How to Write Better Study Notes for Data Science | by Madison Hunter | Apr, 2023

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

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

In all of those six years and various areas of study — including data science — the one thing that I’ve become an expert in is note-taking. Not only that, but I’ve built and refined a system for note-taking in data science that allows you to self-teach data science concepts more efficiently and effectively. No matter the topic, from programming to statistics to machine learning, this note-taking system helps you to build a deeper understanding of data science topics while also helping you better retain the information in the long run.

One of the best tips I received from a friend in law school is to create single-page summary sheets for each unit you complete. The goal of these sheets is to condense all of your many pages of notes from one unit into one document that highlights only the absolutely most important stuff. I began playing around with this concept for data science and it began to make a real difference in my ability to retain and recall concepts I had learned, especially those to do with coding, mathematics, and the intricacies of building machine learning projects.

This is a great exercise in pulling out the most important pieces of information that you know you will continue to use daily as you progress as a data scientist. Furthermore, it helps you focus on what is truly important while discarding any fluff you may have taken note of. Not only that, but these sheets are perfect to keep on hand for quick reference when you’re studying or working on a project. I like to do this by keeping my sheets handy on my desk or taped to a nearby wall. That way, when I’m working on projects, I can quickly reference my notes without having to dig around too much on Google for the answer.

My favorite technique for creating these sheets is to build a mind map with the unit name in the center. The topics that branch off from the center are taken from the learning objectives for that unit. For example, to create a mind map for a unit of calculus concerning derivatives, I would 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 solve problems, and using implicit differentiation to solve related rate problems. Then, I fill in all of the relevant tidbits of information for each branch, such as formulas, important reminders, key tables of information, and other such pieces that are continuously used or relevant.

From personal experience, your data science notes are nothing without in-line examples that help you better relate, identify, and understand concepts.

How many times have you looked at your notes and, for example, noted that “classes are a blueprint that specifies the unique attributes and properties that an object may have” (see below) without actually being able to visualize what they are or what they look like? Don’t worry, this is more common than you think.

Our notes are only as good as the examples we apply to them, and when it comes to studying data science, our examples become even more critical when looking at concepts in programming, mathematics, and the production of visualizations (to name a few). These are examples of topics where in-line examples next to your written notes can make a concept click for you, allowing you to identify visually what you’re talking about, and helping you relate that concept to other knowledge you have.

My favorite way to include in-line notes is to use note-taking apps such as OneNote, GoodNotes, or Notability which allows you lots 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 need to include screenshots of code, diagrams of database systems, mathematical equations, and examples of data visualizations, to name a few.

It’s also important to note that your in-line examples are also perfect places to add context to your notes. For example, it may not click for you why differentials in calculus are important to know until you understand that they’re vital for estimating numbers and errors or developing equations to describe how the rate of an event can change over time. Alternatively, you may not appreciate the importance of using different types of data visualizations until you learn that each one is better suited to representing certain forms of data over others. By providing context in your notes as to how certain data science concepts fit into the bigger picture of data analysis, you’ll be better able to apply these concepts and fit them together to solve a data science problem.

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

This simple trick allows you to create more in-depth notes that provide you with a deeper understanding of concepts. While I disregarded the importance of flowcharting when I was studying software development, I came to appreciate the simple task of drawing out logic and inserting it into my notes before cementing it in code. Having these types of 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’re carrying out can be beneficial to building your understanding of how the different components of data science fit together — how our problem can be turned into logic that can then be coded, extended into machine learning systems, modified into production code, and then used to produce results that can be translated for non-tech individuals.

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

Copying notes directly from your study material has its place, like when a concept is so simply put that you couldn’t possibly write it any clearer. On the other hand, using your own words to explain concepts in plain English (or whatever your language of choice) benefits your studying by forcing you to understand the concept before you write it down.

For example, when studying object-oriented programming (OOP) the definition of a class that you’re provided with may read like this:

Textbook definition: Classes are a template definition of methods and variables for a particular type of object.

That’s great and all, but does it really make sense? Instead, let’s look at how I would describe classes using my own words:

Author’s definition: Classes are a blueprint that specifies the unique attributes and properties that an object may have.

See? That makes more sense already. Then, you’ll have to create your own definition of objects so your understanding of these OOP concepts is more concrete.

