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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

    • 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

    • U

      University of Michigan

      Fitting Statistical Models to Data with Python

      Skills you'll gain: Statistical Modeling, Statistical Methods, Bayesian Statistics, Statistical Inference, Statistical Analysis, Statistical Programming, Regression Analysis, Predictive Modeling, Jupyter, Exploratory Data Analysis, Statistical Hypothesis Testing, Correlation Analysis, Probability Distribution

      4.4
      Rating, 4.4 out of 5 stars
      ·
      702 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free
      Free
      T

      The University of North Carolina at Chapel Hill

      Epidemiology: The Basic Science of Public Health

      Skills you'll gain: Epidemiology, Public Health and Disease Prevention, Public Health, Infectious Diseases, Research Design, Health Policy, Biostatistics, Science and Research, Health Care, Probability & Statistics, Statistical Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      4.9K reviews

      Mixed · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      Natural Language Processing with Classification and Vector Spaces

      Skills you'll gain: Natural Language Processing, Supervised Learning, Dimensionality Reduction, Feature Engineering, Machine Learning Algorithms, Artificial Intelligence, Tensorflow, Linear Algebra, Probability & Statistics

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

      Intermediate · Course · 1 - 4 Weeks

    • D

      DeepLearning.AI

      Linear Algebra for Machine Learning and Data Science

      Skills you'll gain: Linear Algebra, NumPy, Dimensionality Reduction, Machine Learning Methods, Jupyter, Data Manipulation, Data Science, Applied Mathematics, Python Programming, Artificial Intelligence

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      I

      IBM

      IBM Product Owner

      Skills you'll gain: User Story, Sprint Retrospectives, Backlogs, Agile Product Development, Agile Software Development, Stakeholder Communications, Agile Methodology, Kanban Principles, Product Roadmaps, Virtual Teams, Stakeholder Management, Information Technology, Agile Project Management, Computer Hardware, Product Strategy, Product Planning, Product Management, Cloud Computing, Communication, Sprint Planning

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

      Beginner · Professional Certificate · 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 Michigan

      Capstone: Retrieving, Processing, and Visualizing Data with Python

      Skills you'll gain: Data Processing, Data Collection, Data Visualization Software, Data Manipulation, Python Programming, Exploratory Data Analysis, Algorithms, Data Analysis, Web Scraping, Unstructured Data

      4.7
      Rating, 4.7 out of 5 stars
      ·
      14K reviews

      Beginner · Course · 1 - 3 Months

    • I

      IBM

      Introduction to Software Engineering

      Skills you'll gain: Software Development Life Cycle, Software Architecture, Software Design, Scrum (Software Development), Software Engineering, Agile Methodology, Software Development Methodologies, Unified Modeling Language, Programming Principles, Application Development, Web Development, Python Programming

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.3K reviews

      Beginner · Course · 1 - 3 Months

    • S

      Stanford University

      Divide and Conquer, Sorting and Searching, and Randomized Algorithms

      Skills you'll gain: Algorithms, Graph Theory, Data Structures, Theoretical Computer Science, Computational Thinking, Analysis, Programming Principles, Probability & Statistics, Probability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      5.3K reviews

      Intermediate · Course · 1 - 4 Weeks

    • M

      MathWorks

      Practical Data Science with MATLAB

      Skills you'll gain: Data Storytelling, Feature Engineering, Data Visualization, Supervised Learning, Dimensionality Reduction, Data Integration, Interactive Data Visualization, Exploratory Data Analysis, Data Import/Export, Technical Communication, Data Analysis, Applied Machine Learning, Data Visualization Software, Matlab, Regression Analysis, Data Processing, Descriptive Statistics, Data Cleansing, Predictive Modeling, Data Science

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      AI for Medicine

      Skills you'll gain: Deep Learning, Statistical Analysis, Clinical Trials, Feature Engineering, Risk Modeling, Treatment Planning, Data Analysis, Precision Medicine, Decision Tree Learning, Predictive Modeling, Patient Treatment, Image Analysis, Machine Learning Methods, Applied Machine Learning, AI Personalization, Machine Learning, Random Forest Algorithm, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, Medical Imaging

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.4K reviews

      Intermediate · Specialization · 1 - 3 Months

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    1…131415…198

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

    • Self-Driving Cars: University of Toronto
    • Fitting Statistical Models to Data with Python: University of Michigan
    • Epidemiology: The Basic Science of Public Health: The University of North Carolina at Chapel Hill
    • Natural Language Processing with Classification and Vector Spaces: DeepLearning.AI
    • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
    • IBM Product Owner: IBM
    • Meta Marketing Science Certification Prep: Meta
    • Capstone: Retrieving, Processing, and Visualizing Data with Python: University of Michigan
    • Introduction to Software Engineering: IBM
    • Divide and Conquer, Sorting and Searching, and Randomized Algorithms: Stanford University

    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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