Python Data Analysis for Beginners: NumPy, Pandas, and Regression — PickAClass
⏱ 2h 48m 📚 28 lessons 🎧 Audio version

Python Data Analysis for Beginners: NumPy, Pandas, and Regression

Master the essentials of data manipulation and visualization using NumPy, Pandas, and Matplotlib, and build a solid foundation in linear regression.

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About this course

In today's data-driven world, the ability to extract meaningful insights from raw information is one of the most valuable skills you can acquire. Python makes this transition accessible, offering powerful tools that turn complex datasets into clear, actionable stories. This text-based course guides you through the foundational concepts of data analysis, ensuring you understand the logic behind the code. You will progress from setting up a clean development environment to manipulating datasets and implementing your first predictive models. What you'll learn: Understand core data science terminology and the modern Python data analysis ecosystem; Manipulate and clean structured data efficiently using Pandas dataframes; Perform fast numerical computations on multi-dimensional arrays with NumPy; Create clear, informative data visualizations using Matplotlib; Apply the fundamentals of linear regression to make basic data-driven predictions; Configure modern Python virtual environments and practice clean coding standards. The course begins with essential terminology and environment setup, guiding you step-by-step through data manipulation, exploratory analysis, and visualization. You will conclude by learning how to fit and interpret a basic linear regression model using written explanations and practical code walkthroughs. Designed entirely for beginners, this course requires no prior experience in data science or advanced mathematics. Start your journey into data analysis today and unlock the power of your data.

Course contents

What you'll get

  • 📜 Certificate of completion
    Add it to your LinkedIn profile
  • 💬 Personal AI tutor
    Stuck on a lesson? Ask your built-in tutor anything, any time.
  • 🎧 Audio version included
    Learn on the go — no screen needed
  • ♾️ Lifetime access
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  • 📱 Phone or computer
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  • 💸 14-day refund
    No questions asked
  • ⚡ Short & focused
    2h 48m of practical content

Certificate of completion

Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.

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Certificate of Mastery
This certifies that
Name Surname
has successfully demonstrated mastery of
Python Data Analysis for Beginners: NumPy, Pandas, and Regression
Skills demonstrated
✓
Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
✓
A/B test design
Proficient
1.7 hrs
✓
Behavioral copywriting
Advanced
1.9 hrs
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PickAClass — Name Surname
Python Data Analysis for Beginners: NumPy, Pandas, and Regression
Page 2 of 2
Performance detail
Coursework summary
Lessons completed 14 / 14
Practice questions 26 / 28
Assignments submitted 4 (avg 4.5 / 5)
Capstone project Reviewed — 4.6 / 5
Total practice 6.2 hrs
Performance benchmark
Cohort rank Top 12% of 1,625
Time to completion 11 days (median: 22)
Mastery score 91 / 100
Practice-question score 94%
Skill verification Verified Skill Path
Verify this credential
pickaclass.com/certificates/PCC-2026-X4F7-AP19
Issued under the academic standards of PickAClass. Skill levels reflect assessed performance against the course's competency rubric. This is an original credential of this platform.

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Frequently asked

What do I need to take this course? +

Just a phone or computer with internet. No installs, no special hardware.

How do I pay? +

By card via Stripe. We don’t store card details — Stripe handles them securely.

Can I get a refund? +

Yes — full refund within 14 days, no questions asked.

How long will I have access? +

Forever. Once you purchase, the course is yours to revisit anytime.

Will I get a certificate? +

Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.

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