The key here is to use your own words when writing your study notes (in specific circumstances where concepts are not properly explained in the first place) to help you cement your understanding. Additionally, the extra brain power used to create your own definition will make the concept easier to remember when reviewing your notes. This tip is also a part of the Feynman Technique, which you may find helpful in your data science studies.

The best tip I ever received while teaching myself various areas of mathematics is to write down your thoughts while studying. This means writing down everything from questions to comments that arise, directly where they arise.

For example, while working out a calculus problem, I’ll highlight areas of the problem and write my questions or comments there as I go along. Not only does this make it really obvious where my understanding has faltered, but it also helps my instructor give me better advice on how to improve my understanding.

This part of note-taking also helps keep you accountable for what you understand and don’t understand. We all 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 regularly checking back with yourself to see if you understand everything you’re reading.

This tip also applies to programming, where you can type comments and questions directly into your code, as well as any other topics where you may be taking notes, such as those concerning machine learning or data visualization.

This can be one of the hardest tasks to accomplish when you’re teaching yourself data science. How do you review, revise, and test yourself on your notes regularly when you don’t have exams to complete or interviews to prepare for? However, this is one of the most important steps you can take to ensure that your data science notes are actually working for you.

It’s critical that you review, revise, and test yourself using your data science notes to not only retain the material better (the obvious benefit of frequently reviewing, revising, and testing) but also to identify areas where your notes could better serve you and where they leave a little to be desired in the way of thoroughness or the clarity of your descriptions.

As you advance in learning data science concepts, it’s not a bad idea to return to old notes and see if you can find better ways to explain concepts you may not have fully understood when you first went through them. This not only ensures that you’re grasping everything properly but also takes advantage of all of the tips mentioned above to better improve your notes as well as your retention and understanding of topics.

The best way to do this is to sit down at regular intervals (this may be once a month, once a quarter, once every six months, or once a year, depending on how quickly you’re studying data science) and go through your notes, asking yourself seriously where your notes could be better (the idea behind this is that you’re constantly gaining experience in data science which can help you critically evaluate how your notes could be better written or explained). Making notes of these instances, take some time 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 really well. From here, you can modify your notes to suit your needs.

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

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

In all of those six years and various areas of study — including data science — the one thing that I’ve become an expert in is note-taking. Not only that, but I’ve built and refined a system for note-taking in data science that allows you to self-teach data science concepts more efficiently and effectively. No matter the topic, from programming to statistics to machine learning, this note-taking system helps you to build a deeper understanding of data science topics while also helping you better retain the information in the long run.

One of the best tips I received from a friend in law school is to create single-page summary sheets for each unit you complete. The goal of these sheets is to condense all of your many pages of notes from one unit into one document that highlights only the absolutely most important stuff. I began playing around with this concept for data science and it began to make a real difference in my ability to retain and recall concepts I had learned, especially those to do with coding, mathematics, and the intricacies of building machine learning projects.

This is a great exercise in pulling out the most important pieces of information that you know you will continue to use daily as you progress as a data scientist. Furthermore, it helps you focus on what is truly important while discarding any fluff you may have taken note of. Not only that, but these sheets are perfect to keep on hand for quick reference when you’re studying or working on a project. I like to do this by keeping my sheets handy on my desk or taped to a nearby wall. That way, when I’m working on projects, I can quickly reference my notes without having to dig around too much on Google for the answer.

My favorite technique for creating these sheets is to build a mind map with the unit name in the center. The topics that branch off from the center are taken from the learning objectives for that unit. For example, to create a mind map for a unit of calculus concerning derivatives, I would 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 solve problems, and using implicit differentiation to solve related rate problems. Then, I fill in all of the relevant tidbits of information for each branch, such as formulas, important reminders, key tables of information, and other such pieces that are continuously used or relevant.

From personal experience, your data science notes are nothing without in-line examples that help you better relate, identify, and understand concepts.

How many times have you looked at your notes and, for example, noted that “classes are a blueprint that specifies the unique attributes and properties that an object may have” (see below) without actually being able to visualize what they are or what they look like? Don’t worry, this is more common than you think.

