OpenCourser brand icon
Sign in
Sorry, this page is no longer available
We may earn an affiliate commission when you visit our partners.
Course image
Course image
edX logo

Scientific Programming for AI

Prof. Dr.-Ing. Ulrik Schroeder and Dr. Volodymyr Sokol

Get ready to dive into the exciting world of Python programming! This comprehensive course is designed to provide you with a deep understanding of fundamental Python techniques, including data structures, control statements, and functions. You'll also explore advanced concepts such as iterators, file handling, and exceptions, giving you a well-rounded foundation in Python programming.

Read more

Get ready to dive into the exciting world of Python programming! This comprehensive course is designed to provide you with a deep understanding of fundamental Python techniques, including data structures, control statements, and functions. You'll also explore advanced concepts such as iterators, file handling, and exceptions, giving you a well-rounded foundation in Python programming.

But that's not all! You'll also get hands-on experience with powerful libraries like Pandas, NumPy, and MatPlotLib, which are essential for success in Data Science and Machine Learning. These libraries will enable you to manipulate and visualize data like a pro, making your insights more impactful and your work more efficient.

Throughout the course, you'll complete weekly programming exercises, giving you the opportunity to apply and practice what you've learned. This hands-on experience will help you build confidence in your programming skills and enable you to execute programming solutions with ease.

By the end of the course, you'll be able to critically evaluate and interpret the results of your code, making you a valuable asset in any data-driven field. Whether you're looking to start a career in Data Science, Machine Learning, or simply want to expand your programming skills, this course is the perfect starting point. So, are you ready to master Python programming and unlock a world of opportunities? Let's get started!

Enroll now

What's inside

Learning objectives

  • Introduction into python
  • Data structures
  • Control statements
  • Functions
  • External modules & reference semantic
  • Functional programming & iterators
  • File handling & exceptions
  • Numpy
  • Pandas
  • Matplotlib

Syllabus

Week 00: Welcome
Scheduled: 01 July 2024
We will learn how to work with Jupyter Notebooks and will get acquainted with what we’ll learn in later courses.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Introduces Python, a foundational language in many fields, making it accessible to beginners
Covers essential Python techniques, including data structures, control statements, and functions
Explored advanced Python concepts, including iterators, file handling, and exceptions
Provides hands-on experience with real-world libraries like Pandas, NumPy, MatPlotLib
Suitable for Data Science and Machine Learning enthusiasts, providing a strong foundation in Python programming

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Hands-on python for ai & data science

