PROFICIENCY CERTIFICATE COURSE IN COMPUTERIZED STATISTICAL PACKAGES
Course Duration and Cost
- Total Duration: 12 weeks (3 months)
- Weekly Commitment: 2 hours per day
- Course Fee: 15,000 Kenya Shillings
Course Overview
This course focuses on equipping participants with skills in using computerized statistical packages for data analysis and interpretation. Participants will learn to manipulate data, perform statistical tests, create visualizations, and generate reports using popular statistical software. The course covers both theoretical foundations and practical applications, preparing individuals for roles requiring proficiency in statistical analysis and data-driven decision-making.
Course Outline
Week 1: Introduction to Statistical Packages
- Overview of statistical software packages
- Choosing the right software for analysis
- Setting up and navigating statistical environments
- Basic operations and data importing
Week 2: Data Management and Manipulation
- Data cleaning and preprocessing techniques
- Data transformation and normalization
- Handling missing data and outliers
- Merging and reshaping datasets
Week 3: Descriptive Statistics
- Calculating measures of central tendency and dispersion
- Generating frequency distributions and histograms
- Summarizing data with descriptive statistics
- Visualizing data using charts and graphs
Week 4: Statistical Tests Part 1
- Parametric vs. non-parametric tests
- T-tests for comparing means
- Chi-square test for categorical data
- ANOVA for comparing multiple groups
Week 5: Statistical Tests Part 2
- Correlation analysis (Pearson, Spearman)
- Regression analysis (linear and logistic)
- Multivariate analysis techniques
- Interpreting statistical test results
Week 6: Advanced Statistical Techniques
- Factor analysis and principal component analysis (PCA)
- Cluster analysis for segmentation
- Time series analysis and forecasting
- Survival analysis and event history modeling
Week 7: Experimental Design and Analysis
- Designing experiments and hypothesis testing
- Analysis of variance (ANOVA) models
- Statistical power and sample size determination
- Interpreting experimental results
Week 8: Reporting and Visualization
- Creating professional reports and presentations
- Exporting results to different formats (PDF, Excel)
- Using templates and themes for visual appeal
- Incorporating statistical output into documents
Week 9: Big Data Analytics
- Introduction to big data concepts
- Handling large datasets (Hadoop, Spark)
- Parallel processing and distributed computing
- Using statistical packages for big data analysis
Week 10: Machine Learning for Statistical Analysis
- Introduction to machine learning algorithms for statistics
- Supervised and unsupervised learning techniques
- Applying machine learning to statistical problems
- Evaluating machine learning models for accuracy
Week 11: Ethical Considerations in Data Analysis
- Data privacy and confidentiality
- Ethical use of statistical models and data
- Bias and fairness in statistical analysis
- Regulatory compliance and guidelines
Week 12: Final Project Development
- Planning and executing a statistical analysis project
- Applying statistical techniques to solve a real-world problem
- Presenting findings and insights
- Graduation and certificate award ceremony
Certification
Upon successful completion, students will receive a proficiency certificate in Computerized Statistical Packages.