INTRODUCTION
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 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 ten days hands-on course is tailored to put all these
important consideration 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.
It will be conducted using ODK, GIS, NVIVO and PYTHON
DURATION
2 Weeks
LEARNING OBJECTIVES
· 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
· Python
for Data Science and Machine
· Spark
for Big Data Analysis
· Implement
Machine Learning Algorithms
· Numbly
for Numerical Data
· Pandas
for Data Analysis
· Matplotlib
for Python Plotting
· Seaborn
for statistical plots
· interactive
dynamic visualizations
· SciKit-Learn
for Machine Learning Tasks
· K-Means
Clustering, Logistic Regression and Linear Regression
· Random
Forest and Decision Trees
· Natural
Language Processing and Spam Filters
· Neural
Networks
· Support
Vector Machines
· Write
reports from survey data
· Put
strategies to improve data demand and use in decision making
WHO SHOULD ATTEND?
This is a general course targeting participants with
elementary knowledge of Statistics from Agriculture, Economics, Food Security
and Livelihoods, Nutrition, Education, Medical or public health professionals
among others who already have some statistical knowledge, but wish to be
conversant with the concepts and applications of statistical modeling.
TOPICS TO BE COVERED
Module1: Basic statistical terms and concepts
· Introduction
to statistical concepts
· Descriptive
Statistics
· Inferential
statistics
Module 2:Research Design
· 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
Module 3: Survey Planning, Implementation and Completion
· 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
Module 4:Introduction
· 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
Module 5:Survey Authoring
· Design
forms using a web interface using:
o ODK Build
o Koboforms
o PurcForms
· Hands-on
Exercise
Module 6:Preparing the mobile phone
for data collection
· Installing
applications: ODK Collect
o Using Google play
o Manual install (.apk files)
· Configuring
the device (Mobile Phones)
· Uploading
the form into the mobile devices
· Hands-on
Exercise
Module 7:Designing forms manually:
Using XLS Forms
· Introduction
to XLS forms syntax
· New
data types
· Notes
and dates
· Multiple
choice Questions
· Multiple
Language Support
· Hints
and Metadata
· Hands-on
Exercise
Module 8:Advanced survey Authoring
· Conditional
Survey Branching
o Required questions
o Constraining responses
o Skip: Asking Relevant questions
o The specify other
· Grouping
questions
o Skipping many questions at once
(Skipping a section)
· Repeating
a set of questions
· Special
formatting
· Making
dynamic calculations
Module 9:Hosting survey data
(Online)
· ODK
Aggregate
· Formhub
· ona.io
· KoboToolbox
· Uploading
forms to the server
Module 10:Hosting Survey Data
(Configuring a local server)
· Configuring
ODK Aggregate on a local server
· Downloading
data
· Manual
download (ODK Briefcase)
· Using
the online server interface
Module 11: GIS mapping of survey data using QGIS
· Introduction
to GIS for Researchers and data scientists
· Importing
survey data into a GIS
· Mapping
of survey data using QGIS
· Exercise:
QGIS mapping exercise.
Module 12:Understanding Qualitative Research
· Qualitative
Data
· Types
of Qualitative Data
· Sources
of Qualitative data
· Qualitative
vs Quantitative
· NVivo
key terms
· The
NVivo Workspace
Module 13:Preliminaries of Qualitative data Analysis
· 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
Module 14:Introduction to NVIVO
· NVIVO
Key terms
· NVIVO
interface
· NVIVO
workspace
· Use
of NVIVO ribbons
Module 15:NVIVO Projects
· 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
Module 16:Nodes in NVIVO
· 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
Module 17:Classes and summaries
· Source
classifications
· Case
classifications
· Node
classifications
· Creating
Attributes within NVivo
· Importing
Attributes from a Spreadsheet
· Getting
Results; Coding Query and Matrix Query
Module 18: Coding
· Data-driven
vs theory-driven coding
· Analytic
coding
· Descriptive
coding
· Thematic
coding
· Tree
coding
Module 19:Thematic Analytics in NVIVO
· 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
Module 20:Queries using NVIVO
· Queries
for textual analysis
· Queries
for exploring coding
Module 21: Building on the Analysis
· Content
Analysis; Descriptive, interpretative
· Narrative
Analysis
· Discourse
Analysis
· Grounded
Theory
Module 22: Qualitative Analysis Results Interpretation
· Comparing
analysis results with research questions
· Summarizing
finding under major categories
· Drawing
conclusions and lessons learned
Module 23: Visualizing NVIVO project
· 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
Module 24: Triangulating results and Sources
· 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
Module 25: Report Writing
· Qualitative
report format
· Reporting
qualitative research
· Reporting
content
· Interpretation
Module 26: Introduction to Phython
· Course
Intro
· Setup
· Installation
Setup and Overview
· IDEs
and Course Resources
· iPython/Jupyter
Notebook Overview
Module 27:Learning Numpy
· Intro
to numpy
· Creating
arrays
· Using
arrays and scalars
· Indexing
Arrays
· Array
Transposition
· Universal
Array Function
· Array
Processing
· Array
Input and Output
Module 28: Intro to Pandas
· DataFrames
· Index
objects
· Reindex
· Drop
Entry
· Selecting
Entries
· Data
Alignment
· Rank
and Sort
· Summary
Statistics
· Missing
Data
· Index
Hierarchy
Module 29: Working with Data
· Reading
and Writing Text Files
· JSON
with Python
· HTML
with Python
· Microsoft
Excel files with Python
· Merge
and Merge on Index
· Concatenate
and Combining DataFrames
· Reshaping,
Pivoting and Duplicates in Data Frames
· Mapping,Replace,Rename
Index,Binning,Outliers and Permutation
· GroupBy
on DataFrames
· GroupBy
on Dict and Series
· Splitting
Applying and Combining
· Cross
Tabulation
Module 30:Big Data and Spark with Python
· Welcome
to the Big Data Section!
