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Roger Peng s R Programing

Track :

Programming

Lessons no : 55

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What will you learn in this course?
  • Master R programming for statistical analysis, data visualization, and data manipulation using R language and RStudio tools
  • Apply R to perform data cleaning, transformation, and exploratory data analysis for real-world datasets
  • Create compelling data visualizations and graphics with ggplot2, lattice, and base R plotting functions
  • Implement statistical models and hypothesis testing techniques using R for accurate data insights
  • Develop reproducible research workflows and scripts in R to ensure transparency and efficiency
  • Utilize R packages for advanced data analysis, machine learning, and predictive modeling tasks
  • Interpret and communicate data analysis results effectively through visualizations and reports in R
  • Troubleshoot common R programming errors and optimize code for performance and scalability
  • Integrate R with other data science tools and databases for comprehensive data analysis projects
  • Leverage R for automation of data workflows, report generation, and dynamic dashboards
  • Apply best practices in data security, version control, and ethical considerations in R programming
  • Build a strong foundation in R programming to support further learning in data science and analytics

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Lessons | 55
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If you're passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you're interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit .