PROFICIENCY DIPLOMA IN STATISTICAL PACKAGES
Course Duration: 4 to 6 months (16 to 24 weeks)
Course Fee: 30,000 Kenyan Shillings
Weekly Commitment: Minimum of 2 hours per day
Course Overview:
This course provides comprehensive training in various statistical software packages used for data analysis and interpretation. It covers both the theoretical foundations of statistical methods and the practical application of these methods using popular software tools.
Semester 1: Introduction to Statistical Software
Week 1-4: Overview of Statistical Analysis
- Introduction to Statistics and Its Applications
- Understanding Data Types and Statistical Measures
- Introduction to Statistical Software (SPSS, SAS, R)
- Practical Exercises: Basic data entry and manipulation in statistical software
Week 5-8: SPSS (Statistical Package for the Social Sciences)
- Data Management in SPSS
- Descriptive Statistics and Data Visualization
- Performing Basic Statistical Tests (T-tests, Chi-Square)
- Practical Exercises: Conducting and interpreting statistical tests in SPSS
Week 9-12: R Programming for Statistics
- Introduction to R and RStudio Environment
- Data Import, Cleaning, and Manipulation in R
- Basic Statistical Analysis and Visualization in R
- Practical Exercises: Writing R scripts for data analysis
Week 13-16: Advanced Statistical Techniques in SAS
- Introduction to SAS Programming Language
- Data Management and Manipulation in SAS
- Performing Regression Analysis and ANOVA
- Practical Exercises: Analyzing datasets using SAS
Semester 2: Advanced Applications of Statistical Packages
Week 1-4: Data Visualization and Reporting
- Advanced Data Visualization Techniques (using R, SPSS, SAS)
- Creating Professional Reports and Presentations
- Practical Exercises: Developing data visualizations and reports
Week 5-8: Multivariate Analysis
- Introduction to Multivariate Statistical Techniques
- Principal Component Analysis (PCA) and Factor Analysis
- Cluster Analysis and Discriminant Analysis
- Practical Exercises: Performing multivariate analysis using statistical software
Week 9-12: Time Series Analysis
- Basics of Time Series Data
- Performing Time Series Analysis (using SPSS, R, SAS)
- Forecasting and Trend Analysis
- Practical Exercises: Analyzing time series data and making forecasts
Week 13-16: Capstone Project and Professional Development
- Designing and Implementing a Statistical Analysis Project
- Applying Knowledge to Real-World Data
- Building a Professional Portfolio and Preparing for Careers
- Practical Exercises: Capstone project presentation and peer review
Certification:
Upon successful completion of the course, students will receive a "Proficiency Diploma in Statistical Packages," acknowledging their competence in using various statistical software for data analysis and interpretation.