Data-Analysis

Steps for a new data analysis project:

1. Check the data health; clean, transform and prepare the data for analysis (it corresponds to 60% to 80% of the time in a data analysis project):

   • Missing values;
   • Duplicate records;
   • Redundant data: like, for example, total amount columns. Remove data that you do not need;
   • Data types;
   • Consistency of formats: whole numbers X decimal numbers, date formats, etc;
   • Consistency of representations: differences in capitalization, spacing and genders of adjectives;
   • Spelling errors.

2. Understand the data - EDA (Exploratory Data Analysis);

• Pandas Profiling library
• Outlier Analysis

3. Define the audience;

4. From the exploratory analysis, prepare an explanatory material.

   1. Data visualization - Choosing a chart:

      • Material Design.
      • Types of Charts

   2. Choosing colors for data visualization:

      • Beautiful Colors
      • Color Calculator
      • Color Wheel
      • CARTOColors
      • Color Guide
      • Chroma.js Color Palette Helper

   3. Practice makes perfection:

      • Data Viz Today
      • Data Viz Projects
      • Data stories
      • Makeover Monday
      • Tableau Iron Viz

   4. Data Science Competitions:

      • Driven Data
      • Kaggle

   5. Useful:

      • Dashboard Confusion