A proven 6-step process for writing higher study notes for data science

I’ve been a student for a very long time. Like six years-in-post-secondary-so-far form of long.

In all of those six years and various areas of study — including data science — the one thing that I’ve develop into an authority in is note-taking. Not only that, but I’ve built and refined a system for note-taking in data science that lets you self-teach data science concepts more efficiently and effectively. Irrespective of the subject, from programming to statistics to machine learning, this note-taking system helps you to construct a deeper understanding of information science topics while also helping you higher retain the knowledge in the long term.

Probably the greatest suggestions I received from a friend in law school is to create single-page summary sheets for every unit you complete. The goal of those sheets is to condense all your many pages of notes from one unit into one document that highlights only the absolutely most vital stuff. I started fooling around with this idea for data science and it began to make an actual difference in my ability to retain and recall concepts I had learned, especially those to do with coding, mathematics, and the intricacies of constructing machine learning projects.

That is an amazing exercise in pulling out crucial pieces of data that you realize you’ll proceed to make use of each day as you progress as an information scientist. Moreover, it helps you give attention to what is really essential while discarding any fluff you will have taken note of. Not only that, but these sheets are perfect to maintain readily available for quick reference whenever you’re studying or working on a project. I prefer to do that by keeping my sheets handy on my desk or taped to a close-by wall. That way, once I’m working on projects, I can quickly reference my notes without having to dig around an excessive amount of on Google for the reply.

My favorite technique for creating these sheets is to construct a mind map with the unit name in the middle. The topics that branch off from the middle are taken from the training objectives for that unit. For instance, to create a mind map for a unit of calculus concerning derivatives, I might create branches for interpreting derivatives as rates of change, interpreting derivatives as slopes of tangent lines, differentiating algebraic and trigonometric functions, using differentials to estimate numbers and errors, applying derivatives to resolve problems, and using implicit differentiation to resolve related rate problems. Then, I fill in all the relevant tidbits of data for every branch, resembling formulas, essential reminders, key tables of data, and other such pieces which might be constantly used or relevant.

From personal experience, your data science notes are nothing without in-line examples that enable you higher relate, discover, and understand concepts.

How again and again have you ever checked out your notes and, for instance, noted that “classes are a blueprint that specifies the unique attributes and properties that an object can have” (see below) without actually with the ability to visualize what they’re or what they appear like? Don’t worry, that is more common than you think that.

Our notes are only pretty much as good because the examples we apply to them, and with regards to studying data science, our examples develop into much more critical when taking a look at concepts in programming, mathematics, and the production of visualizations (to call just a few). These are examples of topics where in-line examples next to your written notes could make an idea click for you, allowing you to discover visually what you’re talking about, and helping you relate that idea to other knowledge you will have.

My favorite strategy to include in-line notes is to make use of note-taking apps resembling OneNote, GoodNotes, or Notability which allows you plenty of freedom to create customized notes using typed text, handwritten notes, screenshots, drawn diagrams, recorded verbal notes, and more. These solutions are perfect for when you’ll want to include screenshots of code, diagrams of database systems, mathematical equations, and examples of information visualizations, to call just a few.

It’s also essential to notice that your in-line examples are also perfect places so as to add context to your notes. For instance, it could not click for you why differentials in calculus are essential to know until you understand that they’re vital for estimating numbers and errors or developing equations to explain how the speed of an event can change over time. Alternatively, you could not appreciate the importance of using several types of data visualizations until you learn that every one is best suited to representing certain forms of information over others. By providing context in your notes as to how certain data science concepts fit into the larger picture of information evaluation, you’ll be higher capable of apply these concepts and fit them together to resolve an information science problem.

Humans appear to be becoming increasingly vision-driven creatures, which is why so lots of us are succeeding in our studies once we include diagrams, flowcharts, and mind maps in our notes.

This straightforward trick lets you create more in-depth notes that give you a deeper understanding of concepts. While I disregarded the importance of flowcharting once I was studying software development, I got here to understand the easy task of drawing out logic and inserting it into my notes before cementing it in code. Having a majority of these diagrams in your notes can complement our human tendencies to focus immediately on photos and diagrams before reading text.

