Professional Certificate in Data Analysis Techniques
- Assess a dataset for completeness, accuracy and consistency and document cleaning steps.
- Summarise data using measures of central tendency, dispersion and distribution shape.
- Explore relationships through cross-tabulations, scatter plots and correlation coefficients.
- Calculate confidence intervals and perform hypothesis tests for means and proportions.
- Fit and interpret a simple linear regression for a business variable.
- Write a short analytical report that states the question, method, findings and limitations.
- Data Foundations
- Data types and measurement scales
- Data collection methods and sampling
- Ethics and confidentiality of data
- Data Cleaning and Validation
- Detecting errors, duplicates and outliers
- Handling missing data
- Documenting transformations
- Descriptive and Exploratory Analysis
- Central tendency and dispersion
- Distributions and histograms
- Cross-tabulation and grouping
- Probability and Inference
- Probability distributions
- Confidence intervals
- Hypothesis tests for means and proportions
- Relationships Between Variables
- Correlation and its limits
- Simple linear regression
- Interpreting coefficients and fit
- Analysis Tools and Reporting
- Excel and introductory Python workflows
- Visualising findings
- Structuring an analytical report
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Program Professional Framework
Professional Certificate in Data Analysis Techniques
Program Overview
Sound analysis starts with understanding data before applying any tool. This foundation-to-practitioner programme covers the core techniques every analyst should master: data types and collection, sampling, cleaning and validation, descriptive statistics, exploratory analysis and visualisation, probability and distributions, confidence intervals, hypothesis testing, correlation and simple regression, and clear reporting of findings. Exercises use Excel and an introduction to Python on datasets from HR, finance, customer service and operations.
Learning Outcomes
- ✓Assess a dataset for completeness, accuracy and consistency and document cleaning steps.
- ✓Summarise data using measures of central tendency, dispersion and distribution shape.
- ✓Explore relationships through cross-tabulations, scatter plots and correlation coefficients.
- ✓Calculate confidence intervals and perform hypothesis tests for means and proportions.
- ✓Fit and interpret a simple linear regression for a business variable.
- ✓Write a short analytical report that states the question, method, findings and limitations.
Target Audience
- Junior and aspiring data analysts
- Researchers and statistics officers
- HR, finance and operations staff who analyse data
- Quality and performance measurement coordinators
Detailed Program Modules
01Data Foundations
- Data types and measurement scales
- Data collection methods and sampling
- Ethics and confidentiality of data
02Data Cleaning and Validation
- Detecting errors, duplicates and outliers
- Handling missing data
- Documenting transformations
03Descriptive and Exploratory Analysis
- Central tendency and dispersion
- Distributions and histograms
- Cross-tabulation and grouping
04Probability and Inference
- Probability distributions
- Confidence intervals
- Hypothesis tests for means and proportions
05Relationships Between Variables
- Correlation and its limits
- Simple linear regression
- Interpreting coefficients and fit
06Analysis Tools and Reporting
- Excel and introductory Python workflows
- Visualising findings
- Structuring an analytical report
Training Methodology
Applied Project & Assessment
Applied Project
Participants analyse a supplied organisational dataset end to end: data quality assessment, descriptive and exploratory analysis, one hypothesis test, a simple regression and a two-page analytical report with recommendations.
Assessment
Short diagnostic at the start, practical tasks linked to each learning outcome, and evaluation of the applied project against published criteria.


