- Day 1-2: Introduction to R-Studio IDE and its interface
- Day 3-4: Basic file management in R-Studio: reading, writing, and navigating directories
- Day 5-7: Understanding different data types in R: numeric, character, logical, factor, etc.
Week 2: Data Frames and Missing Data Management
- Day 8-9: Introduction to data frames and their importance in R
- Day 10-11: Handling missing data: identifying, removing, and imputing missing values
- Day 12-14: Data organization techniques: sorting, filtering, and arranging data frames
Week 3: Data Manipulation and Exploration
- Day 15-16: Subsetting data: selecting rows and columns based on conditions
- Day 17-18: Advanced data control techniques: merging, reshaping, and transforming data frames
- Day 19-21: Exploratory data analysis: summary statistics, distribution visualization, and correlation analysis
Week 4: Advanced Data Analysis
- Day 22-23: Cross tabulation and contingency tables: analyzing categorical data relationships
- Day 24-25: Matrix manipulation: operations on matrices and their applications
- Day 26-28: Outlier detection and treatment: identifying and handling outliers in data analysis
Final Day: Review and Project Work
- Day 29-30: Review of key concepts and techniques covered throughout the course
- Students work on a project applying their knowledge of R-Studio and data analysis techniques
Throughout the course, hands-on exercises, real-world examples, and practical projects should be incorporated to reinforce learning and provide opportunities for application. Additionally, encourage students to explore additional resources and practice regularly to solidify their understanding of R-Studio and data analysis.
PAPER SECOND
Data Visualization and Basic Statistics
Week 1: Introduction to Data Visualization and Basic Statistics
Day 1-2: Introduction to Categorical Data and Barplots
Understanding categorical data
Introduction to barplots
Practical exercises on creating and interpreting barplots
Day 3-4: Advanced Barplots: Stack Bar Chart and Pie Chart
Introduction to stack bar chart
Creating and interpreting stack bar charts
Introduction to pie chart
Practical exercises on creating and interpreting pie charts
Week 2: Plotting Matric Data and Basic Statistical Concepts
Day 5-6: Plotting Matric Data
Introduction to line charts
Introduction to histograms
Introduction to index plots
Practical exercises on creating and interpreting line charts, histograms, and index plots
Day 7-8: Exploring Relationships: Scatter Plots and Box Plots
Introduction to scatter plots
Introduction to box plots
Practical exercises on creating and interpreting scatter plots and box plots
Week 3: Foundation Statistics: Univariate and Bivariate Analysis
Day 9-10: Descriptive Statistics
Measures of central tendency and dispersion
Visualization of descriptive statistics
Practical exercises on calculating and interpreting descriptive statistics
Day 11-12: Bivariate Analysis
Introduction to bivariate statistics
Correlation analysis
Practical exercises on conducting and interpreting correlation analysis
Week 4: Psych package
Day 1: Descriptive statistics
Day 2: Correlation statistics
Day 3: Reliability
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PAPER THREE
Week 1: Introduction to Advanced Statistics
Overview of Advanced Statistics
Review of Basic Statistical Concepts
Introduction to R Studio and Psych Package
Descriptive Statistics using psych package
Week 2: Exploratory Data Analysis
Exploratory Factor Analysis (EFA) using psych package
Factor Rotation Techniques
Interpreting EFA Results
Week 3: Confirmatory Factor Analysis (CFA) and Reliability Analysis
Principles of CFA
Conducting CFA using psych package
Assessing Model Fit and Interpretation
Reliability Analysis using psych package
Week 4: Advanced Techniques
Item Response Theory (IRT) Fundamentals
Application of IRT using psych package
Cluster Analysis Techniques
Hierarchical and K-means Cluster Analysis using psych package
Introduction to ANOVA and its application
Each week can include theoretical concepts, practical demonstrations in R Studio, and hands-on exercises for students to reinforce their learning. Additionally, assignments and a final project can be incorporated to assess understanding and application of the topics covered.
