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    • Advanced Statistics
    Related topics:上級アルゴリズム上級機械学習データサイエンスのための統計学上級Python確率分布NumPy

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    「advanced statistics」の277件の結果

    • Johns Hopkins University

      Johns Hopkins University

      Advanced Statistics for Data Science

      習得できるスキル: Algebra, Artificial Neural Networks, Bayesian Statistics, Biostatistics, Business Analysis, Calculus, Communication, Confidence, Data Analysis, Dimensionality Reduction, Econometrics, Experiment, General Statistics, Linear Algebra, Machine Learning, Machine Learning Algorithms, Marketing, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Machine Learning, Statistical Programming, Statistical Tests

      4.4

      (657件のレビュー)

      Advanced · Specialization

    • 無料

      Nanjing University

      Nanjing University

      Data Processing Using Python

      習得できるスキル: Python Programming, Statistical Programming, Computer Programming

      4.1

      (268件のレビュー)

      Beginner · Course

    • 無料

      Georgia Institute of Technology

      Georgia Institute of Technology

      Materials Data Sciences and Informatics

      習得できるスキル: General Statistics, Human Computer Interaction, User Experience, Dimensionality Reduction, Experiment, Machine Learning, Materials, Probability & Statistics

      4.5

      (291件のレビュー)

      Intermediate · Course

    • University of Colorado Boulder

      University of Colorado Boulder

      Business Analytics for Decision Making

      習得できるスキル: Risk Management, Business Analytics, Data Analysis, Analysis, Data Management, Algorithms, Finance, Mathematics, Theoretical Computer Science, Analytics, Mathematical Theory & Analysis, Business Analysis, Big Data, Machine Learning, Machine Learning Algorithms, Markov Model, Probability & Statistics, Mathematical Optimization

      4.6

      (1.7k件のレビュー)

      Mixed · Course

    • Coursera Project Network

      Coursera Project Network

      Analyze Survey Data with Tableau

      Intermediate · Guided Project

    • Rice University

      Rice University

      Introduction to Data Analysis Using Excel

      習得できるスキル: General Statistics, Data Analysis, Microsoft Excel, Analysis, Data Mining, Pivot Table, Data Visualization, Computer Programming, Spreadsheet Software, Chart, Business Analysis, Data Analysis Software, Statistical Visualization, Probability & Statistics

      4.7

      (9.4k件のレビュー)

      Mixed · Course

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      Coursera Project Network

      Coursera Project Network

      Create a Custom Marketing Analytics Dashboard in Data Studio

      習得できるスキル: Analysis, Marketing, Market Analysis, Data Visualization

      4.4

      (54件のレビュー)

      Intermediate · Guided Project

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      Johns Hopkins University

      Johns Hopkins University

      Data Science: Foundations using R

      習得できるスキル: Analysis, Application Development, Business Analysis, Computer Programming, Data Analysis, Data Management, Data Visualization, Exploratory Data Analysis, Extract, Transform, Load, Knitr, Probability & Statistics, R Programming, Rstudio, Software Engineering Tools, Statistical Programming

      4.6

      (46.5k件のレビュー)

      Beginner · Specialization

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      無料

      Stanford University

      Stanford University

      Introduction to Statistics

      習得できるスキル: Data Analysis, Analysis, Statistical Analysis, General Statistics, Bayesian Statistics, Basic Descriptive Statistics, Regression, Experiment, Probability Distribution, Econometrics, Probability, Machine Learning, Statistical Tests, Markov Model, Probability & Statistics

      4.5

      (1.1k件のレビュー)

      Beginner · Course

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      IBM

      IBM

      Advanced Data Science with IBM

      習得できるスキル: Algorithms, Apache, Apache Spark, Applied Machine Learning, Artificial Neural Networks, Basic Descriptive Statistics, Bayesian Statistics, Big Data, Change Management, Cloud Computing, Computer Architecture, Computer Graphic Techniques, Computer Graphics, Computer Programming, Computer Vision, Correlation And Dependence, Data Analysis, Data Management, Data Model, Data Structures, Data Visualization, Deep Learning, Dimensionality Reduction, Distributed Computing Architecture, Econometrics, Estimation, Experiment, General Statistics, IBM Cloud, Leadership and Management, Machine Learning, Machine Learning Algorithms, Mathematics, Natural Language Processing, Probability & Statistics, Probability Distribution, Programming Principles, Python Programming, Regression, Signal Processing, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Strategy and Operations, Theoretical Computer Science

      4.3

      (2.9k件のレビュー)

      Advanced · Specialization

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      University of Michigan

      University of Michigan

      Statistics with Python

      習得できるスキル: Bayesian Statistics, Business Analysis, Communication, Computer Programming, Data Analysis, Data Visualization, Econometrics, Experiment, General Statistics, Hypothesis, Hypothesis Testing, Inference, Machine Learning, Machine Learning Algorithms, Marketing, Modeling, Probability & Statistics, Programming Principles, Python Programming, Regression, Statistical Analysis, Statistical Hypothesis Testing, Statistical Inference, Statistical Programming, Statistical Tests, Statistical Visualization

