Online Course
Data Cleaning and Preprocessing with pandas

Master Python’s quintessential pandas library and its core data structures – Series and DataFrame objects. Elevate your data analysis skills for real-world challenges

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  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Skill level:

Intermediate

Duration:

3 hours
  • Lessons (2 hours)
  • Practice exams (30 minutes)

CPE credits:

5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited:

certificate

What You Learn

  • Add the pandas library to your data analysis toolkit.
  • Learn how to install and import Python packages.
  • Gain proficiency with pandas Series and DataFrame objects.
  • Explore methods to clean and preprocess data using pandas.
  • Solve real-world data preprocessing problems using pandas.

Topics & tools

pythondata analysisprogrammingdata preprocessingpandas

Your instructor

Course OVERVIEW

Description

CPE Credits: 5 Field of Study: Information Technology
Delivery Method: QAS Self Study
pandas is one of today’s most celebrated data analysis libraries. A favorite of many, its versatile functionalities can be leveraged for manipulation of many types of data - numeric, text, Boolean, and more. That’s one of the features that make pandas the go-to choice for analysts, especially during the data cleaning and preprocessing stages. pandas is built on NumPy and takes advantage of its computational power and abilities. But what sets pandas apart is its ability to operate with data in an easy-to-use way, allowing you to focus almost entirely on your analytic task. In this course, you will learn how to work with this powerful Python library and its core data structures – the pandas Series and DataFrames.

Prerequisites

  • Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
  • Completion of an introductory Python course is required.
  • Familiarity with NumPy is helpful but not mandatory.

Curriculum

29 lessons 32 exercises 4 exams

Free preview

Introduction to the pandas Library

1.1 Introduction to the pandas Library

6 min

Installing and Running pandas

1.3 Installing and Running pandas

6 min

Introduction to pandas Series

1.4 Introduction to pandas Series

9 min

Working with Attributes in Python

1.7 Working with Attributes in Python

5 min

Using an Index in pandas

1.10 Using an Index in pandas

4 min

Label-based vs Position-based Indexing

1.13 Label-based vs Position-based Indexing

5 min

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ACCREDITED certificates

Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global institutions.

Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities employers value most.

  • Institute of Analytics
  • The Association of Data Scientists
  • E-Learning Quality Network
  • European Agency for Higher Education and Accreditation
  • Global Association of Online Trainers and Examiners

Certificates are included with the Self-Study learning plan.

A LinkedIn profile mockup on a mobile screen showing Parker Maxwell, a Certified Data Analyst, with credentials from 365 Data Science listed under Licenses & Certification. A 365 Data Science Certificate of Achievement awarded to Parker Maxwell for completing the Data Analyst career track, featuring accreditation badges and a gold “Verified Certificate” seal.

How it WORKS

  • Lessons
  • Exercises
  • Projects
  • Practice Exams
  • AI Mock Interviews

Lessons

Learn through short, simple lessons—no prior experience in AI or data science needed.

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Exercises

Reinforce your learning with mini recaps, hands-on coding, flashcards, fill-in-the-blank activities, and other engaging exercises.

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Projects

Tackle real-world AI and data science projects—just like those faced by industry professionals every day.

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Practice Exams

Track your progress and solidify your knowledge with regular practice exams.

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AI Mock Interviews

Prep for interviews with real-world tasks, popular questions, and real-time feedback.

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Student REVIEWS

A collage of student testimonials from 365 Data Science learners, featuring profile photos, names, job titles, and quotes or video play icons, showcasing diverse backgrounds and successful career transitions into AI and data science roles.