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    • Applied Statistics

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    「applied statistics」の782件の結果

    • Indian Statistical Institute

      Indian Statistical Institute

      Postgraduate Diploma in Applied Statistics

      Postgraduate Diploma

    • DeepLearning.AI

      DeepLearning.AI

      Machine Learning

      習得できるスキル: Algorithms, Applied Machine Learning, Artificial Neural Networks, Computer Programming, Computer Vision, Deep Learning, Econometrics, Feature Engineering, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Theoretical Computer Science

      4.9

      (412件のレビュー)

      Beginner · Specialization

    • IBM

      IBM

      Introduction to Data Science

      習得できるスキル: Analysis, Communication, Computer Programming, Data Analysis, Data Management, Data Mining, Database Administration, Database Application, Databases, General Statistics, Machine Learning, Marketing, Modeling, Probability & Statistics, Python Programming, R Programming, Regression, SPSS, SQL, Statistical Programming

      4.6

      (69.1k件のレビュー)

      Beginner · Specialization

    • IBM

      IBM

      Data Science Fundamentals with Python and SQL

      習得できるスキル: Analysis, Basic Descriptive Statistics, Business Analysis, Computational Logic, Computer Programming, Data Analysis, Data Management, Data Visualization, Database Administration, Database Application, Databases, Extract, Transform, Load, General Statistics, Machine Learning, Mathematical Theory & Analysis, Mathematics, Pandas, Plot (Graphics), Probability & Statistics, Probability Distribution, Python Programming, R Programming, Regression, SPSS, SQL, Statistical Analysis, Statistical Programming, Statistical Tests, Statistical Visualization, Theoretical Computer Science

      4.5

      (46.5k件のレビュー)

      Beginner · Specialization

    • DeepLearning.AI

      DeepLearning.AI

      Deep Learning

      習得できるスキル: Advertising, Algorithms, Applied Machine Learning, Artificial Neural Networks, Bayesian Statistics, Business Psychology, Communication, Computational Logic, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Deep Learning, Entrepreneurship, General Statistics, Hardware Design, Human Computer Interaction, Interactive Design, Leadership and Management, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Markov Model, Mathematical Theory & Analysis, Mathematics, Modeling, Natural Language Processing, Network Architecture, Object Detection, Probability & Statistics, Project Management, Python Programming, Regression, Sales, Speech, Statistical Machine Learning, Statistical Programming, Strategy, Strategy and Operations, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science

      4.8

      (133.2k件のレビュー)

      Intermediate · Specialization

    • IBM

      IBM

      Key Technologies for Business

      習得できるスキル: Analysis, Applied Machine Learning, BlockChain, Cloud Computing, Cloud Infrastructure, Cloud Platforms, Cloud Storage, Communication, Computer Architecture, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Data Analysis, Data Mining, Deep Learning, DevOps, Ethics, Finance, General Statistics, Human Computer Interaction, IBM Cloud, Interactive Design, Machine Learning, Machine Learning Algorithms, Network Architecture, Network Security, Operating Systems, Probability & Statistics, Regression, Security, Security Engineering, Software Architecture, Software As A Service, Software Engineering, Software Framework, System Programming, Theoretical Computer Science

      4.7

      (65.7k件のレビュー)

      Beginner · Specialization

    • Placeholder
      IBM

      IBM

      Applied Data Science

      習得できるスキル: Algebra, Algorithms, Analysis, Business Analysis, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Data Mining, Data Visualization, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Interactive Data Visualization, Linear Regression, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Probability & Statistics, Python Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science

      4.6

      (38.1k件のレビュー)

      Beginner · Specialization

    • Placeholder
      IBM

      IBM

      IBM AI Foundations for Business

      習得できるスキル: Analysis, Applied Machine Learning, Artificial Neural Networks, Cloud Computing, Cloud Platforms, Communication, Computer Vision, Data Analysis, Data Architecture, Data Management, Data Mining, Database Administration, Databases, Deep Learning, Ethics, General Statistics, Leadership and Management, Machine Learning, Machine Learning Algorithms, Probability & Statistics, Regression

