It is essential that these efforts go beyond merely generating new insights from data but also to systematically enhance individual human judgment in real development contexts. How can organizations better manage the process of converting the potential of data science to real development outcomes This course is tailored to put all these important considerations into perspective. It is envisioned that upon completion, the participants will be empowered with the necessary skills to produce accurate and cost effective data and reports that are useful and friendly for decision making. new developments in data science offer a tremendous opportunity to improve decision-making. In the development world, there has been an increase in the number of data gathering initiative such as baseline surveys, Socio-Economic Surveys, Demographic and Health Surveys, Nutrition Surveys, Food Security Surveys, Program Evaluation Surveys, Employees, customers and vendor satisfaction surveys, and opinion polls among others, all intended to provide data for decision making. It will be conducted using ODK, GIS, NVIVO and R
Introduction to statistical concepts
Descriptive Statistics
Inferential statistics
The role and purpose of research design
Types of research designs
The research process
Which method to choose?
Exercise: Identify a project of choice and developing a research design
Types of surveys
The survey process
Survey design
Methods of survey sampling
Determining the Sample size
Planning a survey
Conducting the survey
After the survey
Exercise: Planning for a survey based on the research design selected
Introduction to Mobile Data gathering
Benefits of Mobile Applications
Data and types of Data
Introduction to common mobile based data collection platforms
Managing devices
Challenges of Data Collection
Data aggregation, storage and dissemination
Types of questions
Data types for each question
Types of questionnaire or Form logic
Extended data types geoid, image and multimedia
Design forms using a web interface
ODK Build
Kobo forms
PurcForms
Hands-on Exercise
Installing applications: ODK Collect
Using Google play
Manual install (. apk files)
Configuring the device (Mobile Phones)
Uploading the form into the mobile devices
Hands-on Exercise
Introduction to XLS forms syntax
New data types
Notes and dates
Multiple choice Questions
Multiple Language Support
Hints and Metadata
Hands-on Exercise
Conditional Survey Branching
Required questions
Constraining responses
Skip: Asking Relevant questions
The specify other
Grouping questions
Skipping many questions at once (Skipping a section)
Repeating a set of questions
Special formatting
Making dynamic calculations
ODK Aggregate
Formhub
ona.io
Kobo Toolbox
Uploading forms to the server
Configuring ODK Aggregate on a local server
Downloading data
Manual download (ODK Briefcase)
Using the online server interface
Introduction to GIS for Researchers and data scientists
Qualitative Data
Types of Qualitative Data
Sources of Qualitative data
Qualitative vs Quantitative
NVIVO key terms
The NVIVO Workspace
What is qualitative data analysis
Approaches in Qualitative data analysis; deductive and inductive approach
Points of focus in analysis of text data
Principles of Qualitative data analysis
Process of Qualitative data analysis
NVIVO Key terms
NVIVO interface
NVIVO workspace
Use of NVIVO ribbons
Creating new projects
Creating a new project
Opening and Saving project
Working with Qualitative data files
Importing Documents
Merging and exporting projects
Managing projects
Working with different data sources
Theme codes
Case nodes
Relationships nodes
Node matrices
Type of Nodes,
Creating nodes
Browsing Nodes
Creating Memos
Memos, annotations and links
Creating a linked memo
Source classifications
Case classifications
Node classifications
Creating Attributes within NVIVO
Importing Attributes from a Spreadsheet
Getting Results; Coding Query and Matrix Query
Data-driven vs theory-driven coding
Analytic coding
Descriptive coding
Thematic coding
Tree coding
Organize, store and retrieve data
Cluster sources based on the words they contain
Text searches and word counts through word frequency queries.
Examine themes and structure in your content
Queries for textual analysis
Queries for exploring coding
Content Analysis; Descriptive, interpretative
Narrative Analysis
Discourse Analysis
Grounded Theory
Comparing analysis results with research questions
Summarizing finding under major categories
Drawing conclusions and lessons learned
Display data in charts
Creating models and graphs to visualize connections
Tree maps and cluster analysis diagrams
Display your data in charts
Create models and graphs to visualize connections
Create reports and extracts
Triangulating with quantitative data
Using different participatory techniques to measure the same indicator
Comparing analysis from different data sources
Checking the consistency on respondent on similar topic
Qualitative report format
Reporting qualitative research
Reporting content
Interpretation
Introduction to the Instructor and Course
Data & Code Used in the Course
Statistics in the Real World
Designing Studies & Collecting Good Quality Data
Different Types of Data
Rationale for this section
Introduction to the R Statistical Software & R Studio
Different Data Structures in R
Reading in Data from Different Sources
Indexing and Subletting of Data
Data Cleaning: Removing Missing Values
Exploratory Data Analysis in R
Quantitative Data
Measures of Center
Measures of Variation
Charting & Graphing Continuous Data
Charting & Graphing Discrete Data
Deriving Insights from Qualitative/Nominal Data
Data Distribution: Normal Distribution
Checking for Normal Distribution
Standard Normal Distribution and Z-scores
Confidence Interval-Theory
Confidence Interval-Computation in R
Hypothesis Testing
T-tests: Application in R
Non-Parametric Alternatives to T-Tests
One-way ANOVA
Non-parametric version of One-way ANOVA
Two-way ANOVA
Power Test for Detecting Effect
Explore the Relationship Between Two Quantitative Variables
Correlation
Linear Regression-Theory
Linear Regression-Implementation in R
Conditions of Linear Regression
Multi-collinearity
Linear Regression and ANOVA
Linear Regression with Categorical Variables and Interaction Terms
Analysis of Covariance (ANCOVA)
Selecting the Most Suitable Regression Model
Violation of Linear Regression Conditions: Transform Variables
Other Regression Techniques When Conditions of OLS Are Not Met
Regression: Standardized Major Axis (SMA) Regression
Polynomial and Non-linear regression
Linear Mixed Effect Models
Generalized Regression Model (GLM)
Logistic Regression in R
Poisson Regression in R
Goodness of fit testing
Introduction Multivariate Analysis
Cluster Analysis/Unsupervised Learning
Principal Component Analysis (PCA)
Linear Discriminant Analysis (LDA)
Correspondence Analysis
Similarity & Dissimilarity Across Sites
Non-metric multi-dimensional scaling (NMDS)
Multivariate Analysis of Variance (MANOVA)
Writing a report from survey data
Communication and dissemination strategy
Context of Decision Making
Improving data use in decision making
Culture Change and Change Management
Preparing a report for the survey, a communication and dissemination plan and a demand and use strategy.
Presentations and joint action planning
Understand and appropriately use statistical terms and concepts
Design and Implement universally acceptable Surveys
Convert data into various formats using appropriate software
Use mobile data gathering tools such as Open Data Kit (ODK)
Use GIS software to plot and display data on basic maps
Qualitative data analysis using NVIVO
Analyze data by applying appropriate statistical techniques using R
Interpret the statistical analysis using R
Identify statistical techniques a best suited to data and questions
Strong foundation in fundamental statistical concepts
Implement different statistical analysis in R and interpret the results
Build intuitive data visualizations
Carry out formalized hypothesis testing
Implement linear modelling techniques such multiple regressions and GLMs
Implement advanced regression analysis and multivariate analysis
Write reports from survey data
Put strategies to improve data demand and use in decision making
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