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.

Spécialisation Programming for Python Data Science: Principles to Practice

Spécialisation Programming for Python Data Science: Principles to Practice

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

Instructeurs : Andrew D. Hilton

Inclus avec Coursera Plus

Série de 5 cours

Approfondissez votre connaissance d’un sujet

4.0

(52 avis)

niveau Débutant

Expérience recommandée

4 mois à raison de 5 heures par semaine

Planning flexible

Obtenir une qualification professionnelle

Partagez votre expertise avec les employeurs

Série de 5 cours

Approfondissez votre connaissance d’un sujet

4.0

(52 avis)

niveau Débutant

Expérience recommandée

4 mois à raison de 5 heures par semaine

Planning flexible

Obtenir une qualification professionnelle

Partagez votre expertise avec les employeurs

Ce que vous apprendrez

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.

Vue d'ensemble

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.

Ce qui est inclus

Certificat partageable

Ajouter à votre profil LinkedIn

Enseigné en Anglais

30 exercices pratiques

Améliorez votre expertise en la matière

Acquérez des compétences recherchées auprès d’universités et d’experts du secteur
Maîtrisez un sujet ou un outil avec des projets pratiques
Développez une compréhension approfondie de concepts clés
Obtenez un certificat professionnel auprès de Duke University

Spécialisation - série de 5 cours

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

COURS 125 heures3.9 (59 évaluations)Voir le cours

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Data Science with NumPy, Sets, and Dictionaries

COURS 230 heures3.9 (19 évaluations)Voir le cours

Ce que vous apprendrez

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!

Compétences que vous acquerrez

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

Pandas for Data Science

COURS 341 heures4.2 (12 évaluations)Voir le cours

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Designing Larger Python Programs for Data Science

COURS 442 heuresVoir le cours

Ce que vous apprendrez

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.

Compétences que vous acquerrez

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

Data Visualization and Modeling in Python

COURS 532 heuresVoir le cours

Ce que vous apprendrez

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

Compétences que vous acquerrez

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

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeurs

Andrew D. Hilton

Duke University

19 Cours1 122 829 apprenants

Nick Eubank

Duke University

5 Cours24 404 apprenants

Kyle Bradbury

Duke University

5 Cours24 404 apprenants

Genevieve M. Lipp

Duke University

11 Cours282 190 apprenants

Offert par

Duke University

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Foire Aux 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.