Julia Scientific Programming from Coursera

This course introduces you to Julia as a first programming language. Julia is a high-level, high-performance dynamic programming language developed specifically for scientific computing. This language will be particularly useful for applications in physics, chemistry, astronomy, engineering, data science, bioinformatics, and many more. You can start programming with Julia within Coursera and it can also be used from the command line, program files, or a Jupyter notebook.

Julia is designed to address the requirements of high-performance numerical and scientific computing while being effective for general-purpose programming. You will be able to access all the available processors and memory, scrape data from anywhere on the web, and have it always accessible through any device you care to use as long as it has a browser. Join us to discover new computing possibilities. Let's get started on learning Julia.

By the end of the course you will be able to:

- Programme using the Julia language by practicing through assignments

- Write your own simple Julia programs from scratch

- Understand the advantages and capacities of Julia as a computing language

- Work in Jupyter notebooks using the Julia language

- Use various Julia packages such as Plots, DataFrames and Stats

The course is delivered through video lectures, on-screen demonstrations, quizzes, and practical peer-reviewed projects designed to give you an opportunity to work with the packages.

What's inside

Syllabus

Welcome to the course

A warm welcome to Julia Scientific Programming. Over the next four weeks, we will provide you with an introduction to what Julia can offer. This will allow you to learn the basics of the language, and stimulate your imagination about how you can use Julia in your own context. This is all about you exploring Julia - we can only demonstrate some of the capacity and encourage you to take the first steps. For those of you with a programming background, the course is intended to offer a jumpstart into using this language. If you are a novice or beginner programmer, you should follow along the simple coding but recognising that working through the material will not be sufficient to make you a proficient programmer in four weeks. You could see this as the ‘first date’ at the beginning of a long and beautiful new relationship. There is so much you will need to learn and discover. Good luck and we hope you enjoy the course! Best wishes, Henri and Juan

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on labs and interactive materials

Develops professional skills or deep expertise

Taught by Juan H. Klopper and Henri Laurie

Explores scientific computing using Julia

Strong fit for those with no programming background and those with a strong programming background

Uses a Jupyter notebook for a programming environment

Reviews summary

Introduction to julia for scientific computing

According to learners, this course serves as a solid introduction to Julia programming, particularly for applications in scientific computing. Many appreciated the practical examples using real-world data like the Ebola outbreak and the SIR model, finding them engaging and helpful for understanding concepts. The course provides a good overview of working with essential packages like Plots and DataFrames and utilizing Jupyter notebooks. However, some students found the pace challenging, especially if they were absolute beginners to programming. There were also recurring frustrations with the peer-graded assignments and occasional notes that the content or package versions felt slightly dated.

Some aspects may not reflect the latest Julia versions.

"The Julia version used in the course seemed a bit behind the current stable release."

"Found that some package syntax or best practices have evolved since the course was made."

"Needed to look up updated information for certain functions or recommended workflows."

Course includes engaging case studies and data applications.

"The Ebola data case study was a highlight; it made the learning relevant and practical."

"Using the SIR model really helped connect the programming concepts to scientific modeling."

"Working with Plots and DataFrames on the provided examples felt very hands-on and useful."

Provides a foundational understanding of Julia basics.

"Good starting point to learn the basics of Julia programming, especially for someone new to the language."

"I found the initial modules to be very clear and easy to follow, a great first look at Julia."

"This course provided a necessary groundwork for using Julia in scientific contexts."

Issues reported with assignments and grading process.

"The peer review process was inconsistent and often frustrating, impacting my motivation."

"Some assignments felt unclear or had issues with the automatic tests."

"I spent more time troubleshooting the assignment submission than focusing on the code itself..."

Pace can be difficult for true programming novices.

"Moves very quickly if you're new to programming; requires significant extra effort."

"As a beginner, I struggled to keep up with the speed the topics were introduced at."

"Definitely helpful if you have some prior programming knowledge, not truly for absolute first-timers."

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 Julia Scientific Programming with these activities:

Refresh your mathematics skills

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Revise your mathematics skills to improve your understanding of the mathematical concepts used in the Julia programming language.

Browse courses on Mathematics

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Review your notes from previous mathematics courses.
Work through practice problems from textbooks or online resources.
Enroll in an online or in-person mathematics course or workshop.

Review Linear Algebra

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Review the fundamentals of linear algebra, which are essential for understanding many of the concepts in this course, such as matrix operations and solving systems of linear equations.

Browse courses on Linear Algebra

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Go over your notes from a previous linear algebra course.
Work through practice problems from a textbook or online resource.

Complete Julia Tutorials

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Explore online tutorials and resources to supplement your understanding of Julia's features and capabilities.

