Statistical analysis and visualization with R

Module 1: Introduction to R and RStudio

1. Introduction to R:
   • Installing R and RStudio.
   • Basic R syntax and data types.
2. RStudio Interface:
   • Navigating the RStudio environment.
   • Working with scripts, consoles, and environments.

Module 2: Data Import and Cleaning

1. Data Import:
   • Reading data from different file formats (CSV, Excel, etc.).
   • Connecting to databases.
2. Data Cleaning:
   • Handling missing data.
   • Data transformation and reshaping.

Module 3: Exploratory Data Analysis (EDA)

1. Descriptive Statistics:
   • Calculating measures of central tendency and dispersion.
   • Creating summary tables.
2. Data Visualization with ggplot2:
   • Creating scatter plots, bar charts, and histograms.
   • Customizing plots with ggplot2.

Module 4: Statistical Inference

1. Hypothesis Testing:
   • Understanding null and alternative hypotheses.
   • Conducting t-tests, chi-square tests, and ANOVA.
2. Confidence Intervals:
   • Calculating confidence intervals for parameters.
   • Interpreting results.

Module 5: Regression Analysis

1. Linear Regression:
   • Building and interpreting linear regression models.
   • Model diagnostics and assumptions.
2. Logistic Regression:
   • Introduction to logistic regression.
   • Binary and multinomial logistic regression.

Module 6: Multivariate Analysis

1. Principal Component Analysis (PCA):
   • Reducing dimensionality with PCA.
   • Visualizing high-dimensional data.
2. Cluster Analysis:
   • Hierarchical and k-means clustering.
   • Assessing cluster validity.

Module 7: Time Series Analysis

1. Introduction to Time Series:
   • Understanding time series data.
   • Time series decomposition.
2. ARIMA Modeling:
   • Building and interpreting ARIMA models.
   • Forecasting time series data.

Module 8: Advanced Topics

1. Machine Learning with R:
   • Overview of machine learning algorithms.
   • Implementing models with caret.
2. Text Mining and Sentiment Analysis:
   • Analyzing text data with R.
   • Extracting insights from text.

Module 9: Real-world Applications

1. Case Studies:
   • Applying statistical analysis to real-world problems.
   • Analyzing and interpreting results.
2. Capstone Project:
   • Undertaking a comprehensive data analysis project.
   • Presenting findings and insights.

Additional Considerations:

• Hands-On Exercises and Labs:
  • Practical application of statistical concepts with real datasets.
  • Group exercises and coding labs.
• Interactive Sessions:
  • Q&A sessions and discussions.
  • Peer-to-peer learning activities.
• Resources and Further Learning:
  • Providing additional resources for self-paced learning.
  • Sharing relevant books, online courses, and forums.