7 Quick and Easy Tips That Will Help You Learn Data Science Faster | by Madison Hunter | Jul, 2022

#3 is a game-changer that will keep you out of coding tutorial purgatory

Much like scaling a mountain, there are no shortcuts to the summit when learning data science.

Data science is an all-encompassing discipline that takes the best of programming, mathematics, and design to produce stories from data that help the world make decisions. Unfortunately, this also means that anyone looking to study it is in for a lot of hard work and long months to master the skills needed to fulfill one of these coveted jobs.

Luckily, there are some small tricks you can use that will help smooth the path ahead of you. While they won’t be the elusive non-existent chairlift that will take you straight to the summit, they will help you learn more efficiently and may shave time off your learning experience.

As discussed above, data science is an all-encompassing discipline that involves programming, mathematics, hard-core computing, design, industry knowledge, and storytelling.

Because there is so much to learn in a short period, it’s important to know exactly what you need to learn before getting started.

This means taking a deep dive into the type of data science career you want and looking at all of the required skill sets. Data science careers vary greatly in daily tasks, technical requirements, and required knowledge. For example, a data scientist working in academia will likely have a greater requirement for differential equations and number theory compared to a data analyst working for a small company who only needs to know arithmetic, algebra, and statistics.

It’s important at this time, to be honest with yourself in determining what kind of data science job you want. This will greatly impact what you need to learn and will help you with the following tip in setting up your learning plan. While the specifics for each job will vary depending on seniority and industry (researching specific job requirements on Indeed or LinkedIn is a great idea to get a clearer picture), the jobs in data science can generally be broken down into three general categories: data scientists, data analysts, and data engineers.

Here’s a resource to help you decide what kind of career in data science you’re looking for:

Here are some resources to help you decide what you need to learn for each type of job in data science:

What Skills Are Important for a Data Engineering Role at FAANG

Most learning plans you see on the internet are deep, extravagant roadmaps that involve a huge breadth and depth of knowledge that could only be accomplished through years of study.

What these plans fail to recognize is that you only need enough knowledge to land a job — the rest of the skills can be learned afterward. This means that you only need to learn enough to be able to carry out the daily tasks of a data scientist and the true value of your contribution to a company can be built up over time. While everyone would love to make a massive impact from day one, it’s more likely that you will grow your impact over months of high-quality work and learning on the job.

Therefore, you need to build a learning plan that only gives you as much knowledge as you need to get started — no more and no less.

This can be done by looking at existing learning plans available on the internet and trimming the fat. By reducing what you need to learn to the bare minimum, you reduce the time required to break into data science and you free up more time for you to master the exact skills you will need.

The key to properly learning anything in data science is to mix theoretical and practical learning.

While it sounds like an unnecessary extension of the learning process, backing up your theoretical knowledge with hands-on experience will help solidify your learning and will open the doors for more efficient knowledge retention.

The trick to mapping your theoretical knowledge to practical knowledge is to begin working on projects from day one. Each project doesn’t have to be life-changing, but it should incorporate something new that you have learned.

For example, on day one you learn the theoretical knowledge behind writing code in Python. Your project for that day could be to write code that prints “Hello World” to the terminal. On day two, you learn how to calculate the mean of a sample. Your project for that day could be to enter a list of numbers into your Python program and calculate their mean. On day three, you learn the theory behind data visualization. Your project for that day would be to visualize your list of numbers, along with its mean.

While a silly example, it gives you a foundation on which to apply your theoretical knowledge to practical problems. By the end of your learning experience, you may find that your program from day one that prints “Hello World” to the console is now giving you a full analysis of data you scraped from the internet. The key is to keep advancing your project (or projects) every day and pushing yourself to incorporate your new knowledge.

If there’s one thing that self-study reveals it’s that we all have learning weaknesses that keep us from reaching our maximum learning potential.

Learning weaknesses can include simple things like being easily distracted, not being able to follow a schedule, or learning in a way that isn’t suited to your strengths.

These issues can quickly lead to the dissolution of your well-laid plans for studying data science and can also lead to the process taking much longer than is necessary. Therefore, you must identify your learning weaknesses and prepare a plan to help you avoid them at all costs.

