15 Mistakes to Avoid in Data Science

15 Mistakes to Avoid in Data Science

This course is part of Starting a Data Science Career Specialization

Instructor: Madecraft

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

How to avoid the 15 most common data science mistakes that cost teams time, money, and credibility.
How to communicate findings, work honestly with data, and ship results stakeholders trust and act on.
How to build foundational habits across the data science lifecycle, from cleaning data to telling its story.

Details to know

Shareable certificate

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Build your subject-matter expertise

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 4 modules in this course

By the end of this course, you'll recognize the common mistakes that derail data science work — from skipping the fundamentals to overpromising solutions to stakeholders — and you'll have practical habits in place to avoid them. You'll gain confidence in how you handle data, how you work with collaborators, and how you communicate findings to the people who'll act on them.

What sets this course apart is the source material. Every lesson is built around insights from working data scientists who learned these lessons the hard way and want to spare you the same trial and error. You won't get abstract theory. You'll hear directly from people who've made the mistakes and figured out how to do better. Whether you're new to data science, transitioning into the field, or you've been working in it for years, this course meets you where you are. By the end, you'll have a toolkit of practical habits across the full project lifecycle, from data prep to delivery, that will help you produce better work and build trust with the people who depend on it.

Your analysis is only as valuable as your stakeholders' ability to act on it. In this module, you'll strengthen the communication habits that help non-technical stakeholders follow your reasoning, trust your findings, and act on your recommendations.

What's included

6 videos1 reading1 assignment

6 videosTotal 7 minutes

Avoid Common Mistakes to Excel in Data Science1 minute
Communicating With Overly Technical Language1 minute
Overpromising Solutions to Stakeholders1 minute
Assuming Knowledge Level of Stakeholders1 minute
Not Telling a Story with the Data 2 minutes
Not Confirming with Stakeholders2 minutes

1 readingTotal 10 minutes

Calibrating Your Communication to Your Audience's Statistical Literacy10 minutes

1 assignmentTotal 15 minutes

Speaking the Language Your Stakeholders Need15 minutes

The quality of your conclusions is determined long before you fit your first model. In this module, you'll apply the foundational habits that keep your data reliable, your analysis representative, and your conclusions grounded in evidence rather than assumption.

What's included

5 videos2 readings2 assignments

5 videosTotal 5 minutes

Skipping the Fundamentals1 minute
Moving Too Quickly1 minute
Having a Data Set That is Too Small1 minute
Not Considering the Level of Variation1 minute
Including Your Bias1 minute

2 readingsTotal 20 minutes

Protecting Time for Quality Control in Your Analysis Workflow10 minutes
Why the Spread of Your Data Matters as Much as the Average10 minutes

2 assignmentsTotal 35 minutes

Getting Your Data Right Before You Build15 minutes
Your First Thirty Minutes With a New Data Set20 minutes

Technical skill gets you into a data science role, but the habits you build around process, tools, and professional growth determine how effective and sustainable that role becomes. In this module, you'll develop the workflow discipline and professional mindset that make your contributions more reliable, more timely, and better positioned to grow with the field.

What's included

5 videos2 assignments

Good data science practice is a discipline you refine with every project, team, and stakeholder interaction. In this module, you'll identify how to sustain strong data science habits and position yourself to keep improving your practice as the field evolves.

What's included

1 video1 assignment

Earn a career certificate

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Instructor

Madecraft

92 Courses6,946 learners

Offered by

Madecraft

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