Welcome to the Turing College Data Science Preparation Guide!

This collection of resources has two main goals and two main ways to use it.

• The Preparation Guide can be viewed as a stand-alone resource that anyone can go through on their own in order to prepare for studying Data Science at Turing College. It will give the necessary background knowledge that is usually expected in most Data Science courses - such as basics of programming, maths, probability, statistics, most common tools, IT business practices and more. For those coming from the IT world, these things can seem common knowledge; however, if you come from a completely different background, not having these skills can put you at a severe disadvantage and prevent successful, timely studying.
  • If you are a beginner preparing to study Data Science, go through the material fully by following the “Sprints” structure. It will provide a coherent, linear path through all the curriculum in conveniently grouped chunks.
• The Preparation Guide can be viewed as a reference manual for various core topics once you have already started studying Data Science. For example, if you see that you are struggling with Markdown while creating Jupyter notebooks, you can come here and check all the recommended resources related to this topic.
  • To conveniently find the necessary information about specific topics, use the section “Explore by topics”. Simply select a topic and all the material related to it in the Preparation Guide will be shown.

Depth and time estimates

Next to each resource, you will notice numbers indicating how deep you should go into that topic and roughly how many hours you should aim to spend on it. Depth categories are used as follows:

• C1 - watch or read the material. No need to look up the documentation of unfamiliar code or look up new concepts in other resources. If there are exercises that are part of the resource, you can treat them as optional. If you feel that you do not understand more than half of the concepts or code in the resource, you are most likely missing some prerequisites. Your understanding of the new material should be 45-65%.
• C2 - watch or read the material attentively. Look up the documentation of unfamiliar code and look up new concepts in other resources if they feel unclear. Your understanding of the new material should be 65-75%.
• C3 - watch or read the material very attentively. Look up the documentation of unfamiliar code and look up unfamiliar concepts in other resources until you are confident in your understanding. Your understanding of the new material should be 75-85%.
• C4 - watch or read the material while replicating all code examples that you read or see on the screen. Look up the documentation of unfamiliar code and retype all unfamiliar code blocks. Look up unfamiliar concepts in other resources until you are confident in your understanding. It is OK if there are still some concepts (or code) that you don’t understand after the material, then spend time researching them in other resources. Your understanding of the new material should be 85-90%.
• C5 - watch or read the material while replicating all code examples that you read or see on the screen. If there are still concepts (or code) that you don’t understand after the material, then spend time researching them in other resources. Your understanding of the new material should be 90-95%.
• C6 - watch or read the material while replicating all code examples that you read or see on the screen. In addition, try to replicate the functionality or concept yourself with different parameters (e.g., different dataset). If there are still concepts (or code) that you don’t understand after the material, then spend time researching them in other resources. Your understanding of the new material should be 95-100%.

For example, if you see (C4, 1.5) next to a resource, it means you should be reading the material carefully, replicating the code, aiming for an understanding of 85–90%, spending approximately 1.5 hours.

Other notes:

All the materials in the Preparation Guide are supposed to be free, although you might need to create accounts for specific platforms, e.g. Coursera. In case you see courses that seem to be paid, it is usually for a version of the course that provides certificates or similar extra features. However, going through the free versions should always be enough.

In case you find a resource that you cannot access, contact [email protected] and we will be happy to help you or revise the material if the availability of the content changes.

Sprints:

Sprint 1

Sprint 2