Optimization, Risk Management, Statistics, Probability Distribution, Simulation and Simulation Software, Forecasting

      4.6
      Rating, 4.6 out of 5 stars
      ·
      9.1K reviews

      Mixed · Course · 1 - 4 Weeks

    • U

      University of Alberta

      Reinforcement Learning

      Skills you'll gain: Reinforcement Learning, Machine Learning, Sampling (Statistics), Machine Learning Algorithms, Artificial Intelligence, Deep Learning, Simulations, Solution Architecture, Artificial Intelligence and Machine Learning (AI/ML), Markov Model, Supervised Learning, Algorithms, Performance Testing, Artificial Neural Networks, Pseudocode, Linear Algebra, Probability Distribution, Debugging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • M

      Meta

      Meta Marketing Science Certification Prep

      Skills you'll gain: Marketing Analytics, Bayesian Statistics, Descriptive Statistics, Marketing Effectiveness, Statistical Hypothesis Testing, A/B Testing, Target Audience, Marketing Strategies, Marketing Planning, Statistical Inference, Sampling (Statistics), Data Collection, Data Modeling, Statistics, Advertising Campaigns, Campaign Management, Marketing, Analytics, Google Analytics, Data Analysis

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

      Intermediate · Specialization · 3 - 6 Months

    • U

      University of California, Davis

      Geographic Information Systems (GIS)

      Skills you'll gain: ArcGIS, GIS Software, Spatial Data Analysis, Spatial Analysis, Data Storytelling, Geographic Information Systems, Data Presentation, Data Sharing, Geospatial Mapping, Public Health, Land Management, Heat Maps, Metadata Management, Data Quality, Community Health, Data Mapping, Data Visualization Software, File Management, Network Analysis, Data Modeling

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6.3K reviews

      Beginner · Specialization · 3 - 6 Months

    • G

      Google

      Foundations of Data Science

      Skills you'll gain: Data Storytelling, Data Ethics, Data Presentation, Data Analysis, Data-Driven Decision-Making, Analytical Skills, Data Science, Project Design, Communication, Business, Business Ethics, Business Workflow Analysis, Stakeholder Communications, Professional Networking, Data Management

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.1K reviews

      Advanced · Course · 1 - 3 Months

    • G

      Google Cloud

      Digital Transformation Using AI/ML with Google Cloud

      Skills you'll gain: Serverless Computing, Digital Transformation, Cloud Services, Data Ethics, Cloud Infrastructure, Google Cloud Platform, Application Programming Interface (API), Applied Machine Learning, Hybrid Cloud Computing, Infrastructure As A Service (IaaS), Cloud Computing, Public Cloud, Technical Management, Business Transformation, Containerization, Technology Strategies, Cloud-Native Computing, Multi-Cloud, IT Management, Machine Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      7.4K reviews

      Beginner · Specialization · 1 - 3 Months

    • I

      Imperial College London

      Mathematics for Machine Learning: Linear Algebra

      Skills you'll gain: Linear Algebra, NumPy, Applied Mathematics, Data Transformation, Data Science, Jupyter, Machine Learning Methods, Algorithms, Data Manipulation, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      12K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Alberta

      Software Product Management

      Skills you'll gain: Requirements Analysis, Agile Software Development, Agile Methodology, Kanban Principles, Project Planning, Requirements Elicitation, Sprint Retrospectives, Requirements Management, Functional Requirement, Business Requirements, Sprint Planning, Software Development Methodologies, Software Development Life Cycle, Scrum (Software Development), Product Requirements, Software Technical Review, Code Review, Software Development, Risk Management Framework, User Story

      4.7
      Rating, 4.7 out of 5 stars
      ·
      12K reviews

      Beginner · Specialization · 3 - 6 Months

    • U

      University of Toronto

      Self-Driving Cars

      Skills you'll gain: Computer Vision, Image Analysis, Control Systems, Embedded Software, Automation, Deep Learning, Software Architecture, Computer Graphics, Simulations, Safety Assurance, Artificial Neural Networks, Global Positioning Systems, Visualization (Computer Graphics), Hardware Architecture, Estimation, Algorithms, Machine Learning Methods, Predictive Modeling, Scenario Testing, Spatial Data Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.5K reviews

      Advanced · Specialization · 3 - 6 Months

    • K

      Kennesaw State University

      Six Sigma Tools for Analyze

      Skills you'll gain: Six Sigma Methodology, Root Cause Analysis, Process Analysis, Probability Distribution, Process Capability, Statistical Process Controls, Lean Methodologies, Probability & Statistics, Process Improvement, Quality Improvement, Process Mapping, Statistical Analysis, Systems Of Measurement, Risk Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.9K reviews

      Beginner · Course · 1 - 4 Weeks

    Applied Statistics learners also search

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    1…111213…198

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

    • Risk Management: New York Institute of Finance
    • Machine Learning: University of Washington
    • Fundamentals of Quantitative Modeling: University of Pennsylvania
    • Reinforcement Learning: University of Alberta
    • Meta Marketing Science Certification Prep: Meta
    • Geographic Information Systems (GIS): University of California, Davis
    • Foundations of Data Science: Google
    • Digital Transformation Using AI/ML with Google Cloud: Google Cloud
    • Mathematics for Machine Learning: Linear Algebra: Imperial College London
    • Software Product Management: University of Alberta

    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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