The single biggest mistake a new programmer can make is to get stuck in coding tutorial purgatory.

I guarantee you that nearly every single person who has set the goal for themselves of learning how to code has ended up at one point or another in coding tutorial purgatory.

It happens innocently enough in the beginning.

You begin by following a few tutorials until you get comfortable with the process of coding. Once you get some confidence, you feel inspired to take on your own project. …

With so much to learn in the way of programming, data analysis, machine learning, artificial intelligence, mathematics, and all of the many other components of data science, it’s fair to say that the learning of concepts becomes an arduous affair when becoming a data scientist.

With data scientists coming from a multitude of backgrounds, many of them not computer science-based, it’s completely understandable that some computer science principles are passed over in favor of getting to the good stuff: completing data analyses.

One of those concepts is object-oriented programming (OOP).

When you ask current data scientists for their opinion on OOP, you’ll probably come back with a mixed bag of answers. In some cases, OOP can be incredibly instrumental in reducing the complexity and time it takes to complete an analysis. In others, OOP can result in having more code than you need or even know what to do with. …

The internet is currently flooded with articles about the “Top 10 Most In-Demand Technologies To Know To Get a Job in 2021” that suggest you should know machine learning, artificial intelligence, cybersecurity, blockchain, virtual reality, full-stack development, and more just to get a job.

In essence, these types of articles are just plain discouraging for people who are looking to jump into the field of software development because they insist that you must know each of the specified technologies to be a relevant hire. …

At the beginning of a new year, people often set lofty goals for themselves, somewhere along the lines of becoming something or accomplishing something by the end of the year.

However, by the end of the first few weeks of January, many of us have already shelved our goals to wait for the beginning of the next year. Is it because of a lack of motivation? Is it because of a lack of time to put towards accomplishing the goal? The truth is that it’s probably neither.

Coding, like going to the gym, eating healthy, reading 52 books in a year, or any other typical new year goal, is (in the simplest form) a habit that must be nurtured and maintained to get results. One of the reasons why we are so bound to fail with our resolutions is because we fail to make them a habit. Not only that, but many of us unintentionally handicap ourselves with unsustainable habits that don’t allow us to see results. …

Whether you’re a new graduate, someone looking for a career change, or a cat similar to the one above, the data science field is full of jobs that tick nearly every box on the modern worker’s checklist. Working in data science gives you the opportunity to have job security, a high-paying salary with room for advancement, and the ability to work from anywhere in the world. Basically, working in data science is a no-brainer for those interested.

However, during the dreaded job search, many of us run into a situation similar to this one:

At the end of 2020, right around the time when everyone was making new year’s resolutions, I decided that my goal for 2021 was to begin my journey into learning data science.

Part of the learning curriculum I developed focused on the importance of completing projects as a way to further my knowledge and to begin applying the skills I was learning.

That brings us to this article: mapping out the 7 data science projects I plan on completing this year, and how those projects will strengthen my skills in specific areas. I wanted to select projects that focus on subjects that I’m interested in, and I also selected projects that came with source code that I could reference if I got stuck. Furthermore, I also wanted to pick projects that were a bit more unique than the regular ones that are regularly shared. …

As long as you’re willing to put in the effort, it doesn’t matter if you have the aptitude or not — you can learn to do anything.

However, when it comes to learning how to code, you often face times when it seems that no matter the effort you put in, you just can’t seem to grasp a concept or understand how a piece of code works. …

What do data science and writing have in common?

Besides the obvious (that we’re all looking for more hours in the day to get better at both), data science and writing also hold the title for being the two skills that can turn us into well-rounded individuals in both skills if improved in tandem.

Data scientists are commonly referred to as “storytellers” because of the way that they can turn massive data sets into beautiful visualizations that tell stories to the masses. …