NumPy & Pandas: Analyze & Transform Data
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NumPy & Pandas: Analyze & Transform Data
This course is part of Data Analysis with NumPy and Pandas Specialization
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13 reviews
13 reviews
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
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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.
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 videos•Total 88 minutes
- Introduction to Numpy•8 minutes
- Importing Numpy Package and Basic Commands•9 minutes
- Comparision Between List•9 minutes
- Numpy on Basis of Memory and Time•5 minutes
- Why we are using Numpy and why not List•12 minutes
- Numpy Operations and Subsetting•11 minutes
- 2D Numpy Arrays•7 minutes
- Subsetting Operations•8 minutes
- Descriptive Statistics in Numpy Arrays•7 minutes
- Array Updating•4 minutes
- Concatenate Functions•7 minutes
4 assignments•Total 60 minutes
- Graded-Mastering NumPy for Data Foundations•30 minutes
- NumPy Fundamentals•10 minutes
- Why NumPy is Powerful•10 minutes
- Statistical and Structural Operations•10 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 videos•Total 136 minutes
- Introduction to Pandas•9 minutes
- Creating Dataframe from Series and Dictionary•9 minutes
- Making Dataframe from Dictionary•6 minutes
- Concatenate Dataframe•8 minutes
- Joins and Pivot•8 minutes
- Unipivot Dataframe•8 minutes
- Dataframe Operations•9 minutes
- Slicing•8 minutes
- Dicing•7 minutes
- Sorting Dataframes•6 minutes
- Summary Statistics•7 minutes
- Dealing with Duplicate Values•7 minutes
- Importing Dataset•12 minutes
- Head Tail and Unique Function•8 minutes
- Accessing Column•7 minutes
- Rename Variables•7 minutes
- Dropping Variables•8 minutes
5 assignments•Total 70 minutes
- Graded-Working with Pandas for Data Wrangling•30 minutes
- Creating and Combining DataFrames•10 minutes
- Joins, Pivots, and Transformations•10 minutes
- Exploring and Sorting Data•10 minutes
- Cleaning and Structuring Data•10 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.
What's included
13 videos4 assignments
13 videos•Total 125 minutes
- Descriptive Statisitcs•9 minutes
- Group by Functions•10 minutes
- Filtering Functions•11 minutes
- Introduction to Jupyter Notebook•9 minutes
- Missing Values Introduction•12 minutes
- Imputation•5 minutes
- Working with Different Conditions•12 minutes
- Introduction to Data Analysis with Pandas and Python•10 minutes
- Installation of Softwares•6 minutes
- More on Installation•11 minutes
- Downloading and Loading Data•11 minutes
- Wine Data Set•10 minutes
- Slicing and Dicing•10 minutes
4 assignments•Total 60 minutes
- Graded-Advanced Pandas and Applied Data Analysis•30 minutes
- Aggregation and Filtering•10 minutes
- Handling Missing and Conditional Data•10 minutes
- Practical Data Analysis Projects•10 minutes
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Reviewed on Jun 16, 2026
Comprehensive coverage of NumPy arrays and Pandas operations.
Reviewed on May 13, 2026
Hands-on exercises improved confidence analyzing real-world datasets efficiently.
Reviewed on May 10, 2026
Hands-on projects improved my data analysis confidence significantly.
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