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    • Advanced Statistics

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    441 Ergebnisse für „advanced statistics“

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      Johns Hopkins University

      Advanced Statistics for Data Science

      Kompetenzen, die Sie erwerben: Probability & Statistics, General Statistics, Mathematics, Probability Distribution, Regression, Linear Algebra, Bayesian Statistics, Experiment, Econometrics, Machine Learning, Basic Descriptive Statistics, Biostatistics, Calculus, Statistical Tests, Algebra, Artificial Neural Networks, Dimensionality Reduction, Machine Learning Algorithms, Statistical Machine Learning, Communication, Correlation And Dependence, Data Analysis, Estimation, Exploratory Data Analysis, Statistical Analysis

      4.4

      (688 Bewertungen)

      Advanced · Specialization · 3-6 Months

    • Kostenlos

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      Georgia Institute of Technology

      Materials Data Sciences and Informatics

      Kompetenzen, die Sie erwerben: Human Computer Interaction, Probability & Statistics, User Experience, Big Data, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Design and Product, Dimensionality Reduction, Experiment, General Statistics, Machine Learning, Python Programming, Statistical Programming

      4.5

      (308 Bewertungen)

      Intermediate · Course · 1-3 Months

    • Kostenlos

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      Nanjing University

      Data Processing Using Python

      Kompetenzen, die Sie erwerben: Computer Programming, Python Programming, Computer Science, Data Structures, Theoretical Computer Science, Computational Thinking, Human Computer Interaction, User Experience, Statistical Programming

      4.1

      (305 Bewertungen)

      Beginner · Course · 1-3 Months

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      University of Colorado Boulder

      Business Analytics for Decision Making

      Kompetenzen, die Sie erwerben: Data Analysis, Business Analysis, Machine Learning, Markov Model, Algorithms, Big Data, Data Analysis Software, Machine Learning Algorithms, Statistical Analysis, Theoretical Computer Science, Exploratory Data Analysis

      4.6

      (1.8k Bewertungen)

      Mixed · Course · 1-4 Weeks

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      Coursera Project Network

      Analyze Survey Data with Tableau

      Kompetenzen, die Sie erwerben: Data Analysis, Market Research

      Intermediate · Guided Project · Less Than 2 Hours

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      Macquarie University

      Excel Skills for Business

      Kompetenzen, die Sie erwerben: Business Analysis, Microsoft Excel, Spreadsheet Software, Data Analysis, Plot (Graphics), Data Visualization, Basic Descriptive Statistics, Computational Logic, Computer Architecture, Data Analysis Software, Data Management, Data Mining, Data Visualization Software, Extract, Transform, Load, Interactive Data Visualization, Mathematical Theory & Analysis, Mathematics, Theoretical Computer Science

      4.9

      (56.1k Bewertungen)

      Beginner · Specialization · 3-6 Months

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      SAS

      SAS Advanced Programmer

      Kompetenzen, die Sie erwerben: SAS (Software), Statistical Programming, Data Management, Databases, SQL, Business Analysis, Spreadsheet Software, Computational Logic, Computer Programming, Mathematical Theory & Analysis, Mathematics, Programming Principles, Theoretical Computer Science, Big Data, Data Analysis, Computational Thinking

      4.8

      (303 Bewertungen)

      Intermediate · Professional Certificate · 3-6 Months

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      Coursera Project Network

      Create a Custom Marketing Analytics Dashboard in Data Studio

      Kompetenzen, die Sie erwerben: Business Analysis, Data Analysis, Data Analysis Software, Data Visualization, Data Visualization Software, Marketing

      4.4

      (79 Bewertungen)

      Intermediate · Guided Project · Less Than 2 Hours

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      Johns Hopkins University

      Data Science: Foundations using R

      Kompetenzen, die Sie erwerben: R Programming, Data Analysis, Data Science, Exploratory Data Analysis, Statistical Programming, Data Visualization Software, Software Visualization, Statistical Visualization, Basic Descriptive Statistics, General Statistics, Big Data, Computer Programming, Computer Programming Tools, Data Structures, Experiment, Linear Algebra, Machine Learning Software, Probability & Statistics, Probability Distribution, Software Engineering Tools, Spreadsheet Software, Statistical Tests, Application Development, Business Analysis, Data Management, Data Visualization, Extract, Transform, Load, Knitr, Plot (Graphics)

      4.6

      (47.2k Bewertungen)

      Beginner · Specialization · 3-6 Months

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      University of Illinois at Urbana-Champaign

      Accounting Data Analytics with Python

      Kompetenzen, die Sie erwerben: Python Programming, Statistical Programming, Computer Programming, Data Structures, Data Visualization, Databases, Plot (Graphics), Probability & Statistics, Regression, SQL, Theoretical Computer Science, Database Administration, Statistical Visualization, Accounting, Data Analysis, Data Management, Microsoft Excel, Operating Systems, Systems Design

      4.2

      (73 Bewertungen)

      Intermediate · Course · 1-3 Months

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      University of Minnesota

      Introduction to Recommender Systems: Non-Personalized and Content-Based

      Kompetenzen, die Sie erwerben: Basic Descriptive Statistics, Data Analysis, Probability & Statistics, Machine Learning, Applied Machine Learning, Natural Language Processing, Statistical Analysis, Statistical Programming, Correlation And Dependence, Java Programming, Machine Learning Algorithms, Statistical Machine Learning, Business Analysis, Computer Programming, Data Analysis Software, Digital Marketing, General Statistics, Human Computer Interaction, Marketing, Mobile Development, Spreadsheet Software

      4.5

      (626 Bewertungen)

      Intermediate · Course · 1-3 Months

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      CFA Institute

      Statistics for Machine Learning for Investment Professionals

      Kompetenzen, die Sie erwerben: Probability & Statistics, General Statistics, Machine Learning, Data Analysis, Regression, Business Analysis, Computer Programming, Correlation And Dependence, Econometrics, Forecasting, Machine Learning Algorithms, Probability Distribution, Python Programming, Statistical Analysis, Statistical Programming, Statistical Tests, Data Science

      4.7

      (25 Bewertungen)

      Beginner · Course · 1-3 Months

    Suchanfragen mit Bezug zu advanced statistics

    advanced statistics for data science
    advanced linear models for data science 2: statistical linear models
    1234…37

    Zusammenfassend sind hier 10 unserer beliebtesten advanced statistics Kurse

    • Advanced Statistics for Data Science: Johns Hopkins University
    • Materials Data Sciences and Informatics: Georgia Institute of Technology
    • Data Processing Using Python: Nanjing University
    • Business Analytics for Decision Making: University of Colorado Boulder
    • Analyze Survey Data with Tableau: Coursera Project Network
    • Excel Skills for Business: Macquarie University
    • SAS Advanced Programmer: SAS
    • Create a Custom Marketing Analytics Dashboard in Data Studio: Coursera Project Network
    • Data Science: Foundations using R: Johns Hopkins University
    • Accounting Data Analytics with Python: University of Illinois at Urbana-Champaign

    Häufig gestellte Fragen zum Thema Statistik für Fortgeschrittene

    • 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.‎

    Diese häufig gestellten Fragen dienen nur zu Informationszwecken. Den Lernenden wird empfohlen, eingehender zu recherchieren, ob Kurse und andere angestrebte Qualifikationen wirklich ihren persönlichen, beruflichen und finanziellen Vorstellungen entsprechen.
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