· Big
Data Overview
· Spark
Overview
· Local
Spark Set-Up
· AWS
Account Set-Up
· Quick
Note on AWS Security
· EC2
Instance Set-Up
· SSH
with Mac or Linux
· PySpark
Setup
· Lambda
Expressions Review
· Introduction
to Spark and Python
· RDD
Transformations and Actions
Module 31: Data Visualization
· Installing
Seaborn
· Histograms
· Kernel
Density Estimate Plots
· Combining
Plot Styles
· Box
and Violin Plots
· Regression
Plots
· Heatmaps
and Clustered Matrices
Module 32: Data Analysis
· Linear
Regression
· Support
Vector
· Decision
Trees and Random Forests
· Natural
Language Processing
· Discrete
Uniform Distribution
· Continuous
Uniform Distribution
· Binomial
Distribution
· Poisson
Distribution
· Normal
Distribution
· Sampling
Techniques
· T-Distribution
· Hypothesis
Testing and Confidence Intervals
· Chi
Square Test and Distribution
Module 33: Report writing for surveys, data dissemination,
demand and use
· 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
General Notes
·
All our courses can be
Tailor-made to participants needs
·
The participant must be
conversant with English
·
Presentations are
well guided, practical exercises, web-based tutorials, and group work. Our
facilitators are experts with more than 10years of experience
·
Upon completion of
training, the participant will be issued with a Global King Project Management
certificate
·
Training will be done at
the Global King Project Management Centers (Nairobi Kenya, Mombasa Kenya,
Kigali Rwanda, Dubai ,Lagos Nigeria and More others).
·
A discount of 20% will
be given to more than 4 participants from same organization.
·
Course duration is
flexible and the contents can be modified to fit any number of
days.
·
Payment should be done
before commencement of the training, to the Global King Project Management
account, so as to enable us to prepare better for you.
·
For any inquiry
to: [email protected] or +254 114 830 889
·
Website: www.globalkingprojectmanagement.org
·
Tablet and Laptops are
provided to participants on request as an add-on cost to the training fee
·
The course fee for
onsite training includes facilitation training materials, 2 coffee breaks, a
buffet lunch, and a Certificate of successful completion of Training.
Participants will be responsible for their own travel expenses and
arrangements, airport transfers, visa application dinners, health/accident
insurance, and other personal expenses.
Start Date | End Date | Action | Duration | Location | Fee(Kes) | Fee(Us$) |
---|---|---|---|---|---|---|
04/09/2023 | 15/09/2023 | 10Days | Nairobi | 200000 | 2400 | |
02/10/2023 | 13/10/2023 | 10Days | Nairobi | 200000 | 2400 | |
19/02/2024 | 01/03/2024 | 10Days | Nairobi | 200000 | 2400 | |
18/03/2024 | 29/03/2024 | 10Days | Nairobi | 200000 | 2400 | |
15/04/2024 | 26/04/2024 | 10Days | Nairobi | 200000 | 2400 | |
30/10/2023 | 10/11/2023 | 10Days | Mombasa | 240000 | 3000 | |
27/11/2023 | 08/12/2023 | 10Days | Mombasa | 240000 | 3000 | |
22/01/2024 | 02/02/2024 | 10Days | Mombasa | 240000 | 3000 |