How To “Ultralearn” Data Science: removing distractions and finding focus, Part 2

By Ben thecoder, Developer, Writer, Machine Learning and AI enthusiast.

Photo by Jakob Owens on Unsplash.

 

Distractions

In part one, you were given an introduction to ultralearning and an effective strategy for implementing metalearning. With that, you can begin your voyage into the journey of ultralearning data science.

But before you get too excited, you have to first understand that the world is full of distractions. We live in a time where our attention is cheaply sold and the concept of the attention economy is treating our attention as a valuable currency.

“Attention is a resource — a person has only so much of it.” — Matthew Crawford

That said, this article will mainly be focusing on the topic of, you guessed it, focus. Modern life is full of distractions, from constant notifications on your phone to the inclination to binge YouTube and Netflix, our attention span has decreased substantially.

These distractions can be detrimental to a person’s wellbeing and aptness to work productively. Finding your focus in this distraction-laden and attention-sapping world is akin to finding a needle in a haystack.

However, there are simple techniques for you to overcome these obstacles to finding your focus, and reach your ultralearning goals!

 

4 Simple Mental Strategies to Defend Against Distractions

1. Momentum

The initial challenge to finding your focus is most definitely getting focused in the first place.

There are a lot of ways to do this, as we all have our ways to trick ourselves to sit down and start grinding on a personal project or writing an article.

One example of this is setting a timer for five minutes. First, you promise yourself that you can stop working after those five minutes. This impetus provided by the promise that you stop in a short amount of time will usually keep a person going and continue working.

This strategy of tricking yourself is parallel to the prominent Pomodoro technique: set a timer for 25 minutes and work without any distractions whatsoever, and without stopping. When the timer goes off, you take a five-minute break. Then, go back to another 25 minutes after that.

This self-created sense of urgency and rewarding oneself with breaks in between focused work is particularly useful and is proven to be very productive. As the amount of focused work increases, the duration of breaks will also increase proportionally (five minutes to 10/15 minutes).

Conversely, some would prefer to continue after the 25 minutes of focused work as it has already given you the momentum to keep on working, and then only take a break after an hour.

This alternative is called the 52/17 technique. It has recently gained popularity and is even titled as the ideal productivity schedule. Thus, according to your preferences and ability, you can tweak this Pomodoro technique and start obtaining focus.

2. Sustain

After finding your focus, the next trial is sustaining it. This is one of the hardest parts of focused work as we humans have been conditioned by the internet and social media to have shorter attention spans.

These external interruptions can evaporate your concentration. To control your environment and not let it control you, you have to take the initiative to eliminate these distractions.

A few examples are:

1. Put your phone on airplane mode.
2. Switch off your Wi-Fi.
3. Turn on do not disturb mode.
4. Set your smartphone color to greyscale.
5. Switch off notifications.

Other tips to help you continue to focus include:

1. Have a special deep work area where you perform your best work. This helps to get your mind ready the next time as you want to be focused again.
2. If outside, choose a noisy coffee shop as the white noise helps you focus (or the library for those who prefer quietness).
3. Listen to classical music or any music you’re not familiar with.
4. Prepare food and have water beside you.
5. Find a partner to participate in a pair-programming data science project.

3. Interleaving

You might think that the challenges are over, and you have conquered the mountain of attaining focus. But the trial doesn’t end there.

Though you may have started building a logistic regression prediction model from scratch by following a step-by-step tutorial on YouTube, and you think you’re learning a lot, but it could be that you have slipped into autopilot mode.

Try building another model without any video tutorials at all, are you able to do it? If not, you were not fully engaged in the learning process, you were merely inputting new material, without retaining it.

To combat autopilot, apply interleaving — the deliberate alternation between materials and modes of learning.

Ideally, to ensure retention of new concepts, you have to interleave by tackling your data science project in short, regularly spaced sessions. Say, if you have 10 hours in your week to devote to learning about Python, aim for five two-hour sessions every week rather than one ten-hour session in one day.

In each session, plan what you will learn beforehand, such as data visualization on Monday, data cleaning on Tuesday, statistics and probability on Wednesday, etc.

Moreover, plan the resources you will use to learn and have a structure for learning, starting from knowledge building (books and articles), then only to application (writing code in an IDE).

To achieve this interleaving methodology effectively, one can utilize the concept of timeboxing — a term that comes from agile software development, in which a time box is a defined period during which a task must be accomplished.

With timeboxing, only a limited amount of time (timebox) is given to complete one task, after the time has ended, you move forward to the next one, even if the task before is not complete. This ensures productive work and prevents an individual from spending too much time on one particular task.

For example, you are having trouble debugging Python code for your project, and you’ve spent hours on Stack Overflow, Reddit, YouTube, and Discord, but you still have not solved it.

In the end, all you did the entire day was debugging, and you’ve completely neglected other plans. To prepare for situations like this (common in programming), give debugging code about 15–20 minutes.

This way, you can still continue on to other things instead of spending the entire day fixing your code.

4. Mental power peaks

The last step is to make the most of your deep work sessions by noting down your mental arousals — your level of energy and alertness.

We all have certain times where our ability to focus peaks. To engage in productive work, one has to note down their idiosyncratic times where they perform at their best.

This can be illustrated by the night-owls and early birds kinds of people, one is more creative and productive at midnight, while the other works best early in the morning, before the sun rises.

One way to find your mental power peaks is to journal your day before you sleep and takedown at which point of the day you were very effective. Do this for one month and look back at the times where you were productive.

Voila! You have your collection of time intervals where you are focused.

With that, you then allocate tasks according to your arousal levels.

High arousals generate intense but narrow focus — suitable for repetitive tasks, like finding resources for learning about data science and organizing them in applications such as Notion, Trello, GitHub, etc. and researching to solve business problems for a company

Low arousals generate a relaxed but wider type of focus, perfect for lateral thinking and forming new connections such as brainstorming a fun data science project or asking the right questions when solving a problem.

By matching your tasks to your arousal levels, you can perform simple tasks when your focus is aroused and complex tasks when it’s less aroused — for the optimum ultralearning results.

 

Action Plan

Honing your focus will secure your mental stamina to ultralearn data science.

By following these four steps:

1. Gain momentum with time-tracker apps and techniques such as the Pomodoro technique.
2. Sustain your focus by controlling your environment.
3. Split your learning hours into smaller sections, and plan each session using the concept of timeboxing.
4. Find your mental power peaks and allocate repetitive tasks to high arousal times and complex tasks to low arousal times.

Original. Reposted with permission.

 

Related:

• How To “Ultralearn” Data Science, Part 1
• Advice for New and Junior Data Scientists
• 5 Beginner Friendly Steps to Learn Machine Learning and Data Science with Python