Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Is financial aid available?

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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Programming for Python Data Science: Principles to Practice Specialization

Programming for Python Data Science: Principles to Practice Specialization

Harness the Potential of Python for Data Science. Optimize, analyze, and visualize data effectively

Instructors: Andrew D. Hilton

Included with Coursera Plus

Learn more

5 course series

Get in-depth knowledge of a subject

4.0

(52 reviews)

Beginner level

Recommended experience

4 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

5 course series

Get in-depth knowledge of a subject

4.0

(52 reviews)

Beginner level

Recommended experience

4 months at 5 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

Leverage a Seven Step framework to create algorithms and programs.
Use NumPy and Pandas to manipulate, filter, and analyze data with arrays and matrices.
Utilize best practices for cleaning, manipulating, and optimizing data using Python.
Create classification models and publication quality visualizations with your datasets.

Overview

Accelerate your journey as a data scientist with this data science specialization in Python. Designed for data science beginners, this course series helps you develop the skills necessary to effectively manage, analyze, and communicate insights about data with Python. Whether you're a professional looking to add Python to your data science toolkit or a complete novice, this series offers hands-on practice and frameworks to navigate a full data science pipeline.

Across five courses, you’ll develop competency with foundational computer science concepts: algorithm development, data structures, and using the industry-standard text editor for Python, VS Code. You’ll get in-depth experience and create your programs with essential Python libraries for data science — NumPy, Pandas, and Matplotlib. These learning experiences focus on guided, stepwise development of these programs, with live-coding experiences designed to share insights from four experienced data scientists as they navigate these same problems.

In the final two courses, you'll focus on modeling, prediction, and visualization, laying the groundwork for exploring advanced topics like machine learning and inferential statistics. By the end of the series, you'll confidently clean and analyze data, uncover compelling insights, and create programs and visualizations for your data science portfolio. Earning your certificate will demonstrate your ability to generate impactful insights from raw data in a data-driven world.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

30 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from Duke University

Specialization - 5 course series

Throughout this specialization, you’ll create programs to analyze real-world data and produce insights to the most important issues facing society (e.g., infant mortality, economic indices, and carbon emissions). You’ll learn a process to translate abstract problems into functional programs that will create reproducible analyses. Each course emphasizes discrete parts of a data scientist’s toolkit. All courses focus on practical applications, whether you’re debugging basic Python code in industry-standard libraries or optimizing and evaluating predictive models. By completing the programming exercises in this specialization, you’ll develop the analytical and technical skills necessary for completing a full data science pipeline– starting with a messy dataset and resulting in a publication-quality visualization.

Python Programming Fundamentals

Course 125 hours3.9 (59 ratings)View course

What you'll learn

Create algorithms and programs using a logical Seven Step framework.
Create useful test cases and efficiently debug Python code.
Apply Python basics (conditionals, loops, mathematical operators, data types) to build a Python program from scratch to solve a data science problem.

Skills you'll gain

Python Programming, Algorithms, Data Analysis, Debugging, Microsoft Visual Studio, Problem Solving, Data Processing, and Software Development

Data Science with NumPy, Sets, and Dictionaries

Course 230 hours3.9 (19 ratings)View course

What you'll learn

Become proficient in NumPy, a fundamental Python package crucial for careers in data science. This comprehensive course is tailored to novice programmers aspiring to become data scientists, software developers, data analysts, machine learning engineers, data engineers, or database administrators.

Starting with foundational computer science concepts, such as object-oriented programming and data organization using sets and dictionaries, you'll progress to more intricate data structures like arrays, vectors, and matrices. Hands-on practice with NumPy will equip you with essential skills to tackle big data challenges and solve data problems effectively. You'll write Python programs to manipulate and filter data, as well as create useful insights out of large datasets. By the end of the course, you'll be adept at summarizing datasets, such as calculating averages, minimums, and maximums. Additionally, you'll gain advanced skills in optimizing data analysis with vectorization and randomizing data. Throughout your learning journey, you'll use many kinds of data structures and analytic techniques for a variety of data science challenges , including mathematical operations, text file analysis, and image processing. Stepwise, guided assignments each week will reinforce your skills, enabling you to solve problems and draw data-driven conclusions independently. Prepare yourself for a rewarding career in data science by mastering NumPy and honing your programming prowess. Start this transformative learning experience today!

Skills you'll gain

Object Oriented Programming (OOP), NumPy, Data Structures, Data Manipulation, Python Programming, Data Analysis, Image Analysis, Performance Tuning, Big Data, Data Science, and Data Wrangling

Pandas for Data Science

Course 341 hours4.2 (12 ratings)View course

What you'll learn

How and when to leverage the Pandas library for your data science projects
Best practices for cleaning, manipulating, and optimizing data with Pandas

Skills you'll gain

Debugging, Pandas (Python Package), Python Programming, Data Manipulation, Data Integration, Data Cleansing, Data Import/Export, NumPy, Query Languages, and Data Analysis

Designing Larger Python Programs for Data Science

Course 442 hoursView course

What you'll learn

How to plan program decomposition using top down design.
How to integrate discrete pieces of Python code into a larger, more functional, and complex program.

Skills you'll gain

Simulations, Python Programming, Object Oriented Programming (OOP), Debugging, Test Case, Program Development, Computer Programming, Development Testing, Pandas (Python Package), Systems Integration, Data Structures, Computational Thinking, Software Development, Data Manipulation, Data Science, Software Design, Software Engineering, Integration Testing, and Unit Testing

Data Visualization and Modeling in Python

Course 532 hoursView course

What you'll learn

Create professional visualizations for many kinds of data Utilize Classification algorithms to make predictions using a dataset

Skills you'll gain

Predictive Modeling, Machine Learning Algorithms, Regression Analysis, Data Visualization Software, Statistical Inference, Python Programming, Matplotlib, Statistics, Data Visualization, Pandas (Python Package), Data Analysis, Data Science, Predictive Analytics, Scientific Visualization, Probability & Statistics, Data Cleansing, and Data Manipulation

Earn a career certificate

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

Instructors

Andrew D. Hilton

Duke University

19 Courses1,122,266 learners

Nick Eubank

Duke University

5 Courses24,365 learners

Kyle Bradbury

Duke University

5 Courses24,365 learners

Genevieve M. Lipp

Duke University

11 Courses282,009 learners

Offered by

Duke University

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Frequently asked questions

This specialization contains a lot of hands on practice with authentic coding experiences. It's been designed for around 5 hours of work per module, and with that pacing it should take around 4 months. Some modules could be accomplished much quicker than that, and some might take you more time if you're newer to programming.

We advise to take these courses in the order they're arranged in the specialization, as each course builds upon the content from the previous course! This would be:

Python Programming Fundamentals
Data Science with NumPy, Sets, and Dictionaries
Pandas for Data Science
Designing Larger Python Programs for Data Science
Data Visualization and Modeling in Python

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Programming for Python Data Science: Principles to Practice Specialization

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included

Advance your subject-matter expertise