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    Results for "advanced statistics"

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      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

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      4.7
      Rating, 4.7 out of 5 stars
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      28K reviews

      Beginner · Specialization · 3 - 6 Months

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      IBM

      Generative AI for Data Scientists

      Skills you'll gain: Prompt Engineering, Generative AI, ChatGPT, Exploratory Data Analysis, Data Ethics, OpenAI, Feature Engineering, Predictive Modeling, Large Language Modeling, Artificial Intelligence, Data Storytelling, Program Development, Data Modeling, Data Presentation, Predictive Analytics, Data Synthesis, Data Analysis, Data Cleansing, Data Visualization Software, Image Analysis

      4.7
      Rating, 4.7 out of 5 stars
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      6.9K reviews

      Intermediate · Specialization · 1 - 3 Months

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      Multiple educators

      Machine Learning

      Skills you'll gain: Unsupervised Learning, Supervised Learning, Machine Learning Methods, Classification And Regression Tree (CART), Artificial Intelligence and Machine Learning (AI/ML), Applied Machine Learning, Machine Learning Algorithms, Machine Learning, Jupyter, Data Ethics, Decision Tree Learning, Tensorflow, Scikit Learn (Machine Learning Library), Artificial Intelligence, NumPy, Predictive Modeling, Deep Learning, Reinforcement Learning, Random Forest Algorithm, Feature Engineering

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      4.9
      Rating, 4.9 out of 5 stars
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      34K reviews

      Beginner · Specialization · 1 - 3 Months

    • U

      University of London

      Probability and Statistics: To p or not to p?

      Skills you'll gain: Descriptive Statistics, Statistics, Probability & Statistics, Statistical Hypothesis Testing, Data Visualization, Data-Driven Decision-Making, Statistical Modeling, Data Analysis, Probability, Probability Distribution, Sampling (Statistics), Risk Modeling, Statistical Inference, Mathematical Modeling

      4.6
      Rating, 4.6 out of 5 stars
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      Beginner · Course · 1 - 3 Months

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      University of Pennsylvania

      English for Career Development

      Skills you'll gain: English Language, Vocabulary, Business Writing, Interviewing Skills, Verbal Communication Skills, Business Correspondence, Social Skills, Professional Networking, Professional Development, Business Communication

      4.8
      Rating, 4.8 out of 5 stars
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      17K reviews

      Mixed · Course · 1 - 3 Months

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      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
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      51K reviews

      Beginner · Specialization · 3 - 6 Months

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      Imperial College London

      Statistical Analysis with R for Public Health

      Skills you'll gain: Analytical Skills, Correlation Analysis, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Statistics, Statistical Software, Biostatistics, Exploratory Data Analysis, Statistical Analysis, Statistical Programming, Statistics, Statistical Methods, Public Health, Probability & Statistics, Epidemiology, Statistical Modeling

      4.7
      Rating, 4.7 out of 5 stars
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      2K reviews

      Beginner · Specialization · 3 - 6 Months

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      Johns Hopkins University

      Data Science: Foundations using R

      Skills you'll gain: Rmarkdown, Exploratory Data Analysis, Version Control, Statistical Analysis, R Programming, Data Manipulation, Data Cleansing, Data Science, Data Wrangling, Data Visualization, Plot (Graphics), Statistical Programming, Ggplot2, Big Data, Git (Version Control System), Data Integration, Knitr, Data Analysis, Data Sharing, Statistical Reporting

      4.6
      Rating, 4.6 out of 5 stars
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      48K reviews

      Beginner · Specialization · 3 - 6 Months

    • 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
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      13K reviews

      Beginner · Course · 1 - 3 Months

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      University of California, Davis

      Learn SQL Basics for Data Science

      Skills you'll gain: Data Governance, Presentations, SQL, Apache Spark, Distributed Computing, Descriptive Statistics, Data Lakes, Data Storytelling, Data Analysis, Peer Review, Exploratory Data Analysis, Data Quality, Data Pipelines, Databricks, Statistical Analysis, Database Design, Complex Problem Solving, Query Languages, Data Visualization, JSON

      4.6
      Rating, 4.6 out of 5 stars
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      17K reviews

      Beginner · Specialization · 3 - 6 Months

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      Google Cloud

      Google Sheets - Advanced Topics

      Skills you'll gain: Google Sheets, Excel Formulas, Spreadsheet Software, Google Workspace, Data Validation, Data Visualization, Productivity Software, Data Analysis Software, Data Integration, Data Import/Export, Automation

      4.6
      Rating, 4.6 out of 5 stars
      ·
      351 reviews

      Intermediate · Course · 1 - 3 Months

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      Macquarie University

      Excel Skills for Business: Advanced

      Skills you'll gain: Dashboard, Microsoft Excel, Excel Formulas, Spreadsheet Software, Data Analysis Expressions (DAX), Data Cleansing, Finance, Business Intelligence Software, Data Validation, Financial Modeling, Data Visualization, Loans, Depreciation, Automation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6K reviews

      Intermediate · Course · 1 - 3 Months

    Searches related to advanced statistics

    advanced statistics for data science
    advanced quantitative statistics with excel
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    In summary, here are 10 of our most popular advanced statistics courses

    • Data Analysis and Visualization Foundations: IBM
    • Generative AI for Data Scientists: IBM
    • Machine Learning: DeepLearning.AI
    • Probability and Statistics: To p or not to p?: University of London
    • English for Career Development: University of Pennsylvania
    • Data Science: Johns Hopkins University
    • Statistical Analysis with R for Public Health: Imperial College London
    • Data Science: Foundations using R: Johns Hopkins University
    • Data Science Math Skills: Duke University
    • Learn SQL Basics for Data Science: University of California, Davis

    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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