Browse courses on Online Learning

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Go through the official Julia documentation and tutorials.
Follow video tutorials on YouTube or other online platforms.

Four other activities

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Practice Julia Coding Exercises

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Regularly practice coding exercises to strengthen your understanding of Julia syntax and improve your programming skills.

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Solve coding challenges from online platforms like HackerRank or LeetCode.
Work through the practice problems provided in the course materials.

Practice coding challenges

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Improve your Julia programming skills by solving coding challenges on platforms like LeetCode or HackerRank.

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Choose a coding challenge platform and sign up.
Select a challenge that matches your skill level.
Read the problem statement carefully and analyze the requirements.
Implement your solution in Julia.
Submit your solution and review the feedback.

Contribute to Julia Open Source Projects

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Participate in open-source Julia projects to gain practical experience and contribute to the development of the Julia ecosystem.

Browse courses on Community Involvement

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Identify open-source Julia projects that align with your interests.
Review the project documentation and contribute bug reports or feature requests.
Write code and submit pull requests to the project.

Build a data visualization project

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Demonstrate your proficiency in data visualization by creating an interactive dashboard or visualization using Julia packages like Plots.jl and DataFrames.jl.

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Identify a dataset that is relevant to your interests.
Import the dataset into Julia and explore its structure.
Choose appropriate visualizations to represent the data.
Use Julia packages to create the visualizations.
Deploy your dashboard or visualization online.

Career center

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

Data Scientist

Data scientists use scientific methods, processes and algorithms to extract knowledge and insights from data. They typically work with large datasets to identify patterns and trends that can be used to make predictions and develop solutions to problems.

See salaries and explore the career path for Data Scientist

Data Analyst

Data analysts typically collect, analyze and interpret large datasets to identify trends and patterns. They use statistical methods and techniques to extract meaningful insights from data, and to develop predictive models.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians collect, analyze and interpret data to provide insights and make predictions. They use statistical methods and techniques to identify trends and patterns in data, and to develop models that can be used to make predictions about future events.

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Astrophysicist

Astrophysicists, often called space scientists, typically study the universe beyond our solar system. They examine the physical properties and behavior of galaxies, stars and other celestial objects. They often concentrate on single stars or star systems, or on specific types of galaxies. They could also study the universe as a whole, including its origin, structure and evolution.

See salaries and explore the career path for Astrophysicist

Biostatistician

Biostatisticians typically work with scientists and doctors to design studies that collect and analyze large sets of biological or health-related data. They use statistical methods to test the validity of scientific theories and hypotheses, and to develop mathematical models that can be used to predict the outcomes of experiments and studies. They could also be involved in the development of new drugs and treatments.

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Geophysicist

Geophysicists typically study the physical properties and behavior of the Earth, including its atmosphere, oceans and landmasses. They use a variety of techniques to collect data about the Earth, including seismic waves, gravity measurements and magnetic surveys.

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Mathematician

Mathematicians typically work in research and academia, developing new mathematical theories and solving problems in various fields. They use mathematical methods and techniques to analyze data, develop models and simulations, and solve problems in areas such as finance, engineering and science.

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Physicist

Physicists typically work in research and academia, developing new theories and solving problems in various fields of physics. They use mathematical methods and techniques to analyze data, develop models and simulations, and solve problems in areas such as astrophysics, particle physics and nuclear physics.

See salaries and explore the career path for Physicist

Quantitative Analyst

Quantitative analysts typically use mathematical and statistical methods to analyze financial data. They develop models and simulations to identify trends and patterns, and to make predictions about financial markets. They also work with traders and portfolio managers to develop investment strategies.

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Operations Research Analyst

Operations research analysts typically use mathematical and analytical methods to solve complex problems in business and industry. They develop models and simulations to analyze data, identify trends and patterns, and to make recommendations on how to improve efficiency and profitability.

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Chemical Engineer

Chemical engineers combine elements of physics, chemistry, biology and mathematics to produce products such as food, pharmaceuticals, clothing and plastics.

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Software Engineer

Software engineers typically design, develop and maintain software applications. They use programming languages and software development tools to create software that meets the needs of users.

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Civil Engineer

Civil engineers typically design and supervise the construction of infrastructure such as roads, bridges and buildings. They also work on projects such as water supply and waste management systems, and coastal protection.

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Financial Analyst

Financial analysts typically provide advice to individuals and organizations on investment decisions. They analyze financial data to identify trends and patterns, and to make recommendations on how to invest money. They could also work in corporate finance, where they would analyze the financial performance of companies and make recommendations on how to improve profitability.

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Systems Analyst

Systems analysts typically work with businesses and organizations to analyze their systems and processes. They identify areas for improvement and then design and implement solutions to improve efficiency and productivity.

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