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NumPy & Pandas: Analyze & Transform Data

This course is part of Data Analysis with NumPy and Pandas Specialization

Instructor: EDUCBA

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

1 week to complete

at 10 hours a week

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

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

13 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

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.

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 3 modules in this course

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.

What makes this course unique is its step-by-step progression from core numerical computing concepts to applied data analysis projects, ensuring that learners not only understand the theory but also gain hands-on practice. Whether you are a beginner aiming to strengthen your foundations or a professional seeking to improve your data analysis efficiency, this course will equip you with the essential skills to transform raw data into actionable insights using NumPy and Pandas.

Module details

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 assignments

11 videosTotal 88 minutes

Introduction to Numpy8 minutes
Importing Numpy Package and Basic Commands9 minutes
Comparision Between List9 minutes
Numpy on Basis of Memory and Time5 minutes
Why we are using Numpy and why not List12 minutes
Numpy Operations and Subsetting11 minutes
2D Numpy Arrays7 minutes
Subsetting Operations8 minutes
Descriptive Statistics in Numpy Arrays7 minutes
Array Updating4 minutes
Concatenate Functions7 minutes

4 assignmentsTotal 60 minutes

NumPy Fundamentals10 minutes
Why NumPy is Powerful10 minutes
Statistical and Structural Operations10 minutes
Graded-Mastering NumPy for Data Foundations30 minutes

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

17 videosTotal 136 minutes

Introduction to Pandas9 minutes
Creating Dataframe from Series and Dictionary9 minutes
Making Dataframe from Dictionary6 minutes
Concatenate Dataframe8 minutes
Joins and Pivot8 minutes
Unipivot Dataframe8 minutes
Dataframe Operations9 minutes
Slicing8 minutes
Dicing7 minutes
Sorting Dataframes6 minutes
Summary Statistics7 minutes
Dealing with Duplicate Values7 minutes
Importing Dataset12 minutes
Head Tail and Unique Function8 minutes
Accessing Column7 minutes
Rename Variables7 minutes
Dropping Variables8 minutes

5 assignmentsTotal 70 minutes

Creating and Combining DataFrames10 minutes
Joins, Pivots, and Transformations10 minutes
Exploring and Sorting Data10 minutes
Cleaning and Structuring Data10 minutes
Graded-Working with Pandas for Data Wrangling30 minutes

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.