Saturday, April 13, 2024

3 months certificate course on R script

Title: 3 months online certificate course on R studio 
Syllabus:
PAPER FIRST
Week 1: Introduction to R-Studio and File Management
  • 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 

_________

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.






 

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