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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

    • Status: New
      New
      C

      Coursera Instructor Network

      Smart Data Cleaning with Generative AI

      Skills you'll gain: Data Quality, Data Ethics, Data Cleansing, Generative AI, Data Validation, Automation, Applied Machine Learning, Data Transformation, Tensorflow, Pandas (Python Package), OpenAI, Artificial Intelligence, Jupyter, Alteryx

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      P

      Packt

      Financial Analysis with ARIMA and Time Series Forecasting

      Skills you'll gain: Time Series Analysis and Forecasting, Financial Forecasting, Statistical Analysis, Machine Learning, Trend Analysis, Financial Data, Financial Modeling, Jupyter, Predictive Modeling, Regression Analysis, Finance, GitHub

      Intermediate · Course · 1 - 3 Months

    • C

      Coursera Project Network

      ETL pipelines con Python: recopila datos de Spotify

      Skills you'll gain: Interactive Data Visualization, Data Transformation, Data Visualization Software, Business Intelligence, Data Analysis, Extract, Transform, Load, Exploratory Data Analysis, Pandas (Python Package), Data Collection, Data Pipelines, Jupyter, Virtual Environment

      Intermediate · Guided Project · Less Than 2 Hours

    • Status: New
      New
      P

      Politecnico di Milano

      Derivatives: a guide to calculation

      Skills you'll gain: ChatGPT, Calculus, Derivatives, Mathematical Software, AI Personalization, Applied Mathematics, Mathematics Education, Machine Learning Algorithms, Algebra, Geometry, Mathematical Theory & Analysis, Graphing

      Intermediate · Course · 1 - 3 Months

    • Status: New
      New
      Status: Free Trial
      Free Trial
      U

      Universidades Anáhuac

      Ciencia de Datos e IA : De los Fundamentos a la Práctica

      Skills you'll gain: Predictive Modeling, Data Cleansing, Time Series Analysis and Forecasting, Database Management, Artificial Neural Networks, SQL, Predictive Analytics, Databases, Keras (Neural Network Library), Deep Learning, Applied Machine Learning, Tensorflow, Statistical Modeling, Data Modeling, Data Visualization Software, Forecasting, Pandas (Python Package), Data Science, Supervised Learning, Image Analysis

      Intermediate · Specialization · 1 - 3 Months

    • Status: Free
      Free
      A

      Amazon Web Services

      Amazon Connect Contact Lens Fundamentals

      Skills you'll gain: Customer experience improvement, Customer Insights, Customer Relationship Management, Customer Service, Trend Analysis, Performance Analysis, Taking Meeting Minutes, Text Mining, System Implementation

      Mixed · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      I

      Illinois Tech

      Cloud Computing Security - Master's

      Skills you'll gain: Cloud Security, Identity and Access Management, AWS Identity and Access Management (IAM), Incident Response, Cloud Computing, Cloud Computing Architecture, Data Security, Application Security, Network Security, Encryption, Role-Based Access Control (RBAC), Amazon Web Services, Key Management, Infrastructure Security, Security Controls, Firewall, Continuous Monitoring, Multi-Factor Authentication

      Beginner · Course · 1 - 3 Months

    • Status: New
      New
      K

      Korea Advanced Institute of Science and Technology(KAIST)

      Thermodynamics and Phase Equilibria

      Skills you'll gain: Mechanical Engineering, Engineering, Thermal Management, Physics, Chemical Engineering, Physical Science, Energy and Utilities, Chemistry, Applied Mathematics

      Intermediate · Course · 1 - 3 Months

    • G

      Google Cloud

      Machine Learning Operations (MLOps): Getting Started - 日本語版

      Skills you'll gain: MLOps (Machine Learning Operations), Google Cloud Platform, DevOps, Applied Machine Learning, CI/CD, Automation, Continuous Monitoring, Data Pipelines, Scalability

      Intermediate · Course · 1 - 4 Weeks

    • Status: Free Trial
      Free Trial
      J

      Johns Hopkins University

      Computational and Graphical Models in Probability

      Skills you'll gain: Network Analysis, Statistical Modeling, Bayesian Network, R Programming, Simulations, Applied Mathematics, Graph Theory, Data Analysis, Probability, Statistical Analysis, Markov Model, Probability Distribution, Machine Learning

      Intermediate · Course · 1 - 4 Weeks

    • G

      Google Cloud

      Analyze Customer Reviews with Gemini Using Python Notebooks

      Skills you'll gain: Google Cloud Platform, Applied Machine Learning, Big Data, Jupyter, Cloud Applications, Text Mining, Statistical Reporting

      Intermediate · Project · Less Than 2 Hours

    • N

      Northwestern University

      현대 로봇공학, 강좌 1: 로봇 동작의 기초

      Skills you'll gain: Mathematical Modeling, Engineering, Mechanics, Automation, Torque (Physics), Engineering Analysis, Virtual Environment, Simulation and Simulation Software, Engineering Calculations, Matlab, Applied Mathematics, Linear Algebra, Computer Programming Tools

      Intermediate · Course · 1 - 3 Months

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

    • Smart Data Cleaning with Generative AI: Coursera Instructor Network
    • Financial Analysis with ARIMA and Time Series Forecasting: Packt
    • ETL pipelines con Python: recopila datos de Spotify: Coursera Project Network
    • Derivatives: a guide to calculation: Politecnico di Milano
    • Ciencia de Datos e IA : De los Fundamentos a la Práctica: Universidades Anáhuac
    • Amazon Connect Contact Lens Fundamentals: Amazon Web Services
    • Cloud Computing Security - Master's: Illinois Tech
    • Thermodynamics and Phase Equilibria: Korea Advanced Institute of Science and Technology(KAIST)
    • Machine Learning Operations (MLOps): Getting Started - 日本語版: Google Cloud
    • Computational and Graphical Models in Probability: Johns Hopkins 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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