Description

In this assignment we will work with a (fake) dataset encompassing people’s age, number of steps taken and income. The main purpose of this assignment relates to data wrangling / data cleaning – but we will also follow through on some basic analysis and predictions with this data.

Tasks / Learning Goals Hands-on experience with:
– Data Wrangling: Connecting together data from
– Data Visualization: Looking at data to check for problems
– Data Cleaning: Removing ‘bad’ data, dealing with outliers, dealing with missing data – Basic Analysis & Prediction: Looking at how different variables relate to each other

Submitting Assignments

You will submit a Jupyter notebook file (.ipynb) to TritonED. Make sure that the file you submit has the following filename (filled in with unique course ID number): ‘A2_$####.ipynb’

Grading Rubric
This assignment is worth 12% of your grade (12 points).

There are 8 parts to this assignment, with the following point values:

Part 1: Data Wrangling 2.5 points
Part 2: Data Cleaning 1.5 points
Part 3: Data Visualization 1 point
Part 4: Data Pre-Processing 1.5 points
Part 5: Outliers 1 point
Part 6: Basic Analysis 1.5 points
Part 7: Predictions 2 points
Part 8: Revisiting Outliers 1 point