Learning Outcomes
1. Identify business objective
2. Perform data cleansing
3. Perform data visualization and exploration
4. Coding with Python and its libraries
5. Identify and fix logical and run-time errors in programs
Learning goals at programming level
• Function, pandas, control statement, data structure, e.g., list, dictionary
Dataset
Automobile dataset:
• car: manufacturer brand
• price: seller’s price in advertisement (in USD)
• body: car body type
• mileage: as mentioned in advertisement (‘000 Km)
• engV: rounded engine volume (‘000 cubic cm)
• engType: type of fuel (“Other” in this case should be treated as NA)
• registration: whether car registered in Ukraine or not
• year: year of production
• model: specific model name
• drive: drive type
Background
This assignment focuses on data visualization and exploration. Students are expected to derive a business objective from a given dataset, and perform data exploration and visualization on the given dataset in an attempt to respond to the stated business objective.
Tasks
1. Identify a meaningful business objective from the given dataset (20 words max). The stated objective needs to be solvable by a predictive model that we can implement in the future.
2. Data cleansing: clean up unknown data in the given dataset.
3. Analyze the data statistically (not more than 10 lines of code) and present the data and results graphically (not more than 5 graphs). This task is highly dependent on task 1.
4. Write up a report. The conclusion part cannot be more than 50 words..
