Certificate Course in Data Analysis

Course Overview

The world is awash in data; currently there are some 500 billion gigabytes stored an amount that doubles every 18 months. But data is only useful if it is turned into information. That’s the job of a data analyst.

Data analysis is the science of correctly collecting data, assessing it for trustworthiness, extracting information from it, and presenting it in a comprehensible informative way. These skills are vital to institutions such as government, business, or health care where sound decisions must be made based on data and the way it is interpreted.

This data analyst training program is designed for practitioners looking to derive answers from raw data, including “big data” sets, using a comprehensive range of statistical analyses and methods. If you’re responsible for organizing and analyzing complex data, regardless of what industry you’re in, you will benefit from the data analysis training offered in our online program.

Learning Outcomes;

By the end of this course the learner should be able to;

  1. Present different types of data in an appropriate manner.
  2. Perform statistical analysis.
  3. Present findings of data analysis in a research proposal.
  4. Identify areas/issues/situations where statistical analysis would be beneficial.
  5. Collect, analyze and interpret data relevant to their decision-making.
  6. Identify and interpret trends.
  7. Use SPSS to calculate statistical measures and interpret SPSS outputs.
  8. Understand how to apply relevant statistical techniques to solve the underlying problems/issues.
  9. Report on statistical findings




  • Research/Evaluation Design
  • Data Collection Methods
  • Introduction to SPSS
  1. Beginner
  • Introduction to Data Analysis
  • Types of Data
  • Data Measurements
  • Descriptive Statistics
  • Proportions, Rates, Ratio’s, Percentages
  • Central Location,
  • Dispersion,
  • Shape of Distribution
  • Display of data
  • frequency tables, charts and graphs

III.          Intermediate

Introduction to probability

  • Hypothesis Testing 1: Significance Testing (P-value) and Point Estimates (CI)
  • Sampling and Sampling errors
  • Probability Distribution (Binomial/Poisson)
  • Cross-tabulation and Correlation
  1. Advanced
  • Hypothesis Testing 2: Simple Regression
  • Hypothesis testing for Multiple regression model
  • Survival analysis
  • Life tables
  • Kaplan-Meier
  • Cox Regression
  1. Communication of results
  • Critical Issues to note on data analysis during report writing
  • Policy Implications



In order to demonstrate their understanding of the course content, students will be required to submit three assignments.


DURATION: 3 Months



ORGANIZERS: Development Dimensions Africa


FORMAT: Online Learning

GENERAL COURSE CONTACT: infoinfo@humanitarianagenda.org


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