Welcome to the Git repository for the "FAAST Advance Data Science - Intro to Time Series" learning path 🎉
This course is designed to cover the data science concepts you'll need to start creating models for time series data. We'll cover the basics of time series data, how to work with it in Python, classic approaches for forecasting and how to use cross-validation to evaluate your models.
The course is extracted from the Lisbon Data Science Academy. You can access it here.
In this introduction, you'll learn:
- The principles driving your learning experience in this course.
- The structure of the course.
- How to setup your development environment.
- The expectations for students and mentors.
When we assembled this course, we had in mind that our students would be adult individuals with time constraints. They will be looking to implement the knowledge in their work environment and maybe discuss it with their peers.
To fulfill these expectations, we adopted the following principles:
- Prefer self-directed learning over teacher-directed learning;
- Prefer content that's easily accessible (no paywalls or subscriptions);
- Prefer content that can be immediately applied;
- Always use code examples;
- Learning by teaching is encouraged.
The learning units available in the Lisbon Data Science Academy curriculum that will be covered by this module are as follows:
In order to help mentors in tracking the progress of their mentees, we suggest using the following template:
Tracking questions is important so that we can improve the quality of the selected material, as well as create new ones.
We understand that some chapters might be really close, and students might want to ask questions directly to the mentor, to also have the question available publicly is to everybody's advantage.
Students should these instructions in order to setup their development environment.
The Lisbon Data Science Academy has already implemented a workflow for students to complete their learning units. You can find it here. However, since NOS is not affiliated with the LDSSA, exercises are not to be graded; what matters is that your code tests all pass.
Warning The exercises are not to be graded. The LDSSA expects their students to push their code to their GitHub repo to be graded on their Portal.
This is a limitation on our side, so please ignore grading instructions, which involve pushing code to their repo and logging in to their Portal.
Although we understand that time may be constrained, each student has responsibilities with their mentors, namely:
- Be open in your discussions with the mentor.
- Be courteous and respectful to your peers and mentor.
- Set your progress expectations with your mentor.
- Conduct yourself with integrity and honesty.
A mentor are tasked in ensuring their peers become better professionals, as such, we expect them to:
- Reserve at least 30 minutes per week for the people you mentor, for answering questions and giving feedback.
- Encourage your mentee to communicate openly with you.
- Be courteous and respectful to your mentees.
- Keep track of questions and progress of the mentees (see Progress tracking)
- Conduct yourself with integrity and honesty.
In order to make the best use of this learning path, you should know:
- Basic / Intermediary Python: control flow, functions, handling errors, data structures, files, virtual environments, data manipulation libraries.
- Basic Git: add, commit, checkout, merge, and rebase