Statistical Modeling

Statistical Modeling is a quantitative method used to predict or explain data patterns by applying statistical processes. Coursera's Statistical Modeling catalogue equips you with the knowledge and skills to create, interpret, and critique various statistical models. You'll learn fundamental concepts like regression analysis, predictive modeling, probability distribution, hypothesis testing, and Bayesian statistics. This learning will enhance your ability to make data-driven decisions in roles such as data analyst, statistician, economist, machine learning engineer, or any profession requiring strong analytical skills. You'll also explore the use of statistical software tools and packages, making you proficient in applying statistical modeling to real-world problems.

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Results for "Statistical Modeling"

  • Skills you'll gain: Statistical Inference, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, R Programming, Data Ethics, Statistical Analysis, Experimentation, Research Design, Statistical Methods, Data Science, Data Analysis, R (Software), Statistical Programming, Predictive Modeling, Predictive Analytics, Probability & Statistics, Advanced Analytics, Data Modeling, General Science and Research

  • Skills you'll gain: Descriptive Statistics, Data Analysis, Predictive Modeling, Predictive Analytics, Analytics, Statistical Modeling, Business Analytics, Statistical Hypothesis Testing, Exploratory Data Analysis, Data-Driven Decision-Making, Customer Analysis, Data Science, Model Evaluation, Scikit Learn (Machine Learning Library), Statistical Analysis, Feature Engineering, Applied Machine Learning, Data Visualization, Statistical Inference, Supervised Learning

  • Skills you'll gain: Risk Modeling, Descriptive Statistics, Financial Modeling, Regression Analysis, Statistical Modeling, Financial Analysis, Decision Tree Learning, Credit Risk, Lending and Underwriting, Predictive Modeling, Predictive Analytics, Commercial Lending, Portfolio Management, Statistics, Portfolio Risk, Statistical Analysis, Statistical Methods, Performance Metric, Model Evaluation, Supervised Learning

  • Johns Hopkins University

    Skills you'll gain: Regression Analysis, Statistical Analysis, Statistical Modeling, Logistic Regression, Data Science, Data Analysis, Statistical Methods, Model Evaluation, Probability & Statistics, Statistical Inference, Statistical Hypothesis Testing

  • From the course: Statistics Foundations·Lesson: Statistical Modeling

  • Johns Hopkins University

    Skills you'll gain: Statistical Inference, Statistical Hypothesis Testing, Probability & Statistics, Probability, Statistics, Bayesian Statistics, Statistical Methods, Statistical Modeling, Statistical Analysis, Probability Distribution, Sampling (Statistics), Sample Size Determination, Data Analysis

  • University of Michigan

    Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Methods, Statistical Inference, Statistics, Bayesian Statistics, Data Visualization, Plot (Graphics), Data Literacy, Scientific Visualization, Matplotlib, Statistical Visualization, Statistical Software, Probability & Statistics, Model Evaluation, Data-Driven Decision-Making, Statistical Analysis, Jupyter, Python Programming

  • Skills you'll gain: Descriptive Analytics, SPSS, Logistic Regression, SPSS (Software), Regression Analysis, Analytics, Advanced Analytics, Correlation Analysis, Data Analysis, Statistical Reporting, Descriptive Statistics, Statistical Modeling, Exploratory Data Analysis, Data Storytelling, Statistical Analysis, Predictive Modeling, Data Presentation, Predictive Analytics, Statistical Visualization, Scatter Plots

  • From the course: Foundations of strategic business analytics·Lesson: Wrap-up: identifying causes to effects

  • University of Colorado Boulder

    Skills you'll gain: Statistical Analysis, Statistical Methods, Matplotlib, Regression Analysis, Machine Learning Methods, Statistical Modeling, Dimensionality Reduction, Unsupervised Learning, Machine Learning Algorithms, Applied Machine Learning, Machine Learning, Data Science, Environmental Policy, Data Analysis, Climate Change Programs, Energy and Utilities, Data Visualization, Pandas (Python Package), Environmental Issue, Climate Change Mitigation

  • From the course: Data Science Fundamentals, Part 2: Statistical Modeling and ML·Lesson: Statistical Modeling and Machine Learning

  • From the course: Probability, Statistical Inference and Regression Analysis·Lesson: How Big Data Impacts Statistics