      4.6

      (2.8k件のレビュー)

      Beginner · Specialization

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      Google Cloud

      Google Cloud

      Advanced Machine Learning on Google Cloud

      習得できるスキル: Apache, Applied Machine Learning, Artificial Neural Networks, Business Psychology, Cloud Computing, Computational Thinking, Computer Architecture, Computer Programming, Computer Vision, Data Analysis, Data Management, Deep Learning, Distributed Computing Architecture, Entrepreneurship, General Statistics, Google Cloud Platform, Hardware Design, Linear Algebra, Machine Learning, Mathematics, Natural Language Processing, Performance Management, Probability & Statistics, Python Programming, Recommender Systems, Software Architecture, Software Engineering, Statistical Programming, Strategy and Operations, Theoretical Computer Science

      4.4

      (2.2k件のレビュー)

      Advanced · Specialization

    advanced statisticsに関連する検索

    advanced statistics for data science
    advanced linear models for data science 2: statistical linear models
    1234…24

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

    • Advanced Statistics for Data Science: Johns Hopkins University
    • Data Processing Using Python: Nanjing University
    • Materials Data Sciences and Informatics: Georgia Institute of Technology
    • Business Analytics for Decision Making: University of Colorado Boulder
    • Analyze Survey Data with Tableau: Coursera Project Network
    • Introduction to Data Analysis Using Excel: Rice University
    • Create a Custom Marketing Analytics Dashboard in Data Studio: Coursera Project Network
    • Data Science: Foundations using R: Johns Hopkins University
    • Introduction to Statistics: Stanford University
    • Advanced Data Science with IBM: IBM

    上級統計学に関するよくある質問

    • Advanced statistics are the mathematical tools used to discover and explore complex relationships between different variables in large datasets. In contrast to basic statistics such as average and analysis of variance (ANOVA) that simply describe the characteristics of a dataset, advanced statistical approaches often seek to make predictions about the world. This requires the use of more sophisticated statistical inference tools, such as generalized linear models for regression analysis capable of establishing how multiple interrelated factors may impact projected outcomes.

      These advanced statistical methods are increasingly important in the field of data science, which is tasked with uncovering important business insights and developing predictive models from diverse big data-scale datasets. These techniques are also especially important for the proper training and use of machine learning algorithms. As in data science and machine learning more generally, R programming and Python programming skills are typically relied upon to conduct these advanced statistical analyses.‎

    • Advanced statistics skills are essential for work in data science, machine learning, and artificial intelligence (AI), as statistical approaches are at the heart of the learning algorithms that make these applications possible. An understanding of statistics is likewise important for professionals in finance, healthcare, and other industries that are increasingly making use of machine learning and AI, as they increasingly need to work closely with data scientists to ensure that these powerful techniques are developed to solve the right business problems.

      Those wishing to delve deeper into advanced statistical methods and help develop new mathematical approaches in the field may pursue a master’s or even a PhD in statistics. These experts work in academia, government, or at private sector companies involved in scientific or engineering research. According to the Bureau of Labor Statistics, professional statisticians earn a median annual salary of $91,160, and this specialized career path is expected to be in high demand due to expanding opportunities to use statistics to navigate our data-rich world.‎

    • Certainly. Coursera offers a variety of courses in advanced statistics as well as their applications in the context of fields like data science and machine learning. In fact, coursework in statistics is often a prerequisite for data science classes. Regardless of your level of expertise and needs in these areas, Coursera enables you to learn remotely from top-ranked schools like the University of Michigan, Johns Hopkins University, and Duke University. And, since you can view course materials and complete coursework on a flexible schedule, there’s an exceedingly high probability that you can fit online learning about advanced statistics into your existing school or work life.‎

    • You need to have strong math skills, especially in basic calculus, linear algebra, and statistics before starting to learn advanced statistics. It's important that you have strong technical skills and are very comfortable on the computer, strong analytical skills, and the ability to carefully examine and question data that is presented to you so that you can organize and draw conclusions from it. For learning some concepts in advanced statistics, you'll need to have experience using the R statistical software package and understand Bayesian estimation, principles of maximum-likelihood estimation, and calculus-based probability.‎

    • People who enjoy mathematics are best suited for roles in advanced statistics, especially those who enjoy concepts like probability, linear models, and statistics and how they relate to data science. They can quickly grasp and apply complex technical concepts as well. Those who enjoy testing hypotheses and figuring out uncertain outcomes based on probability are also well suited for roles in advanced statistics. Also, people who have wide-ranging computer skills, the ability to communicate their statistical findings in plain language, problem-solving and analytical skills, and teamwork and collaborative skills are best suited for roles involving advanced statistics.‎

    • If you're aspiring to be a biostatistician or data scientist, learning advanced statistics is probably right for you. If you're interested in machine learning and the development of data products, you may also find learning advanced statistics is right for you. People who want to have a career as a statistician, statistical epidemiologist, sports analyst, actuary, market researcher, or investment analyst may also find learning advanced statistics to be the right choice. And if you need to understand how to transform complex sets of data into practical applications, learning advanced statistics is 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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