DEV Community: jpakele

Staying Motivated

jpakele — Sun, 23 May 2021 01:18:35 +0000

Perusing a goal relentlessly to achieve an ultimate goal. It sounds like a nice concept. Setting an ultimate goal and working your way towards something amazing, something that will set us on improve our life significantly and immediately. And it's that end goal that we all work so hard to get to...But how does one stay working towards that goal? What does it mean to be driven? What does it mean to stay driven?

What being "driven" means to me

When I think of what it means to be "driven", I think of the rush of accomplishing a goal. The feeling I chase is the feeling of making it through the finish and being able to look back and say "I've made it. I did something awesome." Knowing that the feeling is coming is what moves me forward. But that feeling fades over time and if I'm not careful about making realistic goals, I can falter and fail at achieving those goals.

What staying "driven" means to me

Pulling myself up from the cliffhanger I left the last section on, I have lost motivation to do something more than enough times to know what I've done wrong and right. "Being driven" and "staying driven" are related concepts, but separate. Staying driven - staying motivated to work towards a goal(s) - means to be conscious of what I'm doing and how it affect how I make my goal a reality. Being mindful of that every moment and excited to work more. The way I do this is by visualizing what I want.

How do I stay "driven?"

I have a cork board in my room that I hang printed pictures of my current goal and my end goal on. "Seeing is believing" is the idea and keeping a constant reminder gives me a small boost in excitement to keep working. Now, it doesn't have to be a cork board and printed pictures. It can be a screen saver on a laptop or wallpaper on a phone screen. The idea here is to just keep a image (not words) of what you want in someplace you'll see it often. A sentence or phrase doesn't work for me because I don't take time to read it when I'm glancing around the room or looking through my phone apps. A picture is easily understood in under a second.

Why do I have two goals?

Previously, I mentioned that I had a current goal and an end goal. That's because often times my goals are lofty. "Buy a house", "get a tesla car", "graduate from school", "beat Resident Evil: Village on the hardest difficulty in under 3 hours." Lofty goal that will take lots of time to accomplish.

Time is the enemy here. The longer a task takes, the less it feels like I'm making progress. When something takes longer, it skews the perception of how obtainable the goal really is. I've often heard people say that what I should instead do is make a series of smaller, easier to obtain goals. I reject that reality and substitute my own!

Do. Both.

I have an end goal and a series of easier to obtain goals that lead up to the end goal. When you have a dream to make come true the last thing you want to hear is someone tell you "That's unrealistic. Set smaller goals for yourself." What an insult! How you feel is how you feel, no one can take that away from you except you. So own what you want, and make small achievements leading up to what you want. And DON'T forget to reward yourself for making it as far as you have.

For example, let's say I want to get through school. In my case, I have a certification program that I'm enrolled in. So my end goal is "Graduate." Perfect. Now, what are my steps up to that? Well, I have 5 phases in my program. I set a reward for each phase completion and lay out what needs to be done in each phase. I take each phase one step at a time and take time to be happy about the minor accomplishments. I have pictures of graduation caps on my cork board as well as the reward for when I accomplish that goal.

Summarizing my thoughts

When you want to be driven and stay driven, it all starts from the frame work of the goals. Create a goal, whatever you currently want, and visualize it in a way that you will see it constantly. Next, make small goals that lead up to it that wont take long to make come true. Remember, the longer it takes, the more impossible it'll feel. After every micro goal achieved take the time to be happy about what you have do so far and move on.

It's important to have something to congratulate often because it keeps the mind stimulated to want more. It changes the mind's view of work away from something you have to do into something you get to do. You'll have something to talk about and be excited for. And believe me when I tell you that your friends and family love to see you excited about something.

My Experience with Avoiding Computer Fatigue

jpakele — Sun, 21 Mar 2021 19:45:59 +0000

What is special about "Computer Fatigue?"

What I mean by "Computer Fatigue" is the exhaustion - mentally and physically - from having too long of an interaction with working from your computer. In the wake of COVID-19 this may be more applicable than ever and I can only surmise it will only affect more and more people. You may think of working at a computer as "the cushy office job" but there's a very real, but very similar kind of exhaustion (when compared to, say, a laborer) that comes from endless hours of screen time. Body strain and degradation, mental tiredness, unhealthy sleeping. These are all things that are very real and just as easy to avoid as lifting with your legs or looking both ways before crossing the street.

A healthy work environment

This my be obvious to some but it certainly was not for me. As a gamer, I can sometimes...neglect the sanctity that is my desk space. A plate of food, a bag of chips, figurines off to the side, cables every which-way. All of this takes up physical space as well as headspace. And while I may not need the ambient feeling of cleanliness to make big plays as Mercy, healing McCree from around a corner for the 40th time it's definitely required for me to feel like I'm in a working environment. So, removing all my decorative items and keeping my things down to a pleasant cup of coffee was what I felt like I needed to get my head in the working mood.

