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Quantitative Data Management, Analysis and Visualization with Python Course

By: Foscore Development Center


19 - 23 Sep, 2022  5 days

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USD 1,000

Venue: Foscore Development Training Center, Nairobi

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 Objective

  • 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

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 modelling using Python

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 processes
  • 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 processes
  • 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
  • python/Jupiter 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 Data Frames
  • Reshaping, Pivoting and Duplicates in Data Frames
  • Mapping, Replace, Rename Index, Binning, Outliers and Permutation
  • Group by on Data Frames
  • Group by on Duct 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


  • All our courses can be Tailor-made to participants needs
  • Course duration is flexible, and the contents can be modified to fit any number of days.
  • Presentations are well guided, practical exercise, web-based tutorials and group work. Our facilitators are expert with more than 10years of experience.
  • One-year free Consultation and Coaching provided after the course.
Foscore Development Training Center, Nairobi Sep 19 - 23 Sep, 2022

Registration: 09:00:am - 04:30:am

USD 1,000.00
(Convert Currency)

Jackson Munene +254 712260031

A discount of 20% for 5 or more participants
Foscore Development Center
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