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Cleaning Bank Marketing Campaign Data

4.1+
13 reviews
Intermediate

Tidy a bank marketing campaign dataset by splitting it into subsets, updating values, converting data types, and storing it as multiple csv files.

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1 Tasks1,500 XP

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Project Description

Data cleaning is an essential skill for data engineers, encompassing reading, modifying, splitting, and storing data.

In this notebook, you will apply your data-cleaning skills to process information about marketing campaigns run by a bank.

You will need to modify values, add new features, convert data types, and save data into multiple files.

Project Tasks

  1. 1
    Use your data-cleaning skills to modify and process bank marketing campaign data!

Technologies

Python Python

Topics

Programming
George Boorman HeadshotGeorge Boorman

Curriculum Manager, DataCamp

George is a Curriculum Manager at DataCamp. He holds a PGDip in Exercise for Health and BSc (Hons) in Sports Science and has experience in project management across public health, applied research, and not-for-profit sectors. George is passionate about sports, tech for good, and all things data science.
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*4.1
from 13 reviews
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  • Ankush B.
    about 1 month

    Good project to enhance your skills in treating data before doing any analysis.

  • Alysson C.
    about 2 months

    It's a good project.

  • Bob J.
    6 months

    This is a very good challenging exercise for data cleaning

  • Mehdi Z.
    7 months

    I was stuck with this project just because I kept receiving a wrong feedback on my code

  • Jordan D.
    10 months

    The instructions needs to stick to one level of detail. I love being able to solve the task how I see fit, but the instructions seem to mislead in certain ways. Based on the data engineering career path, I strongly believe that if we want to focus on writing efficient python, that should have been added into the mix. If we wanted to be more hands free, the instructions should be more along the lines of "the data isn't perfect, please do EDA to determine what needs to be cleaned to match the desired output." Instead, the instructions currently tell you certain things to do but leave out others which makes me think it isn't a problem until you run the script (i.e month field is a string "may, jun, etc."). The last thing is part of the code requires the code written to be exactly as expected which drove me crazy (creation table part). I didn't like the fact that I had to call out the foreign key in a different way than I thought it should be but it may have been the type of sql. But the sql was different from the other practices. Please stick to one.

"Good project to enhance your skills in treating data before doing any analysis."

Ankush B.

"It's a good project."

Alysson C.

"This is a very good challenging exercise for data cleaning"

Bob J.

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