Research Design,ODK Mobile Data Collection,GIS Mapping,Data Analysis Using NVIVO And PYTHON Course

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.