Computer setup

It's important to understand that, while it's true that being able to see more of your work at a glance is important, you can efficiently work with only one screen. The trick, I found, that works for me is keeping the program I'm working in (for instance, a Jupyter notebook) take up half the screen and off set it to the side of the screen that is most comfortable. This is the bulk of my work so it take up the bulk of my screen. Then, whatever else I need open on the rest of the screen will share what's left. This may seem obvious but I say this for those who may default to minimizing their programs one at a time and maximizing those as needed. Again, the more information you have at a glance, the less extra work you'll need to spend time on. The little things add up.

Keep your body healthy

This is the single most important part about setting yourself up for success. A clean mind and energized body makes for a productive time. Make it a point to get at least 8 hours of sleep. There is no "I function better on 6 hours of sleep." That's just statistically not true. I struggled with this, dearly. In addition...eat well. A healthy diet has everything to do with how much energy you have. I'm not saying give up the things you love to eat. Eat a pizza if you wanna. Pizza is amazing. But don't let that be your only source of energy. Balance the good foods with the bad foods.

And lastly, keeping your body physically unstrained is extremely important. Sit up straight. Don't hurt your back because of sitting. If you can't sit then you can't work. Not just that but take breaks. Stand up, lay on the couch a bit. Turn off for a little while. If you're an awkward typer then maybe spend some time developing good typing habit with a basic "learn to type" game. Screw it! Why not have some fun with it?

Take care of your eyes

This goes hand in hand with the above segment but I found this contributes the most to my fatigue...Take. Care. Of. Your. Eyes. The bright screen can be a lot to handle staring at all day but there are ways that I've found to mitigate this to a major extent and I do these every day.

Open the blinds or turn on a light: Don't let your screen light be the only light. The stark contrast between your dark room and your bright screen can be hard on your eyes.
Take regular breaks: Look at something else for a while. It can be as simple as getting a snack for a bit and returning after but you may realize after a while that your eyes are dry and you probably haven't blinked as often as maybe should.
Wear some blue-light blocking glasses: Wouldn't it be great to avoid the eye strain all together? That's what blue light eliminating glasses do for me. This is totally dependent on the person as not everyone I know experiences the benefits of blue-light blocking glasses. They may even be out of your budget, especially if you get them from your optometrist. However, I found they have been extremely worth it. At the end of a 12 hour computer binge I have noticed that I no longer feel "strain" from my eyes. The feeling has changed and the best way I can describe is "naturally tired."

My conclusion

Setting yourself up to not be too tired to work is just the same as any other job. From laborer to CEO, a healthy body, mind, and work environment is pivotal for success. You just simply can't do what you need to do if your body doesn't have the energy for it. Do what you have to do to get your battle station in order and be healthy about how you live.

My first project

jpakele — Sat, 23 Jan 2021 22:56:06 +0000

The Problem at Hand

Imagine a world where Microsoft wanted to get into the Movie Making business. They make PC and video games, separate industries from theater and movie. How would they even know where to start? What is the smartest first move? Enter: Data Science.

For the project, my goal in this fictional (though not believably realistic) scenario is to advise the executives at Microsoft one what a course of action might be. What's profitable? What's reasonable? What is the actionable information that they need?

The Data Set

There were 4 sets of data that came into use in this project, though not all of the data from each was used (as I'll talk more about later).

Box Office Mojo
IMDB
Rotten Tomatoes
TheMovieDB.org

These data set contain itemized information about published movies. Each data set contained various combinations of attributes about a specific movies (movie earning, release year, title, affiliated actors/actresses, etc.)

The Problem WITH the Data

All of these data sets came from different, separate sources. Each source had it's own way of organizing things and not all of the data between each other was the same. For instance the movie ID number between IMDB and RT are neither named the same way nor organized the same way.

Cleaning the Data

In order to gain a better understanding of the dataset as a whole, I felt it was necessary to eliminate as much of the data that was unnecessary as possible. Any information that wasn't directly about the movie genre's themselves, the overall/average rating of a movie, or pertained to the monetization of an individual movie was eliminated all together.

There were a lot of reason for keeping more data (Actors, writers, run time, release date, etc.) but in the context of having to convey this information to a corporate executive I felt that the two things someone in that position would care about the most were how well a movie could be received and how much money it could make them.

Methodology and Results

Methodology 1

At this point, I had a strong and focused set of goals for approaching the problem at hand. From here, I took the data sets that were still left, renamed similar columns and combined them to get one consolidated data set. Combining can be a bit tricky, especially with titles that look different to Python but are the same movie (such as such as "Star Wars the Last Jedi" vs "Star Wars: The Last Jedi"). Likewise, movies that had the same name but were in fact different from each other would show up as repeated rows with data exactly the same as one another. To solve this particular issue I had combined the lists contingent on two columns. Those were "title" and "release date".

From there, I organized the list in order of highest domestic gross to lowest. This is where I took 3 samples of the data. Here I grab counts of every movie title's genre(s) for the top 100 movies, 1,000 movies, and every movie whos gross amount domestically was at least 10,000,000 USD.

The results of this were:

The movies that see the most box office success, both as one of the most common and as one of the highest earners, is 'Action, Animation, & Comedy'.
'Action, Adventure, & Sci-Fi' is the second most common of the highest earners.
'Action, Adventure, & Sci-Fi' is the single highest earner genre combination.

