Quantitative Data Management, Analysis And Visualization With Python Worshop

INTRODUCTION

This comprehensive course will be your guide to learning how to use the power of Python to analyze big data, create beautiful visualizations, and use powerful machine learning algorithms. This course is designed for both beginners with basic programming experience or experienced developers looking to make the jump to Data Science and big data Analysis.

COURSE OBJECTIVES

At the end of course participants should be able to understand:

• Research Design
• 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
• Research report writing

DURATION

10 Days

WHO SHOULD ATTEND

The course targets 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 using Phython

COURSE CONTENT

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 to Phython

·       Course Intro

·       Setup

·       Installation Setup and Overview

·       IDEs and Course Resources

·       iPython/Jupyter Notebook Overview

Module 5: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 6: Intro to Pandas

·       DataFrames

·       Index objects

·       Reindex

·       Drop Entry

·       Selecting Entries

·       Data Alignment

·       Rank and Sort

·       Summary Statistics

·       Missing Data

·       Index Hierarchy

Module 7: 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 8: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 9: Data Visualization

·       Installing Seaborn

·       Histograms

·       Kernel Density Estimate Plots

·       Combining Plot Styles

·       Box and Violin Plots

·       Regression Plots

·       Heatmaps and Clustered Matrices

Module 10: 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 11: 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

·       This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. The course is taught through a mix of practical activities, theory, group works and case studies.

·       Training manuals and additional reference materials are provided to the participants.

·       Upon successful completion of this course, participants will be issued with a certificate.

·       We can also do this as tailor-made course to meet organization-wide needs. Contact us to find out more: training@skillsforafrica.org

·        The training will be conducted at SKILLS FOR AFRICA TRAINING INSTITUTE IN NAIROBI KENYA.

·       The training fee covers tuition fees, training materials, lunch and training venue. Accommodation and airport transfer are arranged for our participants upon request.

·       Payment should be sent to our bank account before start of training and proof of payment sent to: training@skillsforafrica.org