Matplotlib Essentials: Exploratory Data Analysis (EDA)

#DataModelingWorkshops #AIAndMLPreparation #KaggleModelthon #DataExploration #DataVisualizationWorkshop #MatplotlibPython #CorrelationAnalysis #OutlierDetection #DirtyDataCleaning #StatisticalCharts #HeatmapAnalysis #ScatterPlotInsights #DataQualityAssessment #DataInsights #DataPreprocessing #DataScienceBeginnersGuide #AIForDataAnalysis #VisualizingDataPatterns #MLModelPreparation #PythonForDataViz

Workshop Series Introduction

This workshop is one of a series of workshops that aim to enhance understanding of data modeling processes for those interested in artificial intelligence and machine learning. Specifically, it is designed to prepare participants for the first edition of the upcoming IEEEModelthon 1.0 on Kaggle.

Workshop Overview

Now it’s time to discover and visualize the data to gain insights. In this workshop we will go into a little more depth and understand the data.
In this phase, we will create statistical visuals to get an idea about the distribution of data values and how they are correlated to each other using correlation measures by building scatter plots and heat maps. We will also detect outliers (if they exist) and how to deal with them.

In addition, we will evaluate the quality of the data and identify any dirty data to deal with later in the next step of building a machine learning model -data preprocessing and cleansing.

In this workshop, we will start working with the Python Matplotlib library to build visualizations and extract insight from them. We will also learn about the different types of visualizations and how to choose the appropriate visualization based on each data type.

Purpose

Primarily, it will cover how to create data charts using Python Matplotlib library and how to extract the insight about that chart! This workshop is ideal for beginners to intermediate learners eager to understand the initial steps of the data science process.

Objective and Outcome

This workshop is essential for anyone in a data-driven field to gain deeper understanding of the story behind the data. It introduces methods for representing data as charts of various types and understanding the relationships between columns. The main outcome of this phase is to understand the data well to decide how to deal with it in the next phase -data preprocessing.


For more information about the IEEEModelthon1.0, click here.

Go to IEEEModelthon1.0