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    Results for "advanced statistics"

    • I

      IBM

      Data Analysis and Visualization Foundations

      Skills you'll gain: Data Storytelling, Big Data, Data Visualization Software, Data Analysis, Dashboard, IBM Cognos Analytics, Statistical Analysis, Data Mining, Apache Hadoop, Tree Maps, Excel Formulas, Data Wrangling, Apache Hive, Data Collection, Data Mart, Microsoft Excel, Data Warehousing, Data Quality, Data Cleansing, Data Import/Export

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      27K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free
      Free
      D

      Duke University

      Data Science Math Skills

      Skills you'll gain: Probability, Bayesian Statistics, General Mathematics, Calculus, Graphing, Statistics, Data Science, Data Analysis, Plot (Graphics), Algebra, Geometry, Arithmetic, Derivatives

      4.5
      Rating, 4.5 out of 5 stars
      ·
      13K reviews

      Beginner · Course · 1 - 3 Months

    • Status: Free
      Free
      U

      University of Zurich

      An Intuitive Introduction to Probability

      Skills you'll gain: Probability, Probability Distribution, Probability & Statistics, Descriptive Statistics, Applied Mathematics, Statistical Methods, Risk Analysis, Finance

      4.8
      Rating, 4.8 out of 5 stars
      ·
      1.8K reviews

      Beginner · Course · 1 - 3 Months

    • Status: New
      New
      I

      IBM

      IBM Java Developer

      Skills you'll gain: Spring Framework, Cloud-Native Computing, Software Development Life Cycle, Hibernate (Java), Docker (Software), Git (Version Control System), GitHub, HTML and CSS, Object Oriented Programming (OOP), Spring Boot, Object-Relational Mapping, Development Environment, Web Design and Development, Object Oriented Design, Large Language Modeling, Java Programming, Generative AI, Interviewing Skills, Java, Professional Networking

      4.7
      Rating, 4.7 out of 5 stars
      ·
      5.3K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • M

      Meta

      Advanced MySQL Topics

      Skills you'll gain: MySQL, Database Management, Stored Procedure, Database Development, SQL, Performance Tuning, Relational Databases, Query Languages, JSON, Analytics, Data Analysis, Transaction Processing

      4.6
      Rating, 4.6 out of 5 stars
      ·
      234 reviews

      Intermediate · Course · 1 - 4 Weeks

    • I

      IBM

      IBM Data Analytics with Excel and R

      Skills you'll gain: Data Storytelling, Interactive Data Visualization, Shiny (R Package), Data Wrangling, Exploratory Data Analysis, Relational Databases, Big Data, Data Visualization Software, Ggplot2, Database Design, Data Analysis, IBM Cognos Analytics, Statistical Analysis, Data Presentation, Data Mining, Dashboard, Excel Formulas, Data Manipulation, Web Scraping, Microsoft Excel

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      28K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • M

      Meta

      Meta Database Engineer

      Skills you'll gain: MySQL, Database Design, Version Control, Database Management Systems, Database Management, MySQL Workbench, Extract, Transform, Load, Data Modeling, Data Structures, SQL, Unix Commands, Relational Databases, Databases, Git (Version Control System), Database Systems, Stored Procedure, Object Oriented Programming (OOP), Unit Testing, Algorithms, Django (Web Framework)

      4.6
      Rating, 4.6 out of 5 stars
      ·
      7K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of California, Santa Cruz

      Bayesian Statistics: From Concept to Data Analysis

      Skills you'll gain: Bayesian Statistics, Statistical Inference, Data Analysis, Probability, Statistical Modeling, Statistical Analysis, Microsoft Excel, Probability Distribution, R Programming, Regression Analysis

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.2K reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      I

      IBM

      Generative AI for Mobile App Developers

      Skills you'll gain: Prompt Engineering, ChatGPT, Generative AI, AI Personalization, Large Language Modeling, Mobile Development, OpenAI, CI/CD, Software Development, Software Development Life Cycle, DevSecOps, Machine Learning Methods, Data Ethics, Artificial Intelligence, Cloud Applications, Natural Language Processing, Cloud Development, Prototyping, Software Architecture, Program Development

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.3K reviews

      Advanced · Specialization · 1 - 3 Months

    • J

      Johns Hopkins University

      Data Science

      Skills you'll gain: Shiny (R Package), Rmarkdown, Exploratory Data Analysis, Regression Analysis, Leaflet (Software), Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Statistical Inference, Predictive Modeling, Statistical Hypothesis Testing, Data Wrangling, Data Visualization, Plotly, Machine Learning Algorithms, Plot (Graphics), Knitr

      4.5
      Rating, 4.5 out of 5 stars
      ·
      51K reviews

      Beginner · Specialization · 3 - 6 Months

    • Status: Free
      Free
      E

      Erasmus University Rotterdam

      Advanced Valuation and Strategy - M&A, Private Equity, and Venture Capital

      Skills you'll gain: Strategic Decision-Making, Business Valuation, Private Equity, Mergers & Acquisitions, Game Theory, Strategic Thinking, Financial Analysis, Business Strategy, Return On Investment, Investment Banking, Cash Flows, Corporate Finance, Competitive Analysis

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      1.3K reviews

      Advanced · Course · 1 - 3 Months

    • I

      IBM

      Introduction to Data Science

      Skills you'll gain: SQL, Jupyter, Data Literacy, Data Mining, Peer Review, Data Modeling, Relational Databases, Stored Procedure, Databases, Data Science, Big Data, Computer Programming Tools, Query Languages, Data Analysis Software, Data Cleansing, GitHub, Business Analysis, Cloud Computing, Data Analysis, Digital Transformation

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      98K reviews

      Beginner · Specialization · 3 - 6 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
    advanced statistical analysis and tools
    seaborn: visualizing basics to advanced statistical plots
    advanced probability and statistical methods
    advanced linear models for data science 2: statistical linear models
    1…678…262

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

    • Data Analysis and Visualization Foundations: IBM
    • Data Science Math Skills: Duke University
    • An Intuitive Introduction to Probability: University of Zurich
    • IBM Java Developer: IBM
    • Advanced MySQL Topics: Meta
    • IBM Data Analytics with Excel and R: IBM
    • Meta Database Engineer: Meta
    • Bayesian Statistics: From Concept to Data Analysis: University of California, Santa Cruz
    • Generative AI for Mobile App Developers: IBM
    • Data Science: Johns Hopkins University

    Frequently Asked Questions about Advanced Statistics

    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.‎

    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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