Learning pace requirements | Data Science

Turing College offers a self-paced learning model. It gives flexibility when choosing the goals and timeline best suited a learner’s individual needs and capabilities.

However, all scholarship receivers have a minimum pace that they need to reach to maintain their scholarship status. The required pace is shown in the platform.

Failure to complete the sprints at a required pace may result in termination of a scholarship and a requirement to pay for the studies received.

Pace table

We created the table below to make it easier and more convenient for each learner to regularly evaluate their pace and help choose the right one. Note, this table is for general planning only – you should still check your exact deadlines in the platform and discuss your situation with Turing College staff if you cannot meet them.

When starting the program

When starting the program, you should have an idea of how long you would like to study for and sometimes what roles you might be most interested in. Based on that, you can get a target for how many weeks you should complete a sprint in and how many months you should complete a module in.

For example, if you want to get a job as a machine learning engineer in no more than 11 months, you can see that you should aim to complete a sprint in 2.25 weeks on average and a module in 2.25 months on average.

We recommend choosing a target pace early and then re-evaluating the possibilities once you see what your actual realistic pace is.

During the program

Once you get through a couple of sprints, you will see what your actual pace is. Based on that, you can re-evaluate your goals.

For example, if after completing first 2 sprints you see that your pace is closer to 1.5 weeks per sprint rather than the initially planned 2.25, you can re-evaluate whether you want to become a machine learning engineer quicker - finishing the program in 6 months and aiming to get a job in 8 months. Or, you can consider spending the same 11 months on the program that you set for yourself initially, but going for a deep-learning specialisation. On the other hand, if you see that your pace is slower, e.g. 3 weeks per sprint, you can use the table to see that a data analyst focus might be better suited, as all other specialisations might take too long to finish.

The table is meant to be a general, approximate guideline and if you see that you need a more individual approach, always feel free to reach out to Turing College staff for help.

Additional notes:

• Do not worry if you are initially unsure about the data role that you want to aim for. Some learners only make a decision once they are in module 3 and that’s totally fine. Yet we highly recommend using this table even before deciding on the exact role you are aiming for as it should help you track your pace and see which option is the most suitable for you.

• Vacations, emergencies, regular community activities should all be considered as included in these timelines.

• While 3 weeks per sprint is the slowest pace allowed for scholarship receiver, we do not recommend aiming for this as a goal sprint pace. Instead, it would be much safer to a slightly quicker pace (e.g. 2 week) and have a ‘buffer’ for each sprint.

• Module 1 will be easier if you have prior programming experience. We estimate that strong experience (e.g. computer science or related studies, professional experience where coding was the main job) can speed up the module 2x, while some experience (e.g. coding a bit for a hobby or an online program completed) can speed it up 1.5x

• Module 2 will be easier if you have prior university maths/statistics experience (even a single module studying it is enough) and SQL experience. Strong experience can speed up the module 2x, while some experience can speed it up 1.5x

• Modules 3, 4 require all of the skills used in modules 1-2 and will only be easier if you had prior experience with machine learning or data science. Strong professional experience with machine learning can speed up the program 2x, while some experience can speed it up 1.5x.

• Even if you are experienced in a topic, you can still choose to go even deeper into it and thus spend a reasonable, non-reduced amount of time for it.

• Module 4 (deep learning) can take more time than previous individual modules due to difficulty of the subject.

• A specialisation module is considered as 4 sprints when calculating sprint pace.

• Specialisation modules tend to take slightly less time (~0.75x) than non-specialisation modules.

• The estimation for hiring time is based on our current view of the market and experience of our learners. Bigger focus is on the Lithuanian market, but jobs in the rest of EU are taken into account as well.