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    Results for "advanced statistics"

    • T

      Tableau Learning Partner

      Advanced Data Visualization with Tableau

      Skills you'll gain: Data Visualization Software, Spatial Data Analysis, Tableau Software, Data Storytelling, Statistical Visualization, Interactive Data Visualization, Data Presentation, Stakeholder Communications, Geospatial Mapping, Business Reporting, Business Analytics

      4.9
      Rating, 4.9 out of 5 stars
      ·
      89 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: New
      New
      Status: AI skills
      AI skills
      D

      DeepLearning.AI

      DeepLearning.AI Data Analytics

      Skills you'll gain: Data Storytelling, Google Sheets, Data Visualization, Data Presentation, Spreadsheet Software, Large Language Modeling, Web Scraping, Dashboard, Pandas (Python Package), Interactive Data Visualization, Time Series Analysis and Forecasting, Probability & Statistics, Statistical Analysis, Statistics, Statistical Modeling, Matplotlib, Data Visualization Software, Data Manipulation, Data Processing, JSON

      4.7
      Rating, 4.7 out of 5 stars
      ·
      65 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • U

      University of Colorado Boulder

      Modern Regression Analysis in R

      Skills you'll gain: Statistical Inference, Statistical Modeling, Regression Analysis, Data Ethics, Statistical Methods, Statistical Hypothesis Testing, Data Science, R Programming, Data Modeling, Statistical Analysis, Predictive Modeling, Probability & Statistics, Correlation Analysis, Forecasting, Linear Algebra

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      30 reviews

      Intermediate · Course · 1 - 3 Months

    • T

      The State University of New York

      Advanced Information Literacy

      Skills you'll gain: Information Management, Research, Literacy, Lifelong Learning, Critical Thinking, Investigation, Intellectual Property, Planning, Journals, Style Guides

      4.9
      Rating, 4.9 out of 5 stars
      ·
      7 reviews

      Intermediate · Course · 1 - 3 Months

    • J

      Johns Hopkins University

      Getting and Cleaning Data

      Skills you'll gain: Data Manipulation, Data Cleansing, Data Wrangling, Data Integration, Data Quality, Data Transformation, Data Import/Export, Data Collection, Data Management, Web Scraping, Data Access, R Programming, Exploratory Data Analysis, MySQL, File Management, SQL, Application Programming Interface (API)

      4.5
      Rating, 4.5 out of 5 stars
      ·
      8.1K reviews

      Mixed · Course · 1 - 4 Weeks

    • U

      University of Colorado Boulder

      Data Science Foundations: Statistical Inference

      Skills you'll gain: Statistical Hypothesis Testing, Probability, Probability & Statistics, Probability Distribution, Statistical Inference, Statistical Methods, Bayesian Statistics, Sampling (Statistics), Data Ethics, Data Science, Statistical Analysis, Quantitative Research, Statistics, Sample Size Determination, Descriptive Statistics, Correlation Analysis

      Build toward a degree

      4.4
      Rating, 4.4 out of 5 stars
      ·
      322 reviews

      Intermediate · Specialization · 3 - 6 Months

    • K

      Kennesaw State University

      Six Sigma and the Organization (Advanced)

      Skills you'll gain: Quality Improvement, Lean Six Sigma, Lean Methodologies, Six Sigma Methodology, Process Improvement, Operational Excellence, Process Analysis, Business Process Management, Continuous Improvement Process, Project Management, Project Planning, Quality Control, Root Cause Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.6K reviews

      Intermediate · Course · 1 - 3 Months

    • I

      IBM

      Exploratory Data Analysis for Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Statistical Inference, Data Processing, Data Access, Anomaly Detection, Statistical Analysis, Data Analysis, Data Cleansing, Data Manipulation, Machine Learning, Probability & Statistics, Data Transformation, Workflow Management, Scalability

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.2K reviews

      Intermediate · Course · 1 - 3 Months

    • D

      DeepLearning.AI

      TensorFlow: Data and Deployment

      Skills you'll gain: Extract, Transform, Load, Data Pipelines, Image Analysis, Data Import/Export, Tensorflow, iOS Development, Application Deployment, Android Development, Data Processing, Computer Vision, MLOps (Machine Learning Operations), Swift Programming, Feature Engineering, Mobile Development, Data Transformation, JSON, Applied Machine Learning, Data Sharing, Javascript, Deep Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      1.5K reviews

      Intermediate · Specialization · 3 - 6 Months

    • W

      Workday

      Workday Basics Series

      Skills you'll gain: Workday (Software), Business Process, Workflow Management, Business Reporting, System Configuration, Role-Based Access Control (RBAC), Enterprise Security, Organizational Structure, Data Access, Multi-Tenant Cloud Environments

      4.8
      Rating, 4.8 out of 5 stars
      ·
      269 reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      Statistical Analysis Fundamentals using Excel

      Skills you'll gain: Descriptive Statistics, Regression Analysis, Forecasting, Probability Distribution, Business Analytics, Data Analysis, Statistical Analysis, Statistical Methods, Microsoft Excel, Statistics, Spreadsheet Software, Predictive Analytics, Probability

      4.6
      Rating, 4.6 out of 5 stars
      ·
      89 reviews

      Intermediate · Course · 1 - 3 Months

    • U

      University of Amsterdam

      Quantitative Methods

      Skills you'll gain: Scientific Methods, Research Design, Sampling (Statistics), Science and Research, Research, Research Methodologies, Surveys, Quantitative Research, Social Sciences, Experimentation, Ethical Standards And Conduct

      4.7
      Rating, 4.7 out of 5 stars
      ·
      2.3K reviews

      Mixed · Course · 1 - 3 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…202122…262

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

    • Advanced Data Visualization with Tableau: Tableau Learning Partner
    • DeepLearning.AI Data Analytics: DeepLearning.AI
    • Modern Regression Analysis in R: University of Colorado Boulder
    • Advanced Information Literacy: The State University of New York
    • Getting and Cleaning Data: Johns Hopkins University
    • Data Science Foundations: Statistical Inference: University of Colorado Boulder
    • Six Sigma and the Organization (Advanced): Kennesaw State University
    • Exploratory Data Analysis for Machine Learning: IBM
    • TensorFlow: Data and Deployment: DeepLearning.AI
    • Workday Basics Series: Workday

    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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