Data Engineering with Python

Labor Day starts with $70+ in savings on Coursera Plus. Save 40% for 3 months.

Data Engineering with Python

This course is part of Python for Data Analytics and Engineering Specialization

Instructor: Barry Finder

Included with Learn more

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Configure a Python development environment and use pandas to work with structured datasets.
Clean, transform, and prepare data by filtering records, handling missing values, and reshaping datasets.
Analyze and summarize data using Python and pandas to identify patterns and generate meaningful insights.
Apply Python programming techniques to support foundational data engineering workflows.

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

Build your subject-matter expertise

This course is part of the Python for Data Analytics and Engineering 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 2 modules in this course

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.

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.

What's included

92 readings12 assignments

92 readingsTotal 218 minutes

Module overview: pandas fundamentals1 minute
Introduction4 minutes
Python packages1 minute
Key terms1 minute
Introduction1 minute
Python packages2 minutes
Conclusion1 minute
Installing Python packages1 minute
Key terms1 minute
Introduction1 minute
Verifying a package2 minutes
Packages2 minutes
Installing a package5 minutes
Importing a package5 minutes
Conclusion1 minute
Python package modules1 minute
Key terms1 minute
Introduction1 minute
Finding modules5 minutes
Accessing modules (method 1)2 minutes
Accessing modules (method 2)2 minutes
Conclusion1 minute
Introduction to pandas1 minute
Key terms1 minute
Introduction1 minute
pandas1 minute
Manipulating data with pandas2 minutes
Importing data with pandas2 minutes
Conclusion1 minute
pandas DataFrame1 minute
Key terms1 minute
Introduction1 minute
Importing CSV files2 minutes
Practice importing CSV files2 minutes
Importing Excel files2 minutes
Specifying a worksheet2 minutes
Importing SQL database files1 minute
Step 1: Import sqlalchemy1 minute
Step 2: Add credentials3 minutes
Step 3: Add the database type5 minutes
Step 4: Import the database2 minutes
Step 5: Disconnect from the database2 minutes
Importing JSON, HTML, and other file formats6 minutes
Conclusion1 minute
pandas DataFrame characteristics1 minute
Key terms1 minute
Introduction2 minutes
info()10 minutes
head() and tail()5 minutes
describe()3 minutes
shape2 minutes
size2 minutes
Conclusion1 minute
Additional resources1 minute
pandas Series1 minute
Introduction1 minute
Creating a Series manually5 minutes
Creating a Series from a list5 minutes
Creating a Series from a DataFrame5 minutes
Conclusion1 minute
pandas Series characteristics1 minute
Key terms1 minute
Introduction2 minutes
head()2 minutes
tail()2 minutes
describe()2 minutes
shape1 minute
size1 minute
hasnans5 minutes
Conclusion1 minute
pandas Series custom indexing1 minute
Key terms1 minute
Introduction2 minutes
Creating custom numeric indexes1 minute
Example: Create custom numeric indexes10 minutes
Creating custom string indexes10 minutes
Conclusion1 minute
pandas Series conversions1 minute
Introduction1 minute
Converting a pandas Series to a list5 minutes
Converting a Series to a string3 minutes
Conclusion1 minute
pandas Series unique values1 minute
Key terms1 minute
Introduction2 minutes
Checking for unique values11 minutes
Identifying non-unique values7 minutes
Returning unique values7 minutes
Conclusion1 minute
Summary and assessment: Introduction to pandas1 minute
Summary2 minutes
Instructions1 minute

12 assignmentsTotal 297 minutes

Knowledge check: Python packages9 minutes
Knowledge check: Installing Python packages12 minutes
Knowledge check: Python package modules12 minutes
Knowledge check: Introduction to pandas11 minutes
Knowledge check: pandas DataFrame8 minutes
Knowledge check: pandas DataFrame characteristics12 minutes
Knowledge check: pandas Series12 minutes
Knowledge check: pandas Series characteristics12 minutes
Knowledge check: pandas Series custom indexing14 minutes
Knowledge check: pandas Series conversions9 minutes
Knowledge check: pandas Series unique values10 minutes
Assessment: Introduction to pandas176 minutes

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.

What's included

76 readings7 assignments

76 readingsTotal 289 minutes

Overview: Data manipulation with pandas1 minute
Introduction9 minutes
pandas DataFrame reshaping1 minute
Key terms1 minute
Introduction2 minutes
Replacing data in a DataFrame10 minutes
Combining two DataFrames5 minutes
Removing a column from a DataFrame5 minutes
Transposing a DataFrame5 minutes
Example: Transpose a DataFrame2 minutes
Conclusion1 minute
pandas DataFrame sorting by values1 minute
Key terms1 minute
Introduction1 minute
Sorting a DataFrame by one column2 minutes
Sorting by one column in ascending order10 minutes
Sorting by one column in descending order5 minutes
Sorting a DataFrame by multiple columns5 minutes
Sorting a DataFrame by missing values5 minutes
Conclusion1 minute
pandas DataFrame sorting by index1 minute
Key terms1 minute
Introduction1 minute
Sorting a DataFrame by row indexes1 minute
Sorting by row indexes in ascending order15 minutes
Sorting by row indexes in descending order6 minutes
Sorting a DataFrame by column labels6 minutes
Conclusion1 minute
pandas numbers1 minute
Introduction1 minute
sum()9 minutes
count()5 minutes
mean()5 minutes
median()5 minutes
mode()5 minutes
min()5 minutes
max()5 minutes
std()5 minutes
Conclusion1 minute
pandas strings1 minute
Introduction1 minute
upper()10 minutes
lower()5 minutes
contains()5 minutes
find()5 minutes
findall()5 minutes
cat()5 minutes
split()5 minutes
Conclusion1 minute
pandas dates1 minute
Key terms1 minute
Introduction 1 minute
now() 13 minutes
year 10 minutes
month 2 minutes
month_name()2 minutes
day 2 minutes
week 2 minutes
day_name() 2 minutes
hour 2 minutes
minute 2 minutes
second 2 minutes
Conclusion 1 minute
pandas missing data1 minute
Introduction 2 minutes
Removing rows with missing values 15 minutes
Replacing missing values 2 minutes
Using two lines of code 3 minutes
Using one line of code 3 minutes
Replacing incorrect values 10 minutes
Using two lines of code 3 minutes
Using one line of code 2 minutes
Interpolating missing values 4 minutes
Linear interpolation 3 minutes
Previous value2 minutes
Conclusion 1 minute

7 assignmentsTotal 91 minutes

Knowledge check: pandas DataFrame reshaping8 minutes
Knowledge check: pandas DataFrame sorting by values9 minutes
Knowledge check: pandas DataFrame sorting by index8 minutes
Knowledge check: pandas numbers18 minutes
Knowledge check: pandas strings17 minutes
Knowledge check: pandas dates22 minutes
Knowledge check: pandas missing data9 minutes

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Barry Finder

Chegg Skills

33 Courses430 learners

Offered by

Chegg Skills

Explore more from Data Analysis

Chegg Skills
AI-Enabled Web Development Foundations
Course
Chegg Skills
Practical Python Programming
Course

Why people choose Coursera for their career

Felipe M.

Learner since 2018

"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."

Jennifer J.

Learner since 2020

"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."

Larry W.

Learner since 2021

"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."

Chaitanya A.

"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."

Break down barriers to learning with big savings
Get Coursera Plus
Start with easy savings for hard-working teams
Save 30% today

Frequently asked questions

To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.