According to learners, this course offers a strong foundation in Python programming, covering fundamental concepts like data structures and control statements, as well as advanced topics such as functional programming and file handling. Students particularly value the practical application of essential libraries like NumPy, Pandas, and Matplotlib, which are crucial for success in Data Science and Machine Learning. The weekly programming exercises are highlighted as a key strength, providing hands-on experience that builds confidence. It's considered an excellent starting point for career-focused individuals aiming to apply Python in data-driven fields, providing a comprehensive and well-structured learning path.
Course content is well-organized and easy to follow.
"The progression from Python basics to advanced topics like iterators was logical and easy to follow."
"I appreciated the clear weekly structure, which helped me manage my study time effectively."
"The course materials are presented in a way that allows for a smooth learning curve from start to finish."
Weekly exercises reinforce learning and build skills.
"The weekly programming exercises were crucial for applying what I learned immediately."
"I found the hands-on practice invaluable for building my coding confidence in real-world scenarios."
"The exercises were well-designed, ensuring I could execute programming solutions with ease and understanding."
Builds a solid understanding of core Python concepts.
"I feel I gained a very solid understanding of Python fundamentals, from data structures to functions."
"The clear explanations of control statements and external modules were incredibly helpful for my learning."
"I appreciate how thoroughly it covered Python's core principles before diving into more advanced libraries."
Highly relevant for aspiring data science/ML professionals.
"This course is a perfect starting point for a career in Data Science or Machine Learning."
"I feel much better prepared for roles that require scientific programming skills in AI and related fields."
"The skills learned are directly applicable to real-world data-driven fields, making it highly valuable."
Effectively teaches essential libraries for data science.
"The sections on NumPy and Pandas were incredibly useful; I can now manipulate data effectively."
"I'm confident using Matplotlib for data visualization after completing this course's material."
"This course directly equipped me with the tools needed for AI and ML programming tasks."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Scientific Programming for AI with these activities:
Connect with Python experts
Seek guidance and support from experienced Python developers to enhance your learning and career development.
Show steps
  • Attend industry events, meetups, or online forums to connect with Python professionals.
  • Reach out to Python developers on LinkedIn or other platforms.
  • Ask for mentorship or guidance, expressing your specific areas of interest.
Review linear algebra concepts
Ensure a solid understanding of linear algebra concepts, which are essential for advanced topics in data science and machine learning.
Browse courses on Linear Algebra
Show steps
  • Review notes or textbooks from previous linear algebra courses or tutorials.
  • Go through online resources or videos on linear algebra basics.
  • Solve practice problems to reinforce your understanding.
Review Python basics
Review the basics of Python programming, including data types, variables, control structures, and functions, to ensure a strong foundation for this course.
Browse courses on Python
Show steps
  • Go through your notes or textbooks from previous Python courses or tutorials.
  • Review online tutorials or resources on Python basics.
  • Complete practice exercises or quizzes to test your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice coding exercises
Engage in regular coding practice to reinforce your understanding of Python concepts and improve your problem-solving skills.
Show steps
  • Solve coding challenges from online platforms like HackerRank or LeetCode.
  • Work through the practice exercises provided in the course materials.
  • Create your own coding projects and experiment with different Python features.
Join a study group
Connect with fellow students to discuss course concepts, work on projects together, and provide support and feedback.
Show steps
  • Find or create a study group with other students in your course.
  • Meet regularly to discuss course materials, solve problems, and share insights.
  • Collaborate on projects and assignments to enhance your learning.
Explore NumPy and Pandas tutorials
Deepen your understanding of NumPy and Pandas, essential libraries for data manipulation and analysis, through guided tutorials.
Show steps
  • Follow online tutorials or workshops on NumPy and Pandas.
  • Complete the exercises and examples provided in the tutorials.
  • Apply what you learn to your own data analysis projects.
Develop a data visualization project
Showcase your data analysis and visualization skills by creating a project that presents data insights in a clear and engaging manner.
Show steps
  • Choose a dataset and identify the key insights you want to convey.
  • Use Matplotlib or other visualization libraries to create charts, graphs, or dashboards.
  • Present your project to your peers or instructors for feedback.
Build a Python portfolio
Create a collection of Python projects that demonstrate your skills and explore different applications of the language.
Show steps
  • Identify areas of Python you want to develop or showcase.
  • Plan and design projects that align with your goals.
  • Build and document your projects, highlighting your problem-solving approach and technical skills.
  • Share your portfolio with potential employers or clients to showcase your abilities.

Career center

Learners who complete Scientific Programming for AI will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Similar courses are unavailable at this time. Please try again later.
Course image
Effort
5 to 6 hours per week for 10 weeks
Level
Intermediate
Via
edX
Institution
RWTH Aachen University
Instructors
Prof. Dr.-Ing. Ulrik Schroeder
Dr. Volodymyr Sokol
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Introduces Python, a foundational language in many fields, making it accessible to beginners
Covers essential Python techniques, including data structures, control statements, and functions
Explored advanced Python concepts, including iterators, file handling, and exceptions
Provides hands-on experience with real-world libraries like Pandas, NumPy, MatPlotLib
Suitable for Data Science and Machine Learning enthusiasts, providing a strong foundation in Python programming
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Scientific Programming for AI.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
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 - 2025 OpenCourser