Contents:
The Data Science Starters Academy is an introductory course in data science, designed to help people become entry-level data scientists.
As of its eighth edition (2024) it consists of 37 Learning Units, 6 Hackathons and 1 capstone project. It contains 3 units to be done during the admission process, see more about it in the admission process. This is the fourth edition held remotely.
The Data Science Starters Academy should not be mistaken for the Lisbon Data Science Academy, which is the organization responsible for running the course.
1. Structure
The Starters Academy lasts 34 weeks of part-time work over the course of 10 months. Of that time, there are 8 days where we require you to be virtually present (usually on Sundays). The rest is done remotely via GitHub, Slack and the Academy’s grading and submission system. You do not need to quit your primary responsibility to take part in this course.
The curriculum can be referenced via the curriculum-development repo.
1.1 Time commitment
You should be prepared to spend about 10 hours per week on the course and an absolute minimum of 5 hours per week in case you are having a busy week. The weekly time requirements might be higher during specialization 1 and the capstone project.
1.2 Introductory session
The introduction session is a 2-hour session that kicks off the academy.
In this session, all the rules of the Academy will be presented, together with any practical details for the current batch. Additionally, we will go over the following topics:
- Course structure and schedule
- Code of conduct
- How to ask for and give help
- Criteria for graduation
1.3 Bootcamp Details
The Bootcamp are two days with virtual classes presented by the Academy instructors, about 4 hours on each day. It will take place on two consecutive Sunday mornings.
The participation in the bootcamp is obligatory for graduation.
1.4 Specializations
Specializations are sets of Learning Units and Hackathons, focusing on specific themes.
The first Specialization consists of:
- 3 SLUs that are covered during the admissions process (see here)
- 14 SLUs that include classes presented during the Bootcamp
- 2 optional SLUs with extra content
- Hackathon #1 about Binary Classification - participation in Hackathon #1 is obligatory for graduation
Each of the remaining 5 Specializations consists of:
- 3 BLUs
- 1 hackathon
1.5 Learning Units
Learning Units (LUs) are the backbone of the Academy coursework. There are two types of LUs: Small Learning Units (SLUs) and Big Learning Units (BLUs).
1.5.1 Small Learning Units (SLUs)
SLUs are the LUs that we use in Specialization 1. They consist of:
- A virtual class during the bootcamp
- A learning notebook which explains the concepts and the coding details and shows how to solve the problems
- An example notebook which summarizes the concepts and shows code examples
- An exercise notebook which contains graded exercises for the students to solve and some optional extra exercises.
1.5.2 Big Learning Units (SLUs)
BLUs are longer LUs that are used in Specializations 2-6. They consist of:
- Up to 3 learning notebooks which explain the concepts and the coding details and show how to solve the problems
- An exercise notebook which contains graded exercises for the students to solve and some optional extra exercises.
1.6 Hackathons
In order to participate in a hackathon, the student must complete all the LUs for the given Specialization with a minimum score of 16/20.
Hackathons are longer and less structured team exercises in which the students receive a data set and solve a data science problem. Hackathons require the students to apply the skills they learned in the Learning Units, but also to communicate and manage work in a team and finally present their solution to other students and the instructors. Hackathons are graded both on the technical performance and on the solution presentation.
The hackathon instructors present their own solution and share some tips. This solution is not meant to be the top-performing solution, but a good set of baseline steps and techniques that make sense in the context of the specialization topic. This instructor solution will be published on the day after the hackathon so that students can study it and iterate on it on at their own pace.
There are 6 hackathons in total, held on selected Sundays throughout the academy. To attend the hackathon of a specialization, you must deliver all the learning units until the specialization deadline.
1.6.1 Example hackathon structure
- 8h30 - Session opening
- 9h00 - Hackathon prompt, team assignment
- 9h30 - Start hacking!
- 12h30 - Lunch time
- 14h00 - Goal - make the first submission
- 15h00 - Goal - improve your score
- 16h00 - Prepare your presentation
- 17h00 - Stop hacking!
- 17h30 - Student presentations
- 18h20 - Instructor presentation
- 18h30 - Winners announced
- 18h45 - Closing remarks, remote networking
1.7 Capstone
The Capstone is the final project where students will put everything they’ve learned together. It simulates a real-life data science project. The students receive a data set and a ‘client’ research question. They have to understand the client requirements, analyze the data set, create a model and client interface, and write a report. The Capstone must be completed individually.
2. Graduation certificate
We think that the most important thing you gain from the Academy will be the new knowledge and the people you’ll meet. But we recognize that a lot of people value a certificate stating that they’ve participated and completed the course. So, we emit a certificate to confirm the student’s completion of the Academy.
The certificate will list all the Starters Academy’s specializations, explain what it means to pass each specialization, and show the grades that you’ve achieved in each of them.
You’ll be issued a final certificate if:
- You delivered all the learning units with the minimal required score of 16/20
- You attended the virtual classes in Specialization #1 and Hackathon #1
- You missed 0 or 1 of the remaining hackathons
- You delivered and passed the capstone report
Certificates have an expected issuing time of 1 month from the end of the academy.
3. Schedule
The overall schedule and most important deadlines for batch 8:
| Activity | Date/Deadline |
|---|---|
| Admissions | June - July 2024 (see full calendar here) |
| Batch 8 kick off | Sep 2024 |
| Introductory session | Sep 2024 |
| Set-up | 15 - 21 Sep 2024 |
| Bootcamp classes | 22 and 29 Sep 2024 |
| Specialization 1 submission deadline | 19 Oct 2024 8 pm UTC+1 |
| Hackathon 1 | 20 Oct 2024 |
| Specialization 2 submission deadline | 16 Nov 2024 8 pm UTC |
| Hackathon 2 | 17 Nov 2024 |
| Specialization 3 submission deadline | 14 Dec 2024 8 pm UTC |
| Hackathon 3 | 15 Dec 2024 |
| Christmas break | 16 Dec 2024 - 5 Jan 2025 |
| Specialization 4 submission deadline | 1 Feb 2025 8 pm UTC+1 |
| Hackathon 4 | 2 Feb 2025 |
| Specialization 5 submission deadline | 1 Mar 2025 8 pm UTC+1 |
| Hackathon 5 | 2 Mar 2025 |
| Specialization 6 submission deadline | 29 Mar 2025 8 pm UTC+1 |
| Hackathon 6 | 30 Mar 2025 |
| Spring break | 31 Mar - 6 Apr 2025 |
| Capstone | 7 Apr - 15 Jun 2025 |
| Academy graduates announced | 16 Jun 2025 |
The master schedule google calendar is here
- you can add this calendar to most calendar apps so that you stay aware of all the important dates.