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  • Applied Statistics

Applied Statistics Courses

Applied statistics courses can help you learn data analysis, hypothesis testing, regression techniques, and experimental design. You can build skills in interpreting data sets, making predictions, and communicating statistical findings effectively. Many courses introduce tools like R, Python, and Excel, that support performing statistical analyses and visualizing data.

Popular Applied Statistics Courses and Certifications


  • Status: Free Trial
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    K

    Kennesaw State University

    Six Sigma Tools for Define and Measure

    Skills you'll gain: Data Collection, Lean Six Sigma, Six Sigma Methodology, Quality Improvement, Process Improvement, Process Improvement and Optimization, Project Design, Project Schedules, Business Process, Project Management, Project Planning, Quality Management, User Feedback, Probability & Statistics, Process Flow Diagrams, Data Validation, Process Mapping, Descriptive Statistics, Stakeholder Engagement, Stakeholder Analysis

    4.6
    Rating, 4.6 out of 5 stars
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    2.6K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    K

    Kennesaw State University

    Six Sigma Black Belt

    Skills you'll gain: Statistical Process Controls, Lean Six Sigma, Lean Methodologies, Six Sigma Methodology, Process Improvement, Team Management, Process Capability, Team Performance Management, Lean Manufacturing, Data Collection, Knowledge Transfer, Management Training And Development, Team Building, Process Improvement and Optimization, Quality Improvement, Statistical Hypothesis Testing, Meeting Facilitation, Continuous Improvement Process, Performance Measurement, Statistics

    4.6
    Rating, 4.6 out of 5 stars
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    862 reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: New
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    Status: Free Trial
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    S

    Sage Publications

    Foundations of Psychological Research Methods

    Skills you'll gain: Surveys, Survey Creation, Research Design, Research, Research Methodologies, Experimentation, Scientific Methods, Science and Research, Qualitative Research, Research and Design, Data Collection, Psychology, Statistical Methods, Case Studies, Sample Size Determination, Statistical Hypothesis Testing, Statistical Inference, Statistics, Data Analysis

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
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    IBM

    IBM Project Manager

    Skills you'll gain: User Story, Risk Management, Team Motivation, Stakeholder Engagement, Project Planning, Peer Review, Agile Software Development, Scrum (Software Development), Project Management Life Cycle, Risk Management Framework, Project Management Institute (PMI) Methodology, Stakeholder Management, Agile Methodology, Stakeholder Communications, Project Management, Interviewing Skills, Professional Networking, Leadership, Agile Project Management, Communication

    4.8
    Rating, 4.8 out of 5 stars
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    5.9K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: Free Trial
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    IBM

    IBM Introduction to Machine Learning

    Skills you'll gain: Unsupervised Learning, Exploratory Data Analysis, Feature Engineering, Dimensionality Reduction, Supervised Learning, Classification Algorithms, Regression Analysis, Scikit Learn (Machine Learning Library), Machine Learning Algorithms, Statistical Methods, Data Preprocessing, Applied Machine Learning, Model Evaluation, Statistical Inference, Predictive Modeling, Machine Learning Methods, Statistical Hypothesis Testing, Model Training, Data Processing, Machine Learning

    4.6
    Rating, 4.6 out of 5 stars
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    3.4K reviews

    Intermediate · Specialization · 3 - 6 Months

  • Status: Preview
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    U

    University of London

    Foundations of Data Science: K-Means Clustering in Python

    Skills you'll gain: Plot (Graphics), Data Visualization, Data Literacy, Statistical Methods, Data Science, Applied Machine Learning, Unsupervised Learning, Statistics, NumPy, Python Programming, Pandas (Python Package), Data Analysis, Machine Learning Algorithms, Descriptive Statistics, Data Processing, Analytics, Machine Learning, Data Manipulation

    4.6
    Rating, 4.6 out of 5 stars
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    735 reviews

    Beginner · Course · 1 - 3 Months

  • Status: New
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    C

    Case Western Reserve University

    Mathematical Thinking for Advanced Mathematics

    Skills you'll gain: Computational Thinking, Mathematical Theory & Analysis, Mathematics and Mathematical Modeling, Advanced Mathematics, Mathematical Modeling, Logical Reasoning, Deductive Reasoning, Critical Thinking, Applied Mathematics, Theoretical Computer Science, General Mathematics, Problem Solving, Mathematics Education, Analytical Skills, Numerical Analysis, Algebra, Geometry, Quantitative Research, Estimation, Verification And Validation

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    D

    DeepLearning.AI

    Linear Algebra for Machine Learning and Data Science

    Skills you'll gain: Linear Algebra, Dimensionality Reduction, Mathematical Software, Machine Learning Methods, Data Transformation, Data Manipulation, Applied Mathematics, Machine Learning, Python Programming, Algebra, Image Analysis

    4.6
    Rating, 4.6 out of 5 stars
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    2.4K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: Free Trial
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    The Hong Kong University of Science and Technology

    Matrix Algebra for Engineers

    Skills you'll gain: Linear Algebra, Engineering Calculations, Algebra, Engineering Analysis, General Mathematics, Advanced Mathematics, Applied Mathematics, Numerical Analysis

    4.9
    Rating, 4.9 out of 5 stars
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    4.7K reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
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    Imperial College London

    Mathematics for Machine Learning: PCA

    Skills you'll gain: Dimensionality Reduction, NumPy, Unsupervised Learning, Statistical Methods, Descriptive Statistics, Jupyter, Statistics, Geometry, Linear Algebra, Data Transformation, Python Programming, Advanced Mathematics, Applied Mathematics, Calculus

    4
    Rating, 4 out of 5 stars
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    3.2K reviews

    Intermediate · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    J

    John Wiley & Sons

    Core Machine Learning Algorithms and Model Validation

    Skills you'll gain: Model Evaluation, Scikit Learn (Machine Learning Library), Keras (Neural Network Library), Machine Learning Algorithms, Model Training, Applied Machine Learning, Deep Learning, Machine Learning Methods, Machine Learning Software, Statistical Modeling, Predictive Modeling, Supervised Learning, Machine Learning, Python Programming, Unsupervised Learning, Data Science, Predictive Analytics, Data Preprocessing, Algorithms, Data Analysis

    Intermediate · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    I

    IBM

    Introduction to Software Engineering

    Skills you'll gain: Software Development Life Cycle, Software Architecture, Full-Stack Web Development, Software Design, Web Development, Software Development Methodologies, Software Design Patterns, Development Environment, Unified Modeling Language, Software Engineering, Web Applications, Software Development Tools, Software Development, Application Deployment, Programming Principles, Front-End Web Development, Web Language, Back-End Web Development, Integrated Development Environments, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
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    4.8K reviews

    Beginner · Course · 1 - 3 Months

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

  • Six Sigma Tools for Define and Measure: Kennesaw State University
  • Six Sigma Black Belt: Kennesaw State University
  • Foundations of Psychological Research Methods: Sage Publications
  • IBM Project Manager: IBM
  • IBM Introduction to Machine Learning: IBM
  • Foundations of Data Science: K-Means Clustering in Python: University of London
  • Mathematical Thinking for Advanced Mathematics: Case Western Reserve University
  • Linear Algebra for Machine Learning and Data Science: DeepLearning.AI
  • Matrix Algebra for Engineers: The Hong Kong University of Science and Technology
  • Mathematics for Machine Learning: PCA: Imperial College London

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