Some ideas for avoiding learning weaknesses include putting your cellphone in another room, using a website blocker, planning a daily study schedule ahead of time and displaying it in a prominent location, and conducting research on different learning styles.

Here is a great resource to help you learn more about learning styles and how to improve the independent learning process:

While it’s rare to come across a data science enthusiast who can only type using their index fingers, it’s important to note that learning how to type properly can mean a world of difference in the speed with which you can complete your learning.

Learning how to type properly not only involves learning how to type without looking and at a quicker rate but also learning the shortcuts that can save you keystrokes when writing notes or code.

This may sound like a trivial task, but it’s been found that increasing your typing speed by 20% can save you up to 35 minutes a day which equals 213 hours per year saved, simply by typing faster.

Here are some resources to help you learn how to type faster and use keyboard shortcuts:

Self-studying data science is a tricky task that requires discipline, perseverance, and mental fortitude. Productivity slumps can happen when your discipline isn’t running at 100% every day which can result in diminishing the speed with which you complete your data science studies.

Because we’re all humans and can’t be expected to run at peak productivity every day, it’s important to find a workaround for this problem. The solution? Automate your study schedule so you don’t waste time trying to figure out what you will study each day.

This can be done by writing a simple Python script that presents you with a to-do list of what you will be studying that day. While this requires a little advanced planning and a few hours of coding, it can save you precious minutes every day by presenting to you exactly what you need to learn that day. Think about it: if you spend 20–30 minutes each day trying to decide what to learn, you could end up losing 2–3 hours every week that could otherwise be spent studying.

Here is a great resource to get you started with automating your daily routine that can be expanded to automating your study schedule :

Nothing quite energizes and inspires the brain like reading literature on what you are passionate about.

It can get monotonous working through a study plan with no outside input. Therefore, it’s vital to keep the spark for data science alive by reading articles that discuss the latest ideas in the field.

Spending 30 minutes every day reading can give your brain the respite it needs to be more productive once you get back to studying, and can also spark ideas or trigger conceptual understandings. Being up to date on the latest in data science is also a valuable skill to maintain when it comes time to network, apply for jobs, or even write your own data science content.

Here are a couple of resources that provide daily data science content:

#3 is a game-changer that will keep you out of coding tutorial purgatory

Much like scaling a mountain, there are no shortcuts to the summit when learning data science.

Data science is an all-encompassing discipline that takes the best of programming, mathematics, and design to produce stories from data that help the world make decisions. Unfortunately, this also means that anyone looking to study it is in for a lot of hard work and long months to master the skills needed to fulfill one of these coveted jobs.

Luckily, there are some small tricks you can use that will help smooth the path ahead of you. While they won’t be the elusive non-existent chairlift that will take you straight to the summit, they will help you learn more efficiently and may shave time off your learning experience.

As discussed above, data science is an all-encompassing discipline that involves programming, mathematics, hard-core computing, design, industry knowledge, and storytelling.

Because there is so much to learn in a short period, it’s important to know exactly what you need to learn before getting started.

This means taking a deep dive into the type of data science career you want and looking at all of the required skill sets. Data science careers vary greatly in daily tasks, technical requirements, and required knowledge. For example, a data scientist working in academia will likely have a greater requirement for differential equations and number theory compared to a data analyst working for a small company who only needs to know arithmetic, algebra, and statistics.

It’s important at this time, to be honest with yourself in determining what kind of data science job you want. This will greatly impact what you need to learn and will help you with the following tip in setting up your learning plan. While the specifics for each job will vary depending on seniority and industry (researching specific job requirements on Indeed or LinkedIn is a great idea to get a clearer picture), the jobs in data science can generally be broken down into three general categories: data scientists, data analysts, and data engineers.

Here’s a resource to help you decide what kind of career in data science you’re looking for:

Here are some resources to help you decide what you need to learn for each type of job in data science:

What Skills Are Important for a Data Engineering Role at FAANG

Most learning plans you see on the internet are deep, extravagant roadmaps that involve a huge breadth and depth of knowledge that could only be accomplished through years of study.