      4.7

      (63.9k件のレビュー)

      Beginner · Specialization

    • Placeholder
      University of Virginia Darden School Foundation

      University of Virginia Darden School Foundation

      IBM & Darden Digital Strategy

      習得できるスキル: Analysis, Analytics, Apache, Applied Machine Learning, Big Data, BlockChain, Business Analysis, Business Design, Business Transformation, Change Management, Cloud Computing, Cloud Infrastructure, Cloud Platforms, Cloud Storage, Computer Architecture, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Data Analysis, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Databases, Deep Learning, Design and Product, DevOps, Digital Marketing, Entrepreneurship, Ethics, Extract, Transform, Load, Finance, General Statistics, Human Computer Interaction, IBM Cloud, Interactive Design, Leadership and Management, Machine Learning, Machine Learning Algorithms, Market Analysis, Marketing, Network Architecture, Network Security, NoSQL, Operating Systems, Product Strategy, Professional Development, Research and Design, Sales, Security, Security Engineering, Software Architecture, Software As A Service, Software Engineering, Software Framework, Strategy, Strategy and Operations, System Programming, Theoretical Computer Science

      4.7

      (18.1k件のレビュー)

      Beginner · Specialization

    • Placeholder
      DeepLearning.AI

      DeepLearning.AI

      Supervised Machine Learning: Regression and Classification

      習得できるスキル: Theoretical Computer Science, Linear Algebra, Machine Learning, Probability & Statistics, Linear Regression, Feature Engineering, Statistical Machine Learning, Mathematics, Regression, Logistic Regression, Algorithms, Econometrics, Linearity, General Statistics, Applied Machine Learning, Machine Learning Algorithms

      4.9

      (393件のレビュー)

      Beginner · Course

    • Placeholder
      DeepLearning.AI

      DeepLearning.AI

      DeepLearning.AI TensorFlow Developer

      習得できるスキル: Applied Machine Learning, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Deep Learning, Entrepreneurship, Forecasting, General Statistics, Machine Learning, Machine Learning Algorithms, Modeling, Natural Language Processing, Probability & Statistics, Programming Principles, Python Programming, Statistical Machine Learning, Statistical Programming

      4.7

      (21.9k件のレビュー)

      Intermediate · Professional Certificate

    • Placeholder
      DeepLearning.AI

      DeepLearning.AI

      Machine Learning Engineering for Production (MLOps)

      習得できるスキル: Applied Machine Learning, Business Analysis, Change Management, Cloud Computing, Computer Networking, Computer Programming, Data Analysis, Data Management, Data Visualization, Deep Learning, DevOps, Estimation, Exploratory Data Analysis, Extract, Transform, Load, Feature Engineering, General Statistics, Leadership and Management, Machine Learning, Machine Learning Algorithms, Modeling, Network Security, Probability & Statistics, Python Programming, Security Engineering, Security Strategy, Statistical Programming, Statistical Visualization, Strategy and Operations

      4.7

      (2k件のレビュー)

      Advanced · Specialization

    1234…66

    要約して、applied statistics の人気コース10選をご紹介します。

    • Postgraduate Diploma in Applied Statistics: Indian Statistical Institute
    • Machine Learning: DeepLearning.AI
    • Introduction to Data Science: IBM
    • Data Science Fundamentals with Python and SQL: IBM
    • Deep Learning: DeepLearning.AI
    • Key Technologies for Business: IBM
    • Applied Data Science: IBM
    • IBM AI Foundations for Business: IBM
    • IBM & Darden Digital Strategy: University of Virginia Darden School Foundation
    • Supervised Machine Learning: Regression and Classification: DeepLearning.AI

    応用統計学に関するよくある質問

    • 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.‎

    このFAQの内容は、情報提供のみを目的としています。受講生は、自分の個人的、職業的、経済的な目標に合ったコースやその他の資格を取得するために、さらに調べることをお勧めします。
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