We may earn an affiliate commission when you visit our partners.
James Robinson,
Marc Scott,
Martin O'Hanlon,
Ross Exton,
Michael Conterio,
Sam Isaacs,
Nina Szymor,
Mac Bowley,
and
Alex Parry
Topics Covered
- Use functions with parameters and return values
- Design and apply algorithms to data
- Breaking down problems into smaller parts
- Searching and sorting
- Efficiency of algorithms
- Understanding of list structures and their uses
Register for this course and see more details by visiting:
OpenCourser.com/course/unl7es/programming
Save this course
Save Programming 102: Think Like a Computer Scientist to your list so you can find it easily later:
Save
Save
Activities
Coming soon
We're preparing activities for
Programming 102: Think Like a Computer Scientist.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Programming 102: Think Like a Computer Scientist will develop knowledge and skills
that may be useful to these careers:
For more career information including salaries, visit:
OpenCourser.com/course/unl7es/programming
Reading list
We haven't picked any books for this reading list yet.
Save
Deep dive into Python's advanced features, covering metaprogramming, decorators, generators, and concurrency.
Practical guide to using Python for basic automation tasks, providing a gentle introduction to Python's core concepts and its practical applications.
Save
Collection of practical recipes and solutions for common Python programming tasks, covering a wide range of topics.
Comprehensive guide to Python's data analysis ecosystem, including NumPy, Pandas, and Matplotlib, with a focus on practical applications.
Save
Comprehensive guide to deep learning using Python, covering neural networks, convolutional neural networks, and recurrent neural networks.
Save
Uses a creative and engaging approach to teach Python concepts, with a focus on visual learning and hands-on exercises.
Save
Comprehensive guide to the basics of Python programming, covering data types, control flow, functions, object-oriented programming, and debugging.
Save
Is an introductory text to Python programming, with a focus on computational thinking and problem-solving.
Comprehensive guide to machine learning using Python, covering supervised and unsupervised learning, model evaluation, and deployment.
Comprehensive guide to the Python Standard Library, covering its vast collection of modules and their applications.
Practical guide to testing Python code using the pytest framework, covering unit testing, integration testing, and end-to-end testing.
Practical guide to using Python for bioinformatics tasks, covering sequence analysis, genome assembly, and data visualization.
Save
Comprehensive guide to using Python for financial analysis and modeling, covering data manipulation, financial calculations, and visualization.
Save
Concise and comprehensive reference to the Python language, covering syntax, built-in functions and objects, and advanced topics.
Save
Provides a comprehensive introduction to artificial intelligence, covering topics such as machine learning, natural language processing, and computer vision. It is suitable for students with a strong background in mathematics and computer science.
Save
Provides a comprehensive introduction to computer science, covering topics such as programming, data structures, and algorithms. It is suitable for students with a basic understanding of mathematics.
Provides a comprehensive introduction to mathematical statistics, covering topics such as probability, sampling, and hypothesis testing. It is suitable for students with a strong background in calculus.
Provides a comprehensive introduction to linear algebra, covering topics such as vector spaces, matrices, determinants, and eigenvalues. It is suitable for students with a basic understanding of algebra.
Provides a rigorous introduction to calculus, including topics such as limits, derivatives, integrals, and differential equations. It is suitable for students with a strong background in algebra and trigonometry.
Save
Provides a comprehensive overview of functions and graphs, covering topics such as linear functions, polynomials, rational functions, exponential functions, and logarithmic functions. It is suitable for students with a basic understanding of algebra.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/unl7es/programming
Share
Similar courses
Here are nine courses similar to
Programming 102: Think Like a Computer Scientist.
Data Mining with Weka
OpenCourser.com/course/0t514w/data
Data Mining with Weka
Algorithmic Design and Techniques
OpenCourser.com/course/734u3e/algorithmic
Algorithmic Design and Techniques
Executing Graph Algorithms with GraphFrames on Databricks
OpenCourser.com/course/l5reos/executing
Executing Graph Algorithms with GraphFrames on Databricks
Data Structures and Algorithms In Java ( DSA )
OpenCourser.com/course/1b0bos/data
Data Structures and Algorithms In Java ( DSA )
Algorithmic Toolbox
OpenCourser.com/course/ydmd6y/algorithmic
Algorithmic Toolbox
Using Artificial Intelligence (AI) Technologies for Business Planning and Decision-making
OpenCourser.com/course/r8ulsf/using
Using Artificial Intelligence (AI) Technologies for...
Algorithms Data Structures in Java #2 (+INTERVIEW QUESTIONS)
OpenCourser.com/course/u1bfo8/algorithms
Algorithms Data Structures in Java #2 (+INTERVIEW...
Machine Learning Algorithms with R in Business Analytics
OpenCourser.com/course/7m3kww/machine
Machine Learning Algorithms with R in Business Analytics
Getting Started with Stream Processing with Spark Streaming
OpenCourser.com/course/vfcvjw/getting
Getting Started with Stream Processing with Spark...
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser