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Results for "Statistical computing (R, Python)"

  • 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: Logistic Regression, Analytical Skills, Correlation Analysis, Science and Research, Regression Analysis, Sampling (Statistics), Statistical Hypothesis Testing, Data Literacy, Data Analysis, R Programming, Descriptive Analytics, Descriptive Statistics, Statistical Software, Statistical Modeling, Biostatistics, Model Evaluation, Exploratory Data Analysis, Statistical Analysis, Statistical Programming, R (Software)

  • Skills you'll gain: Model Evaluation, Sampling (Statistics), NumPy, Pandas (Python Package), Data Literacy, Feature Engineering, Statistical Visualization, Data Analysis, Predictive Modeling, Analytics, Regression Analysis, Predictive Analytics, Statistics, Analysis, Computational Thinking, Data Science, Business Intelligence, Applied Machine Learning, Data Quality, Machine Learning

  • Skills you'll gain: Data Import/Export, Python Programming, NumPy, Scripting, Data Collection, Data Analysis

  • Johns Hopkins University

    Skills you'll gain: R (Software), Statistical Analysis, R Programming, Statistical Programming, Statistical Methods, Data Analysis, Debugging, Simulations, Program Development, Programming Principles, Software Installation, Data Structures, Performance Tuning, Data Import/Export

  • Skills you'll gain: Descriptive Statistics, Data Visualization, Statistical Analysis, Data Presentation, Data Analysis, Probability Distribution, Statistics, Statistical Methods, Statistical Hypothesis Testing, Data Science, Statistical Programming, Data Visualization Software, Probability & Statistics, Jupyter, Regression Analysis, Statistical Modeling, Descriptive Analytics, Statistical Inference, Correlation Analysis, Probability

What brings you to Coursera today?

  • Skills you'll gain: R (Software), Data Manipulation, Web Scraping, R Programming, Data Analysis, Data Science, Data Structures, Data Import/Export, Programming Principles, Jupyter, File I/O, Integrated Development Environments, Development Environment

  • Skills you'll gain: Data Wrangling, Exploratory Data Analysis, Model Evaluation, Data Cleansing, Data Preprocessing, Data Manipulation, Data Analysis, Data Processing, Model Training, Scatter Plots, Statistical Analysis, Predictive Modeling, Regression Analysis, Statistical Methods, Data Transformation, Feature Engineering, Data Import/Export, Scientific Visualization, Data Visualization, Python Programming

  • Skills you'll gain: Data Storage Technologies, Probability & Statistics, Statistics, Data Store, Mathematical Software, Data Storage, Data Access, Database Software, Data Manipulation, Data Transformation, Model Evaluation

  • Skills you'll gain: Git (Version Control System), Data Visualization, Data Presentation, Generative AI, Version Control, Matplotlib, Exploratory Data Analysis, Data Cleansing, Pandas (Python Package), R Programming, Tidyverse (R Package), Ggplot2, Jupyter, Python Programming, Microsoft Visual Studio, Statistical Visualization, Data Storytelling, GitHub, NumPy, Machine Learning Methods

  • Skills you'll gain: Exploratory Data Analysis, Data Wrangling, Statistical Analysis, Data Analysis, Model Evaluation, Data Transformation, R Programming, Data Visualization, Regression Analysis, Predictive Modeling, Statistical Methods, R (Software), Data Manipulation, Plot (Graphics), Data Science, Box Plots, Statistical Visualization, Correlation Analysis, Model Training, Tidyverse (R Package)

  • Skills you'll gain: Generative AI, Matplotlib, Data Visualization, Data Literacy, Plotly, Data Ethics, Plot (Graphics), Generative Model Architectures, Scientific Visualization, Data Cleansing, Data Presentation, Interactive Data Visualization, Pandas (Python Package), Data Quality, Exploratory Data Analysis, Generative Adversarial Networks (GANs), Data Manipulation, Model Evaluation, Data Analysis, Machine Learning