Our notes are only as good as the examples we apply to them, and when it comes to studying data science, our examples become even more critical when looking at concepts in programming, mathematics, and the production of visualizations (to name a few). These are examples of topics where in-line examples next to your written notes can make a concept click for you, allowing you to identify visually what you’re talking about, and helping you relate that concept to other knowledge you have.

My favorite way to include in-line notes is to use note-taking apps such as OneNote, GoodNotes, or Notability which allows you lots 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 need to include screenshots of code, diagrams of database systems, mathematical equations, and examples of data visualizations, to name a few.

It’s also important to note that your in-line examples are also perfect places to add context to your notes. For example, it may not click for you why differentials in calculus are important to know until you understand that they’re vital for estimating numbers and errors or developing equations to describe how the rate of an event can change over time. Alternatively, you may not appreciate the importance of using different types of data visualizations until you learn that each one is better suited to representing certain forms of data over others. By providing context in your notes as to how certain data science concepts fit into the bigger picture of data analysis, you’ll be better able to apply these concepts and fit them together to solve a data science problem.

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

This simple trick allows you to create more in-depth notes that provide you with a deeper understanding of concepts. While I disregarded the importance of flowcharting when I was studying software development, I came to appreciate the simple task of drawing out logic and inserting it into my notes before cementing it in code. Having these types of 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’re carrying out can be beneficial to building your understanding of how the different components of data science fit together — how our problem can be turned into logic that can then be coded, extended into machine learning systems, modified into production code, and then used to produce results that can be translated for non-tech individuals.

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

Copying notes directly from your study material has its place, like when a concept is so simply put that you couldn’t possibly write it any clearer. On the other hand, using your own words to explain concepts in plain English (or whatever your language of choice) benefits your studying by forcing you to understand the concept before you write it down.

For example, when studying object-oriented programming (OOP) the definition of a class that you’re provided with may read like this:

Textbook definition: Classes are a template definition of methods and variables for a particular type of object.

That’s great and all, but does it really make sense? Instead, let’s look at how I would describe classes using my own words:

Author’s definition: Classes are a blueprint that specifies the unique attributes and properties that an object may have.

See? That makes more sense already. Then, you’ll have to create your own definition of objects so your understanding of these OOP concepts is more concrete.

The key here is to use your own words when writing your study notes (in specific circumstances where concepts are not properly explained in the first place) to help you cement your understanding. Additionally, the extra brain power used to create your own definition will make the concept easier to remember when reviewing your notes. This tip is also a part of the Feynman Technique, which you may find helpful in your data science studies.

The best tip I ever received while teaching myself various areas of mathematics is to write down your thoughts while studying. This means writing down everything from questions to comments that arise, directly where they arise.

For example, while working out a calculus problem, I’ll highlight areas of the problem and write my questions or comments there as I go along. Not only does this make it really obvious where my understanding has faltered, but it also helps my instructor give me better advice on how to improve my understanding.

This part of note-taking also helps keep you accountable for what you understand and don’t understand. We all 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 regularly checking back with yourself to see if you understand everything you’re reading.

This tip also applies to programming, where you can type comments and questions directly into your code, as well as any other topics where you may be taking notes, such as those concerning machine learning or data visualization.

This can be one of the hardest tasks to accomplish when you’re teaching yourself data science. How do you review, revise, and test yourself on your notes regularly when you don’t have exams to complete or interviews to prepare for? However, this is one of the most important steps you can take to ensure that your data science notes are actually working for you.

It’s critical that you review, revise, and test yourself using your data science notes to not only retain the material better (the obvious benefit of frequently reviewing, revising, and testing) but also to identify areas where your notes could better serve you and where they leave a little to be desired in the way of thoroughness or the clarity of your descriptions.

As you advance in learning data science concepts, it’s not a bad idea to return to old notes and see if you can find better ways to explain concepts you may not have fully understood when you first went through them. This not only ensures that you’re grasping everything properly but also takes advantage of all of the tips mentioned above to better improve your notes as well as your retention and understanding of topics.

The best way to do this is to sit down at regular intervals (this may be once a month, once a quarter, once every six months, or once a year, depending on how quickly you’re studying data science) and go through your notes, asking yourself seriously where your notes could be better (the idea behind this is that you’re constantly gaining experience in data science which can help you critically evaluate how your notes could be better written or explained). Making notes of these instances, take some time 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 really well. From here, you can modify your notes to suit your needs.