Julia Scientific Programming

Go to Course: https://www.coursera.org/learn/julia-programming

Introduction

## Course Review: Julia Scientific Programming on Coursera ### Overview The **Julia Scientific Programming** course on Coursera is a fantastic introduction tailored for those eager to explore one of the most cutting-edge programming languages in the realm of scientific computing. Julia is engineered for high-performance tasks, making it ideal for various applications across science domains including physics, chemistry, astronomy, engineering, data science, and bioinformatics. The course is structured to accommodate both beginners with no prior programming experience and those who might already have a foundation in programming. Given the increasing significance of data analytics and computational science, learning Julia not only equips you with a new skill but also broadens your career horizons. ### Course Structure The course is thoughtfully laid out over four weeks, seamlessly blending theory with practical applications. Below is a breakdown of what to expect: 1. **Welcome to the Course** - A warm introductory note sets a positive tone. The teaching team, Henri and Juan, encourages students to see this experience as the beginning of a meaningful relationship with programming in Julia. This module emphasizes exploration, laying the groundwork for understanding both the basics of Julia and its applications. 2. **A Context for Exploring Julia: Working with Data** - Here, you'll delve into a compelling case study of the Ebola epidemic. This hands-on approach allows learners to engage with real data, teaching essential skills like: - Creating arrays from dataset. - Utilizing logical structures (IF and FOR) to navigate through data. - Visualizing data through plotting techniques. - By the end of this module, students will have created visual representations that illustrate the disease's incidence across various countries, reinforcing the practical significance of their coding skills. 3. **Notebooks as Julia Programs** - This week introduces the use of Jupyter notebooks, popular in the data science community for interactive coding. You will engage with the SIR epidemiological model, gaining insights into: - Understanding compartments that categorize populations within the model (susceptible, infectious, recovered). - Plotting model predictions against real data, a crucial skill in data analysis. - By learning how to adjust model parameters, you'll not only hone your coding skills but also understand how to derive insights from data - an invaluable skill across numerous scientific fields. 4. **Structuring Data and Functions in Julia** - In the final module, focus shifts to sophisticated data management and statistical analysis. You'll learn to: - Create your own functions and utilize collections effectively. - Generate random variables, dataframes, and diverse visualizations. - Conduct statistical tests and explore data exporting. - This section is particularly beneficial for those interested in diving deeper, as additional honors materials are provided for advanced learners. ### Recommendations I highly recommend **Julia Scientific Programming** for anyone interested in scientific computing or data analysis. The course does an exceptional job making complex concepts accessible through practical case studies and hands-on exercises. The incremental learning approach allows beginners to grasp foundational skills while also providing experienced programmers with a platform to build advanced competencies in Julia. The community aspect of Coursera also means that you have the opportunity to interact with peers, ask questions, and share insights. This collaborative environment enhances the learning experience. ### Conclusion Whether you're a novice looking to step into programming or seeking to upskill as a data scientist or researcher, **Julia Scientific Programming** serves as a solid stepping stone into the world of programming. The course promises not just knowledge, but the ability to apply it in real-world scenarios, making it a valuable asset to your professional toolbox. Don't miss out on the chance to explore this dynamic language and its vast potential!

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

In our case study we use Julia to store, plot, select and slice data from the Ebola epidemic. Taking real data, we explain how to work in Julia using arrays, and for loops to work with the structures. By the end of this module, you will be able to: create an array from data; learn to use the logical structures IF and FOR ; conduct basic array slicing, getting the incidence data and generating total number of cases; use Plots to generate graphs and plot data; and combine the Ebola data outputs to show a plot of disease incidence in several countries.

in this week, we demonstrate how it is possible to use Julia in the notebook environment to interpret a model and its fit to the data from the Ebola outbreak. For this, we apply the well-known SIR compartmental model in epidemiology. The SIR model labels three compartments, namely S = number susceptible, I =number infectious, and R =number recovered. By the end of this module, you will be able to: understand the SIR models; describe the basic parameters of an SIR model; plot the model-predicted curve and the data on the same diagram; adjust the parameters of the model so the model-predicted curve is close (or rather as close as you can make it) to the data.

As a scientific computing language, Julia has many applications and is particularly well suited to the task of working with data. In this last module, we will use descriptive statistics as our topic to explore the power of Julia. You should see this week as offering you a chance to further explore concepts introduced in week one and two. You will also be introduced to more efficient ways of managing and visualizing your data. We have also included additional, honors material for those who want to explore further with Julia around functions and collections. By the end of this module, you will be able to: 1. Practice basic functions in Julia 2.Creating random variables from data point values 3. Build your own Dataframes 4. Create a variety of data visualisations 5. Conduct statistical tests 6. Learn how to export your data.