What these plans fail to recognize is that you only need enough knowledge to land a job — the rest of the skills can be learned afterward. This means that you only need to learn enough to be able to carry out the daily tasks of a data scientist and the true value of your contribution to a company can be built up over time. While everyone would love to make a massive impact from day one, it’s more likely that you will grow your impact over months of high-quality work and learning on the job.

Therefore, you need to build a learning plan that only gives you as much knowledge as you need to get started — no more and no less.

This can be done by looking at existing learning plans available on the internet and trimming the fat. By reducing what you need to learn to the bare minimum, you reduce the time required to break into data science and you free up more time for you to master the exact skills you will need.

The key to properly learning anything in data science is to mix theoretical and practical learning.

While it sounds like an unnecessary extension of the learning process, backing up your theoretical knowledge with hands-on experience will help solidify your learning and will open the doors for more efficient knowledge retention.

The trick to mapping your theoretical knowledge to practical knowledge is to begin working on projects from day one. Each project doesn’t have to be life-changing, but it should incorporate something new that you have learned.

For example, on day one you learn the theoretical knowledge behind writing code in Python. Your project for that day could be to write code that prints “Hello World” to the terminal. On day two, you learn how to calculate the mean of a sample. Your project for that day could be to enter a list of numbers into your Python program and calculate their mean. On day three, you learn the theory behind data visualization. Your project for that day would be to visualize your list of numbers, along with its mean.

While a silly example, it gives you a foundation on which to apply your theoretical knowledge to practical problems. By the end of your learning experience, you may find that your program from day one that prints “Hello World” to the console is now giving you a full analysis of data you scraped from the internet. The key is to keep advancing your project (or projects) every day and pushing yourself to incorporate your new knowledge.

If there’s one thing that self-study reveals it’s that we all have learning weaknesses that keep us from reaching our maximum learning potential.

Learning weaknesses can include simple things like being easily distracted, not being able to follow a schedule, or learning in a way that isn’t suited to your strengths.

These issues can quickly lead to the dissolution of your well-laid plans for studying data science and can also lead to the process taking much longer than is necessary. Therefore, you must identify your learning weaknesses and prepare a plan to help you avoid them at all costs.

Some ideas for avoiding learning weaknesses include putting your cellphone in another room, using a website blocker, planning a daily study schedule ahead of time and displaying it in a prominent location, and conducting research on different learning styles.

Here is a great resource to help you learn more about learning styles and how to improve the independent learning process:

While it’s rare to come across a data science enthusiast who can only type using their index fingers, it’s important to note that learning how to type properly can mean a world of difference in the speed with which you can complete your learning.

Learning how to type properly not only involves learning how to type without looking and at a quicker rate but also learning the shortcuts that can save you keystrokes when writing notes or code.

This may sound like a trivial task, but it’s been found that increasing your typing speed by 20% can save you up to 35 minutes a day which equals 213 hours per year saved, simply by typing faster.

Here are some resources to help you learn how to type faster and use keyboard shortcuts:

Self-studying data science is a tricky task that requires discipline, perseverance, and mental fortitude. Productivity slumps can happen when your discipline isn’t running at 100% every day which can result in diminishing the speed with which you complete your data science studies.

Because we’re all humans and can’t be expected to run at peak productivity every day, it’s important to find a workaround for this problem. The solution? Automate your study schedule so you don’t waste time trying to figure out what you will study each day.

This can be done by writing a simple Python script that presents you with a to-do list of what you will be studying that day. While this requires a little advanced planning and a few hours of coding, it can save you precious minutes every day by presenting to you exactly what you need to learn that day. Think about it: if you spend 20–30 minutes each day trying to decide what to learn, you could end up losing 2–3 hours every week that could otherwise be spent studying.

Here is a great resource to get you started with automating your daily routine that can be expanded to automating your study schedule :

Nothing quite energizes and inspires the brain like reading literature on what you are passionate about.

It can get monotonous working through a study plan with no outside input. Therefore, it’s vital to keep the spark for data science alive by reading articles that discuss the latest ideas in the field.

Spending 30 minutes every day reading can give your brain the respite it needs to be more productive once you get back to studying, and can also spark ideas or trigger conceptual understandings. Being up to date on the latest in data science is also a valuable skill to maintain when it comes time to network, apply for jobs, or even write your own data science content.

Here are a couple of resources that provide daily data science content: