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Data Engineering with Python

via Coursera

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Details

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

2 weeks, 7 hours a week
Sessions

Self-Paced
Level

Beginner
Subtitles

English

Found in

Part of

Python for Data Analytics and Engineering

Overview

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Data Engineering with Python teaches learners how to use Python and pandas to prepare, transform, and analyze structured data for modern data engineering workflows. Through hands-on coding exercises and practical projects, learners build foundational skills for working with datasets, cleaning data, performing transformations, and extracting meaningful insights. Throughout the course, learners configure a Python development environment, install and manage packages, and use the pandas library to load, clean, filter, transform, and analyze structured datasets. They practice handling missing values, aggregating information, reshaping data, and preparing datasets for downstream analysis through practical exercises that reflect common data engineering tasks. By the end of the course, learners will be able to prepare and transform structured datasets using Python and pandas, perform foundational data analysis, and apply programming techniques that support modern data engineering workflows.

Syllabus

Introduction to pandas
- Working with data efficiently requires tools designed to organize, explore, and analyze information. In this module, you'll be introduced to pandas, one of Python's most widely used libraries for data analysis. You'll learn how to install and import Python packages, create and explore Series and DataFrames, and import data from common file formats. These foundational skills will prepare you to work confidently with datasets as you continue building your data engineering knowledge. As you explore new datasets, take time to examine their structure before making changes. Understanding how your data is organized is an important first step toward choosing the right approach for analysis and transformation.
Data manipulation with pandas
- Real-world datasets are rarely ready for analysis without some preparation. In this module, you'll explore how to use pandas to reshape and organize data, perform calculations, manipulate text and date values, and address missing information. Through practical examples, you'll develop the skills needed to transform raw data into a more accurate, consistent, and analysis-ready format. Data preparation is often an iterative process. As you make changes to a dataset, review your results along the way to confirm that each transformation supports the quality and integrity of your data.