/Exploratory-Data-Analysis

Primary LanguageJupyter Notebook

Exploratory Data Analysis

Goal: Learn how to explore data and gather insights with Pandas, Matplotlib, Seaborn and Plotly

Topics covered

  1. Data Cleansing
  • Handling missing values, outliers, transforming data types, removing corrupt data and removing duplicates.
  1. Descriptive Analytics
  • Mean, Median, Mode, Standard Deviation, Quartiles, Inter-quartile range, Variance, Correlation, bin distributions, Covariance, Univariate, bivariate and multivaiate analysis.
  1. Entity Summary
  • Identify entity, group data and derive summary
  1. Data Visualization
  • Histogram, bar plots, line plots, stacked charts, area charts, column charts, combo charts, donut, pie charts, funnel charts, map charts, catter plots, bubble plots, 3-D plots, animated charts
  1. Gathering business insights
  • Deriving KPI's for chosen entity like best performing, worst performing, summary of entity over time, creating business power points.