Quantitative Data Management, Analysis And Visualization With Python Training Worshop

INTRODUCTION

Unlock the power of Python to analyze big data, create stunning visualizations, and apply advanced machine-learning algorithms in this thorough course. Designed for both beginners with basic programming experience and seasoned developers aiming to transition into data science and big data analysis, this workshop on Quantitative Data Management, Analysis, and Visualization with Python will provide you with the skills and knowledge needed to handle large datasets, perform robust data analysis, and generate meaningful insights. Through hands-on exercises and practical examples, participants will learn to leverage Python's powerful libraries and tools to manage, analyze, and visualize quantitative data effectively. By the end of this course on Quantitative Data Management, Analysis, and Visualization with Python, you will be proficient in using Python for data science tasks, enabling you to drive data-informed decisions and strategies in your organization.

Course Objectives

By the end of this course on Quantitative Data Management, Analysis, and Visualization with Python, participants will be able to:

• Understand research design and its applications
• Utilize Python for data science and machine learning
• Implement Spark for big data analysis
• Apply machine learning algorithms
• Use NumPy for numerical data handling
• Utilize Pandas for data analysis
• Create visualizations with Matplotlib
• Develop statistical plots with Seaborn
• Create interactive dynamic visualizations
• Use SciKit-Learn for machine learning tasks
• Implement K-Means Clustering, Logistic Regression, and Linear Regression
• Apply Random Forests and Decision Trees
• Conduct Natural Language Processing and develop spam filters
• Implement Neural Networks and Support Vector Machines
• Write comprehensive research reports

Duration

10 Days

Who Should Attend

This course on Quantitative Data Management, Analysis, and Visualization with Python is ideal for participants with elementary knowledge of statistics from fields such as agriculture, economics, food security, livelihoods, nutrition, education, medical or public health professionals, and others who wish to deepen their understanding of statistical modeling using Python.

Course Content

Module 1: 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
• Choosing the Right Method
• Exercise: Identify a Project of Choice and Develop a Research Design

Module 3: Survey Planning, Implementation, and Completion

• Types of Surveys
• The Survey Process
• Survey Design
• Methods of Survey Sampling
• Determining Sample Size
• Planning a Survey
• Conducting the Survey
• Post-Survey Activities
• Exercise: Plan a Survey Based on the Selected Research Design

Module 4: Introduction to Python

• Course Introduction
• Setup and Installation
• Overview of IDEs and Course Resources
• iPython/Jupyter Notebook Overview

Module 5: Learning NumPy

• Introduction to NumPy
• Creating Arrays
• Using Arrays and Scalars
• Indexing Arrays
• Array Transposition
• Universal Array Functions
• Array Processing
• Array Input and Output

Module 6: Introduction to Pandas

• DataFrames and Index Objects
• Reindexing and Dropping Entries
• Selecting Entries
• Data Alignment, Ranking, and Sorting
• Summary Statistics
• Handling Missing Data
• Index Hierarchy

Module 7: Working with Data

• Reading and Writing Text Files
• Working with JSON, HTML, and Microsoft Excel Files
• Merging and Combining DataFrames
• Reshaping, Pivoting, and Handling Duplicates
• Mapping, Replacing, Renaming, and Binning
• Handling Outliers and Permutation
• GroupBy Operations and Cross Tabulation

Module 8: Big Data and Spark with Python

• Introduction to Big Data and Spark
• Setting Up Local Spark
• AWS Account and EC2 Instance Setup
• Introduction to PySpark
• RDD Transformations and Actions

Module 9: Data Visualization

• Installing Seaborn
• Creating Histograms and Kernel Density Estimate Plots
• Combining Plot Styles
• Creating Box and Violin Plots
• Generating Regression Plots
• Creating Heatmaps and Clustered Matrices

Module 10: Data Analysis

• Linear Regression and Support Vector Machines
• Decision Trees and Random Forests
• Natural Language Processing
• Understanding Various Distributions (Uniform, Binomial, Poisson, Normal)
• Sampling Techniques
• T-Distribution, Hypothesis Testing, and Confidence Intervals
• Chi Square Test and Distribution

Module 11: Report Writing for Surveys, Data Dissemination, Demand, and Use

• Writing a Report from Survey Data
• Developing a 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

• The course is delivered by seasoned trainers with extensive professional experience in their respective fields.
• Training includes practical activities, theoretical knowledge, group work, and case studies.
• Participants receive comprehensive training manuals and additional reference materials.
• A certificate is awarded upon successful course completion.
• Custom-tailored courses to meet specific organizational needs are available. Contact us at training@skillsforafrica.org for more information.
• The training will be conducted at the Skills for Africa Training Institute in Nairobi, Kenya.
• The training fee covers tuition, materials, lunch, and the training venue. Accommodation and airport transfer can be arranged upon request.
• Payment should be made to our bank account before the training starts, and proof of payment sent to: training@skillsforafrica.org