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;
- Present different types of data in an appropriate manner.
- Perform statistical analysis.
- Present findings of data analysis in a research proposal.
- Identify areas/issues/situations where statistical analysis would be beneficial.
- Collect, analyze and interpret data relevant to their decision-making.
- Identify and interpret trends.
- Use SPSS to calculate statistical measures and interpret SPSS outputs.
- Understand how to apply relevant statistical techniques to solve the underlying
problems/issues. - Report on statistical findings
COURSE MODULES
I.Pre-requisite
Research/Evaluation Design
Data Collection Methods
Introduction to SPSS
II. 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
IV. Advanced
Hypothesis Testing 2: Simple Regression
Hypothesis testing for Multiple regression model
Survival analysis
Life tables
Kaplan-Meier
Cox Regression
V. Communication of results
Critical Issues to note on data analysis during report writing
Policy Implications
Assignments
In order to demonstrate their understanding of the course content, students will be required to
submit three assignments.
DURATION: 3 Months
REGIONS TARGETED: Global
COURSE FEE: Euros 500
LANGUAGE: English
FORMAT: Online Learning
GENERAL COURSE CONTACT: info@globalreliefinstitute.org
