Programming Languages
In order to help students become more proficient programmers in a variety of languages, this course presents fundamental programming concepts with a focus on functional programming and design principles.
Description for Programming Languages
Overview of Programming Languages: Acquire foundational programming principles via the languages ML, Racket, and Ruby, while understanding language architecture and the ability to transition between other programming languages.
Focus on Functional Programming: Highlight functional programming methodologies that are crucial for developing reusable, resilient, and sophisticated software, applicable in contemporary programming languages.
Creating Accurate and Appealing Programs: Establish a foundation for comprehending and utilizing language constructs proficiently, emphasizing program design concepts that yield clean, efficient code.
Deep Thinking Beyond Syntax: Develop the capacity for critical analysis of programming languages, emphasizing principles over syntax to improve proficiency in any language.
Level: Intermediate
Certification Degree: Yes
Languages the Course is Available: 22
Offered by: On Coursera provided by University of Washington
Duration: 29 hours (approximately)
Schedule: Flexible
Pricing for Programming Languages
Use Cases for Programming Languages
FAQs for Programming Languages
Reviews for Programming Languages
0 / 5
from 0 reviews
Ease of Use
Ease of Customization
Intuitive Interface
Value for Money
Support Team Responsiveness
Alternative Tools for Programming Languages
Featured Tools
Learn practical insights on how to integrate AI into your business and comprehend its societal impact by navigating a complicated landscape of AI adoption.
A data analysis course covering practical skills, data visualization in Excel and BI tools, Python for data analysis, and portfolio development through hands-on projects.
With the help of machine learning, this course teaches students how to predict health insurance costs by taking into account factors like age, gender, BMI, and smoking habits.
Construct and train neural networks and tree ensemble methods using TensorFlow, while applying effective machine learning practices for real-world data generalization.
Construct the gradient descent algorithm, execute univariate linear regression with NumPy and Python, and create data visualizations with matplotlib.