Python Data Science Mistakes to Avoid

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Python Data Science Mistakes to Avoid

This course is part of Starting a Data Science Career Specialization

Instructor: Madecraft

Included with Learn more

5 modules

Gain insight into a topic and learn the fundamentals.

4 hours to complete

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

How to write clean, well-named, well-documented Python that you and your teammates can run, debug, and build on.
How to spot and fix data mistakes, messy files, outliers, wrong structures, that quietly wreck your analysis.
How to pick reliable model features and avoid ML traps like redundancy and features missing at test time.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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This course is part of the Starting a Data Science Career Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

Most Python courses teach you how to write code that works once. This one focuses on something just as valuable but rarely taught directly: how to avoid the small, common mistakes that quietly cost data scientists hours of debugging and undermine their results.

In this course you'll discover how to write cleaner, more reliable Python code, and structure it so it runs the way you intend. The lessons move through four areas, coding practices, structuring code, handling data, and machine learning, using short, practical examples you can apply immediately. You'll learn to comment and name code so collaborators can actually use it, organize and share projects cleanly, catch errors with simple tests, choose the right data structures and visualizations, clean data and address outliers, and select model features that hold up on unseen data. Whether you're new to Python or already experienced, you'll walk away with a concrete toolkit of habits that make your work faster, clearer, and more trustworthy.

The small habits you build while coding decide whether future-you (and your teammates) can actually use your work. In this module, you'll apply practical conventions, like clear comments, organized directories, relative paths, tests, and precise names, so your code stays readable, runnable, and easy to share.

What's included

10 videos2 readings2 assignments

10 videosTotal 20 minutes

Avoiding Common Python Mistakes1 minute
Getting the Most From This Course1 minute
Not Writing Comments 3 minutes
Not Organizing Your Directory3 minutes
Not Testing3 minutes
Not Sharing Data Referenced in Code1 minute
Hardcoding Inaccessible Paths3 minutes
Name Clashing with Python Standard Library2 minutes
Not Importing Relevant Libraries and Modules1 minute
Naming Vaguely2 minutes

2 readingsTotal 20 minutes

Comments That Earn Their Keep10 minutes
When Your Variable Shadows Python10 minutes

2 assignmentsTotal 35 minutes

Tame the Messy Project Folder20 minutes
Code You Will Thank Yourself For15 minutes

How you structure code determines whether it runs at all, and whether you can maintain it later. In this module, you'll restructure common trouble spots, from looping over lists to ordering functions and untangling dependencies, so your programs run cleanly and stay easy to work with.

What's included

5 videos2 assignments

Your model is only as good as the data you feed it, and rushing past your data is where many projects quietly go wrong. In this module, you'll inspect, visualize, clean, and correctly update your data so the decisions and models that follow rest on an accurate foundation.

What's included

6 videos1 reading2 assignments

6 videosTotal 10 minutes

Not Choosing the Right Data Structure2 minutes
Skimming Data2 minutes
Not Using the Right Visualization Type1 minute
Not Addressing Outliers1 minute
Not Updating Your Dataset2 minutes
Not Cleaning Data1 minute

1 readingTotal 10 minutes

Choosing the Right Container10 minutes

2 assignmentsTotal 35 minutes

Pick the Plot That Fits20 minutes
Look Before You Model15 minutes

Your model's ability to generalize depends on the feature choices you make well before you tune any algorithm. In this module, you'll select features that will still be available on unseen data and prune redundant ones so your models generalize well and stay efficient.

What's included

2 videos1 reading1 assignment

Your data science practice grows stronger every time you spot and fix a mistake before it reaches your output. In this module, you'll identify trusted external resources, from official Python documentation to professional data science platforms, to sustain the habits that keep your Python data science work clean, reliable, and efficient.

What's included

1 video1 assignment

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Instructor

Madecraft

147 Courses18,964 learners

Offered by

Madecraft

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