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learn data analysis with rstudio in four weeks

Are you interested in learning how to use RStudio, a powerful tool for data analysis, visualization, and statistical computing?

If yes, you are invited to join our upcoming training workshop on RStudio, specially designed for data enthusiasts, students, and researchers who want to master this skill. 

RStudio is an integrated development environment (IDE) for R and Python, two popular programming languages for data science. An IDE software application helps you write, run, debug, and conveniently organize your code. 

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Introduction of R

RStudio has many features that make it easy to use and learn, such as: 

  • Code highlighting: This feature shows different parts of your code in different colors, making it easier to read and understand. 
  • Automatic bracket matching: This feature helps pair opening and closing brackets correctly, avoiding errors and confusion. 
  • Code completion: This feature suggests possible commands and arguments as you type, saving time and effort. 
  • Smart indentation: This feature aligns your code neatly, making it more organized and readable. 
  • Execute R code directly from the source editor: This feature allows you to run your code without switching to another window or tab, making it more efficient and convenient. 
  • Integrates the tools with R into a single environment: This feature lets you access various tools for data analysis, visualization, and statistical computing within RStudio, such as ggplot2, dplyr, tidyr, purrr, shiny, rmarkdown, flexdashboard, etc. 
  • Quick access to functions and parameters of functions: This feature helps you find information about the functions and their arguments easily without leaving RStudio. 
  • Enables easy navigation to files and functions: This feature helps you locate and open the files and functions you need quickly without wasting time. 

In this workshop, you will learn how to use RStudio effectively for data analysis, visualization, and statistical computing. 

You will be guided by experienced professionals who have extensive expertise in the field of data science. They will share their knowledge and tips through interactive lectures, demonstrations, and exercises. You can also practice your skills on real-world datasets and scenarios. This workshop is suitable for anyone who wants to learn or improve their skills in RStudio, regardless of their prior experience or background. 

We have tailored the content to meet the needs of different skill levels, from beginners to advanced users. You can choose the level that matches your current proficiency and learning goals. By completing this workshop, you will receive a certificate of accomplishment that recognizes your new RStudio and data science expertise. This certificate will be valuable to your resume and portfolio, demonstrating your dedication and excellence in this field. 

It will also enhance your career prospects and opportunities in the data science domain. To ensure you have a productive and enjoyable learning experience, we have prepared comprehensive practice materials and engaging coding exercises. These resources will help you apply what you learn to practical situations effectively. They will also serve as a valuable reference for future learning and improvement. 

The workshop will take place on September-01-2023, Online through Zoom or Youtube. We encourage you to register as soon as possible because the seats are limited. We want to provide each participant with enough attention and feedback from our instructors. Please visit our website, data03.online, and complete the registration form. We are excited to welcome you to this workshop and help you achieve your learning objectives. We hope you will join us on this journey of discovery and growth in data science through RStudio.

Workshop Outline

Week 1: Introduction to R and RStudio

  • Learn the basics of R programming language, such as data types, operators, control structures, functions, and packages.
  • Learn how to use RStudio IDE, such as code editor, console, environment, history, files, plots, help, and viewer panes.
  • Learn how to install and load packages from CRAN and GitHub, such as tidyverse, DataExplorer, look, etc.
  • Learn to import and export data from various sources and formats, such as CSV, Excel, JSON, XML, SQL, etc.
  • Learn how to manipulate data using tidyverse packages, such as dplyr, tidy, stringer, lubricate, etc.
  • Learn how to explore data using summary statistics and descriptive plots, such as histograms, boxplots, scatterplots, etc.

Week 2: Exploratory Data Analysis (EDA) in R

  • Learn how to do EDA in R using DataExplorer and Look packages, which automate the EDA process and generate comprehensive reports.
  • Learn to use advanced EDA techniques, such as correlation analysis, principal component analysis (PCA), cluster analysis, outlier detection, etc.
  • Learn how to visualize data using the ggplot2 package, a powerful and flexible tool for creating aesthetic and informative graphics.
  • Learn how to customize and enhance your plots using various options and features of ggplot2, such as themes, scales, facets, labels, legends, etc.
  • Learn how to create interactive plots using the Plotly package, which allows you to add interactivity and animation to your ggplot2 graphics.

Week 3: Machine Learning in R

  • Learn the basics of machine learning (ML), such as unsupervised and supervised learning, classification, regression problems, training and testing data sets, model evaluation metrics, etc.
  • Learn to use the caret package, a unified interface for various ML algorithms and methods in R.
  • Learn how to perform data preprocessing steps using a caret package, such as data splitting, feature selection, feature engineering, data imputation, data transformation, etc.
  • Learn how to train and tune various ML models using caret package, such as linear regression, logistic regression, k-nearest neighbors (KNN), decision trees (CART), random forests (RF), support vector machines (SVM), neural networks (NN), etc.
  • Learn how to compare and select the best ML model using a caret package, such as resampling methods (cross-validation), performance metrics (accuracy, precision, recall), confusion matrix (TPR/FPR), ROC curve (AUC), etc.

Week 4: Machine Learning Applications in R

  • Learn how to apply ML models to real-world problems and scenarios using various case studies and examples in R.
  • Learn to use ML models for prediction and inference using predict function and other methods in R.
  • Learn how to explain the results of ML models using various techniques and tools in R.
  • Learn how to communicate and present your findings and insights using the markdown package, which allows you to create dynamic documents that combine code, text, and visuals in a single file.
  • Learn how to create reproducible reports with the markdown package, which supports various output formats like HTML, PDF, Word, PowerPoint, etc.


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