Methodology 2

Similarly to the first methodology, I had sorted the same list by the most highly rated to the lowest. However, from the start I had removed all movie that were unable to receive at least 10,000 votes. I saw this as anything less had failed to become relevantly popular enough and should be seen as a non-example.

Then I took my 3 samples, a value count of every single-movie's genre(s) from the top 100 from this list, the top 1,000 from this list, and all movies that received a 7.0/10 average rating or higher.

The results were:

The data shows that purely 'Drama' movies have the highest ratings as well as being the most common genre type.
The second most common genre is 'Comedy & Drama' in combination.

Methodology 3

This was significantly different as now I needed to find a correlation between the two of these findings. By creating a scatter plot from these I had discerned that there seemed to be an upward trend in data between the total domestic gross and the average rating of a movie. But it was hard to put a pin in why this was true. A higher rated movie meant higher earning but was there any data that looked similar to this?

Yes! Production budget vs domestic gross! It seems like the higher the budget of a movie is, the higher it tended to rate.

Actionable Info

The gist of my recommendations boiled down to making 'Drama' and 'Drama, Comedy' movies are safe for establishing a good foundation of well received movies to earn a kind of "brand trust". Use these to test the water with smaller budgets and increasing said budget based on how well each proceeding movie did. Eventually the goal is to make an 'Action, Adventure, Sci-Fi' movie with a large budget to garner a larger return on investments.

I had also made the recommendation that since Microsoft and Xbox are the owners of very, large well beloved intellectual properties in the form of video games and video game franchise they'd be able to save effort on story crafting by making a video game into a movie. They wouldn't have to go through the hassle of purchasing rights to anything new and would be able to capitalize on the nostalgia factor that these games instill in audiences.

Why Data Science?

jpakele — Fri, 20 Nov 2020 23:20:56 +0000

Data Science can seem like a nebulous concept and an omniscient presence in our day to day lives. It's a buzzword that the Average Jane could recognize but couldn't explain it's meaning. Defining Data Science in it of itself is a topic all on it's own. So why would anyone choose Data Science as their career path without knowing what it truly means to be a Data Scientist?

What is Data Science?

Data Science is the intake of information, cleaning, scrubbing, rearranging, and formatting it into a usable structure for answering questions about a data set. When given information about a subject, a data scientist is able to take that information, transform it, and present it in some meaningful way to satisfy aa given question.

Consider this: Data Scientists exist wherever data exists. Always. On a marketing team analyzing trends and making prediction for the future, taking in crime statistics for officers to be aware of which places are statistically "higher risk" and how to find out why that it, or as simple as working for Walmart to predict what items will sell well and how much to stock based on past trends of similar items. In the most general sense, that is what a data scientist does.

Why Data Science is important to me

I'm an avid gamer. I like to spend my free time playing a videogame, backwards and forwards, exploring every detail and making multiply play throughs of a single game. In a strategy game, there can be a lot of statistics to keep in mind. More often then not, the numbers are overwhelming. So how does one keep track of every bit of information? The answer is: you don't. Not efficiently, anyways

Players, especially true in my case, tend to take to the internet for information and will scrub through webpage after webpage to find what they need. It's a tedious and long process but there isn't a better way to do it? Except that this is Data! We could take in the information we need, run it into an algorithm and tell it to give us only what we need? And indeed, I've already done so with the statistics of a game titled Fire Emblem: Three Houses and posted it to reddit for use users to play test and give feed back on.

The purpose of this project was simply to make an easy to use, all-in-one information sheet to save players time and energy. Information documents like this are examples of how a data scientist might reformat and cleanup data into something more useable. Writing a series of algorithms to automatically comb the information for only the desired results is just the tip of the iceberg.

Why Data Science is important to YOU?

As I've said, Data Scientist exist anywhere data does, including in your purchases. You've probably noticed that when you're window shopping on Amazon it recommends other items base on "other things people bought" and what you've looked at in the past. Now think about if you've ever bought something that was recommended to you. This is another example of Data Science in play, serving to save the end user time and effort. However, this takes things a step further in adding the extra benefit of prediction based on past examples.

That's all well and good but those are first-world solutions to a first-world problem that nobody has. What about the things that matter most like saving lives? Well, doctors and hospitals rely on data science as well. Objective and transparent data is the most valuable thing when it comes to making decision on how to move forward with new medicine. What were the effect of Medicine X patient groups 1 - 12? What were the side effects? How long where the effects? What was the mean, median, and mode of the pain ratings before and after Medicine X? These questions are critical and absolute require answers. It's a data scientist's job and privilege to give the objective truth to those who need it.

Closing Thoughts

The work data scientists do is always meant to help improve lives or, at the very least, give someone else the tools they need to do something more and improve quality of lives. This will always be relevant. This will always affect you - the reader - as long as we all live in a technical world. If there is anything I hope you take from reading this blog, may it be this: Never underestimate just how much the world revolves around math and data. It's in literally everything we do.