By completing this course, learners will be able to analyze datasets using NumPy and Pandas, perform efficient numerical operations, reshape and clean data, handle missing values, and apply end-to-end data analysis workflows on real-world datasets. The course begins with the foundations of NumPy, focusing on array structures, memory optimization, and statistical operations. It then transitions into Pandas, guiding learners through creating DataFrames, performing joins, pivots, and unpivots, as well as exploring, sorting, and cleaning data. Finally, learners will advance to practical applications, mastering aggregation, filtering, and conditional operations before applying these skills to real-world projects like the Wine dataset.
NumPy & Pandas: Analyze & Transform Data
NumPy & Pandas: Analyze & Transform Data
This course is part of Data Analysis with NumPy and Pandas Specialization
Instructor: EDUCBA
Included with 
Gain insight into a topic and learn the fundamentals.
9 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Perform numerical operations and memory optimization with NumPy.
Create, join, pivot, and clean Pandas DataFrames effectively.
Apply aggregation, filtering, and workflows on real datasets.
Skills you'll gain
Details to know
Shareable certificate
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Recently updated!
October 2025
Assessments
13 assignments
Taught in English
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This course is part of the Data Analysis with NumPy and Pandas Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
This module introduces learners to the fundamentals of NumPy, including its advantages over Python lists, array structures, and efficient operations. Learners will explore slicing, reshaping, statistical calculations, and concatenation to build a solid foundation in numerical computing.
What's included
11 videos4 assignments1 plugin
This module guides learners through Pandas, covering how to create DataFrames, perform joins, reshape data, and explore datasets. Learners will also practice cleaning, renaming, and dropping variables, equipping them with skills for effective data preparation.
What's included
17 videos5 assignments
This module focuses on advanced Pandas features such as grouping, filtering, and handling missing values. Learners will also explore real-world data analysis workflows, including importing datasets, applying conditions, and working with practical case studies like the Wine dataset.
What's included
13 videos4 assignments
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