Natural language processing is perhaps the most talked-about subfield of data science. It’s interesting, it’s promising, and it can transform the way we see technology today. Not just technology, but it can also transform the way we perceive human languages.

Natural language processing has been gaining too much attention and traction from both research and industry because it is a combination between human languages and technology. Ever since computers were first created, people have dreamt about creating computer programs that can comprehend human languages.

The advances in machine learning and artificial intelligence fields have driven the appearance and continuous interest in natural language processing. This interest will only grow bigger, especially now that we can see how natural language processing could make our lives easier. …

Becoming a well-distinguished data scientist is often a bumpy road. Because data science is an interdisciplinary field, you need to know many fields to master it and fully understand its building blocks.

You need to know some maths, statistics, the basis of business models, programming, visualization, and science communications skills. You will also need to know some core technical skills, such as dealing with docker images, being familiar with Git, and completely comfortable with databases.

I can understand how this can be overwhelming for new people joining the field. I won’t deny that going down the road to becoming a data scientist can be quite much, but being a data scientist is one of the most fulfilling, intelligent challenging jobs out there. …

Writing documentation is probably one of the tasks programmers dread the most. I mean, we are programmers and not writers anyway. It is well-known that programmers are great and writing code but not so great at explaining it's thought process.

I believe that’s why we hate writing documentation so much because then, we need to explain our thought process in words for others to understand. And that’s never a fun task.

Regardless of that, all programmers know the importance of good-written documentation and how essential it is to any coding project's success, whether it was intended for open-source or as a provide project within a team. …

So you finished your project, you got some good data, you processed it, cleaned it, trained your model, applied it to your data, and got amazing results. That’s it.

Not really…

Often, a software is developed for others to use, so once the programmer or data scientist finishes building the project, they will need to do the task that most of us hate…

Documenting the code.

In software engineering, in general, writing documentation refers to the process where the programmer of the main developer of the code writes a script explaining in detail what the code does its goal and how it achieves that. …

It’s the start of a new year, new adventures, new events, and maybe a new career?

Here we are in 2021; you might be sitting there, unhappy with your current career and want to change it; you might be unsatisfied with the work you’ve been doing, with the financial aspects of your current job, or simply don’t feel challenged enough. Or, you’re maybe a student trying to decide what to do with your future, or you might be either.

Regardless of why or how you get here, you’re here, and you’re considering a career change or a career start. You look up online for jobs that pay well, are intellectually challenging and fulfilling. …

We all — probably — started our data science or programming journey using some GUI. A tool or an app with everything built up, and we just have to write code and click on some button to compile and run it. Voila, the results show up, and we are done.

Although there is absolutely nothing wrong with GUIs, as you advance in your career, you will need a better, faster, and more efficient way to control your computer and get the job done. That’s using shell commands — command prompt in Windows — to control your workstation. …

As developers or data scientists, we often go through many stages, from getting an idea to reaching a valid, working implementation of it. We need to design/ validate an algorithm, apply it to the problem at hand, and then test it for various input datasets.

In the initial state of solving a problem, it helps a lot if we could eliminate the hassle of having to be bound by the syntax rules of a specific programming language when we are designing or validating an algorithm. …

During the lifespan of our lives, we go through various learning and self-developing. The start of any journey is always challenging; you don’t know the right way; you’re unsure what to expect or how to overcome the bumps in the road.

Joining a field like data science is both intimidating and rewarding. It is intimidating because the field is vast and has many options for what you could do. But as a beginner, you don’t know what you like, so you have to feel everything before you settle on a topic.

Once you choose a topic and start mastering it, the learning process goes from terrifying to enjoyable. Navigating your way in a new field is a tough task if you’re a newbie. You don’t have enough knowledge to make decisions, and you don’t fully comprehend what is out there. …