Exploratory Analysis

• LO1 – Students should be able to assess a novel dataset for errors and/or anomalies using Tableau and develop a plan to address the found issues
• LO2 – Students should be able to create an exploratory Tableau Workbook or Jupyter Notebook for a new dataset that will help with their understanding of the data
• LO5 - Students should be able to identify appropriate tools for ‘data wrangling’ and explain the purpose of the tools

When you first gain access to a new dataset, you will want often want to being by exploring the data. So far, we have introduced two tools that can be helpful with this: Jupyter and Tableau. There are benefits and drawbacks to each, and you may find yourself using both rather than picking one or the other.

🛠 Tools

• Tableau
• Jupyter/Python

📚 Resources

• Phillipe Bouaziz, July 13, 2020. Better, Faster, Stronger Python Exploratory Data Analysis. Toward Data Science
• Anooj, November 24, 2018. Tableau for Exploratory Data Analysis (EDA). Toward Data Science
• Analytics University, December 2, 2018. Exploratory Analysis (EDA) Using Python (Jupyter Notebook):

☑️ Tasks

• Review the exploratory analysis template notebook (link)
• Import your dataset into a the template
• Import your dataset into the tableau template and try to mimic the tasks from the Jupyter Notebook template

Optional Submissions

• Submit your data transformation Jupyter Notebook to GitHub Classroom: Moodle Submission- Data Wrangling GitHub Classroom

  Indicate whether this submission is for feedback or automated grading

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