This page is dedicated to gather useful tips, tricks and materials
for data science people.

Useful materials

Copilot for students

Did you know that with your EPFL student email, you can set up GitHub Copilot for free in VS Code? 🤖 GitHub Copilot is an AI-powered tool that helps developers write code faster and more efficiently.

To get started, simply follow the steps outlined below. It's quick and easy, and you'll be up and running in no time. 💻 🚀

Read more here! 

Where to find semester and masters project ?

Are you also struggling to find the best project for your semester project or your Master's thesis in Data Science topics? IC has gathered a list of labs which are offering  topics which can be interesting for data scientist. Check it out here!

Data Science Roadmap

Data visualizations

Data visualization is an essential skill for anyone working with data. Visualizations help to communicate insights, patterns, and trends in a way that is easily digestible for stakeholders and decision-makers. In this post, we'll walk through a roadmap for data visualization in data science.

Learn the fundamentals of data visualization

1.To create effective and meaningful visualizations, it's important to understand the basics of data visualization. This includes the different types of charts and graphs, color theory, and principles of design. By learning these fundamentals, you'll be able to create visualizations that are both aesthetically pleasing and easy to understand.

DataViz book 

Another way to deepen your understanding of data visualization is to read a book on the topic. One highly recommended book is "Data Visualization: A Handbook for Data Driven Design" by Andy Kirk. This book provides a comprehensive overview of data visualization, covering topics such as data design, visual encoding, and interaction design.

The book is suitable for both beginners and more experienced practitioners, with clear explanations of key concepts and plenty of examples to illustrate different visualization techniques. The author also provides practical advice on how to choose the right visualization for your data, and how to effectively communicate your findings to others.

You can find the book in the library! 

Choose your tools

2.There are many tools available for creating data visualizations, including programming languages like Python and R, as well as specialized software like Tableau and PowerBI. Additionally, JavaScript has become a popular choice for data visualization, with libraries such as D3.js and Chart.js providing powerful and flexible options.

When choosing your tool, consider your specific needs and goals. For example, if you prefer to work in a coding environment and want more control over the visualization, a programming language like Python or JavaScript might be a good choice. On the other hand, if you're looking for a tool that is easy to use and requires no coding, Tableau or PowerBI might be a better option. Additionally, some tools may be better suited for certain types of visualizations, such as interactive visualizations, which may require specialized tools like Bokeh or Plotly.

If you choose to use a programming language for data visualization, such as Python or R, it's important to learn the syntax and functions specific to data visualization libraries. Some popular data visualization libraries in Python include Matplotlib, Seaborn, and Plotly, while popular R libraries include ggplot2 and lattice.

Data Visualization EPFL Course 

If you're looking to deepen your understanding of data visualization and gain hands-on experience, taking a course can be a great option. One course to consider is COM-480, at the EPFL. This course covers a range of topics, including web development, JavaScript, data-driven documents (D3.js), and designing visualizations. 

Learn more about the course here! 

Online course

If you're looking for a free and flexible way to learn about data visualization, consider taking an online course. One highly recommended course is offered by FreeCodeCamp, a non-profit organization that provides free coding education to people around the world.

Their course on data visualization covers topics such as data visualization theory, D3.js, and creating interactive visualizations. The course is self-paced, so you can learn at your own speed and take breaks when needed. Additionally, FreeCodeCamp offers a supportive community of learners and mentors who can help answer your questions and provide feedback on your work.

See here the link of the course! 

Clean and preprocess your data

3. Before creating visualizations, you must clean and preprocess your data to ensure that it is accurate, consistent, and in a format that can be easily visualized. This includes tasks such as removing duplicates, handling missing values, and transforming data types.

Choose the right visualization

4. Once your data is cleaned and preprocessed, it's important to choose the visualization that best suits the data and the message you want to convey. Common types of visualizations include bar charts, line charts, scatter plots, and heatmaps. It's also important to consider design principles such as simplicity, clarity, and coherence when choosing the visualization.

Python graph gallery

If you're looking for inspiration and examples of data visualizations created with Python, be sure to check out the Python Graph Gallery. This website is a comprehensive resource for data visualization using Python, with a wide range of examples and tutorials on different types of visualizations.

The Python Graph Gallery includes examples of popular Python visualization libraries such as Matplotlib, Seaborn, and Plotly. The gallery is organized by type of visualization, making it easy to find examples of specific types of charts or graphs.

Inspire yourself here! 

Create the visualization

5. Using your chosen tool, create the visualization. Be sure to take into account the design principles you learned earlier and make adjustments as necessary. For example, you might need to adjust the colors or font size to make the visualization easier to read.

Analyze and interpret the visualization

6. Once the visualization is complete, analyze and interpret the insights it provides. Use the visualization to communicate insights to stakeholders and decision-makers. This might involve highlighting specific trends or patterns in the data or pointing out areas that require further investigation.

Iterate and improve

7. Finally, iterate and improve your visualization as necessary, based on feedback and new insights. Keep refining your skills and tools to create better and more effective visualizations.

In conclusion, data visualization is an essential skill for anyone working with data. By following this roadmap, you can develop your skills in data visualization and create effective and meaningful visualizations that help you communicate insights and drive decisions. Good luck!