As much as data science is steeped in code, I find that visual representations of the logic, processes, or sequences that you simply’re carrying out might be helpful to constructing your understanding of how the various components of information science fit together — how our problem might be changed into logic that may then be coded, prolonged into machine learning systems, modified into production code, after which used to supply results that might be translated for non-tech individuals.

Diagrams are perfect for learning how different pieces of code work together, how machine learning works, or tell a greater data story. Flowcharts are obligatory for writing out coding and machine learning logic. Finally, mind maps are great tools for relating the various concepts of industry questions, code, mathematics, data, and design that make up an information science project.

Copying notes directly out of your study material has its place, like when an idea is so simply put that you simply couldn’t possibly write it any clearer. Alternatively, using your personal words to clarify concepts in plain English (or whatever your language of selection) advantages your studying by forcing you to understand the concept before you write it down.

For instance, when studying object-oriented programming (OOP) the definition of a category that you simply’re supplied with may read like this:

Classes are a template definition of methods and variables for a specific kind of object.

That’s great and all, but does it really make sense? As an alternative, let’s take a look at how I might describe classes using my very own words:

Classes are a blueprint that specifies the unique attributes and properties that an object can have.

See? That makes more sense already. Then, you’ll must create your personal definition of objects so your understanding of those OOP concepts is more concrete.

The important thing here is to make use of your personal words when writing your study notes (in specific circumstances where concepts will not be properly explained in the primary place) to enable you cement your understanding. Moreover, the additional brain power used to create your personal definition will make the concept easier to recollect when reviewing your notes. This tip can also be a component of the Feynman Technique, which you could find helpful in your data science studies.

The very best tip I ever received while teaching myself various areas of mathematics is to jot down down your thoughts while studying. This implies writing down all the things from inquiries to comments that arise, directly where they arise.

For instance, while figuring out a calculus problem, I’ll highlight areas of the issue and write my questions or comments there as I am going along. Not only does this make it really obvious where my understanding has faltered, nevertheless it also helps my instructor give me higher advice on improve my understanding.

This a part of note-taking also helps keep you accountable for what you understand and don’t understand. All of us get into the rhythm sometimes of just copying information down without actually checking to see if we understand it. By annotating your notes with comments and questions, you’re frequently checking back with yourself to see should you understand all the things you’re reading.

This tip also applies to programming, where you may type comments and questions directly into your code, in addition to some other topics where you could be taking notes, resembling those concerning machine learning or data visualization.

This might be one in every of the toughest tasks to perform whenever you’re teaching yourself data science. How do you review, revise, and test yourself in your notes frequently whenever you don’t have exams to finish or interviews to organize for? Nonetheless, that is one of the essential steps you may take to be certain that your data science notes are literally working for you.

It’s critical that you simply review, revise, and test yourself using your data science notes to not only retain the fabric higher (the plain good thing about ceaselessly reviewing, revising, and testing) but additionally to discover areas where your notes could higher serve you and where they leave a bit of to be desired in the way in which of thoroughness or the clarity of your descriptions.

As you advance in learning data science concepts, it’s not a foul idea to return to old notes and see should you can find higher ways to clarify concepts you could not have fully understood whenever you first went through them. This not only ensures that you simply’re grasping all the things properly but additionally takes advantage of all of the information mentioned above to raised improve your notes in addition to your retention and understanding of topics.

The very best strategy to do that is to take a seat down at regular intervals (this will likely be once a month, once 1 / 4, once every six months, or annually, depending on how quickly you’re studying data science) and undergo your notes, asking yourself seriously where your notes could possibly be higher (the thought behind that is that you simply’re continuously gaining experience in data science which might enable you critically evaluate how your notes could possibly be higher written or explained). Making notes of those instances, take a while to then test yourself, whether via flashcards, coding challenges, or example university tests available online. After marking the test, ask yourself again where your notes failed you in understanding concepts or where they worked rather well. From here, you may modify your notes to fit your needs.