DEV Community: zyazyaG

4 Things Your Data Science Project Might Be Missing

zyazyaG — Sun, 30 Jan 2022 17:18:22 +0000

Throughout my yet short journey in the Data Science field, I’ve studied many projects and works of other beginners and professionals. Mainly, this is how I improve my skills and get directions on where I need to strengthen my knowledge. Looking at those works I’ve noticed 4 shortcomings related to project structure of beginner data scientists that I want to mention today.

1. No Comments

Most of the time when we look at someone else’s code, we have that weird facial expression called confusion.

The lack of well-written comments is the main weakness of the project. It is important not to only explain what you are doing, but also why are you doing that. Why?! If you want someone to look at your work and not skip over it, you need to make sure that you clearly explained the thought flow and process of your project. For example, if you are utilizing SMOTE in your project, explain that the reason behind it is data imbalance.

2. No Visualizations

If I ask you to close your eyes and list all continents, the first thing that your brain would do is visualize the map. The human brain tends to better comprehend and analyze visual information. Thus, while working on a project, it is essential to show your findings or analysis through graphs and plots. That way you’ll make sure that the person looking at your work will be more engaged and interested. And maybe even see things you might have missed.

3. No Virtual Environment

Imagine that someone liked your project and wanted to take a better look at it or test it on his/her own. So, they download it…but wait, for some reason your project is not working on their local machine. Most of the time that happens because some of the libraries or packages you’ve used in your project are not on their system. The best way to ensure that strangers won’t have problems utilizing your work is to create a virtual environment for your project. This environment will have all the packages and dependencies of your work that would ease the interaction of others with your project.

4. No User-Defined Functions

The last thing I wanted to mention is the lack of user-defined functions and classes. Most of the time the code snippets in Data Science projects repeat themselves: when you perform data cleaning, EDA, feature engineering, and modeling. Why not make your life and the life of others easier?! If you noted that you type the same code over and over, try to build a reusable function or class for it. In that case, you’ll save your time and make the code look more organized and easier to understand.

Keep in mind those 4 things, like I do when working on my projects. I hope you'll find those tips as useful as I do.

Quick look to R vs. Python

zyazyaG — Sun, 23 Jan 2022 23:42:24 +0000

Since the beginning of my journey in the Data Science field, I’ve programmed my projects with help of Python programming language. But I was aware that another popular language related to Data Science is R. Recently, I’ve started my research on what are the main differences, similarities, and main uses of those two languages. And here is a quick look at what I’ve learned.

Overview of languages

R

R is a statistical language. It is used for statistical software development and data analysis.
R has many libraries for creating dynamic and interactive graphs.
The development of R began as a research project by New Zealanders Robert Gentleman and Ross Ihak. In 1993, the first binary versions of R were published in Statlib, an archive of statistical software and datasets.
R has its own open-source CRAN (Comprehensive R Archive Network) repository. There are over 16,000 packages available in CRAN for analytical tasks.
R is a command-line language, but there are several IDEs that provide an interactive GUI.

Python

Python is a multifunctional language that can be used in web development, software development, system scripting.
It is among the ten most commonly used programming languages.
Python was developed by the Dutch programmer Guido van Rossum in 1991. The name was inspired by the TV show Monty Python's Flying Circus.
The language is designed to be easy to read and has similarities to English.
Python uses dynamic typing (a variable is bound to a type at the time a value is assigned).
Previously, Python lacked libraries for data analysis and ML. Now, it provides APIs for creating artificial intelligence.
Most data processing tasks can be solved using five Python libraries: Numpy, Pandas, Scipy, Scikit-learn, and Seaborn.
It is suitable for those who want to use the results of calculations in an application or website.

What is the difference?

R was created for statistical tasks and data analysis, while Python is more versatile.
R is great for complex visualizations, as opposed to Python.
R is difficult to integrate into a production process, while Python can easily become part of the product.
Python codes are easier to maintain and more robust than similar algorithms in R.

Benefits of each

Pros of Python

It is common to hear that it is easier for beginners to learn Python. The R language has a rather steep "learning curve" because statisticians created it for themselves. Python has a simpler syntax.
Compared to R logic, the coding syntax in Python is more in line with how people think. Therefore, Python is easier to "translate" into other programming languages.

Pros R

The language is command-line oriented, but many use the RStudio or R commander environments. These IDEs have data editors, debugging support, and a graphics window. Python is partially closing this functionality with Eclipse and Visual Studio.
R is designed specifically for data visualization. Rendering in Python is more confusing, and there are fewer libraries to choose from - only a few dozen.

Which language to choose?

To understand what language you need, first, decide what problems you want to solve, how the results should be presented, and do you plan to deploy your model.

If you are planning to work with massive amounts of data, build deep learning models or perform non-statistical tasks – Python would be a better option. And if you are going to build statistical models and require better visualizations, then R would be just what you need.

My Fight with Procrastination

zyazyaG — Mon, 11 Jan 2021 02:04:18 +0000

After the beginning of my Phase 1 Project week I was so excited to start coding. I was full of motivation and energy. But as soon as I hit the first serious bug that I couldn’t fix my desire to code started to fade away…and soon enough I’ve started to procrastinate.

In this post I will share the tricks I used to make myself work on the project and code.

First of all, procrastination, in psychology, means a person's tendency to constantly put things off for later. In a mild form, it does not harm and is even considered normal, but as soon as procrastination worsens, it begins to harm (which was my case).

Most important part was to realize that I’ve actually started to procrastinate. I tried to avoid my laptop, Slack app, and even felt like avoiding my instructors. But when I understood that the deadline is close and I haven’t achieved anything, I started to move. Thus, I highly recommend paying attention to what you do when you have a problem with code.

After starting to code, I still couldn’t make myself to concentrate and actually code the project. It is then I started to think about motivation. I remembered all the reasons why this project is important for me and where it will take me. If work for you is a tool to achieve your goal, remember this more often.

My motivation gave me a kick start, but it didn’t last for long because the bugs were all over me. I’ve started to self-deprecate and scold. And my mom, who knows nothing about coding asked me to show her what I had done. After I’ve done so, she started to praise what I’ve done already. It is then, I realized that even though I have gaps, there is still a lot of “gain”. So, I felt superior. It is very important to never scold yourself, but rather praise yourself for what you’ve already done. You may even get yourself a gift or something.

While coding, I noticed that large amount of my time is consumed by social media. I love Instagram and funny memes, but they distracted me so much. So, I’ve turned off everything. IT DIDN’T WORK! I became obsessed with feeling that I’ve missed some important message, post or email and lost my concentration at all. What helped me is using social media as a reward for myself. After an hour of working on project, I gave myself 5 minutes break to scroll the Instagram. Better than nothing… So, don't try to fight the social media as your enemy. Better learn to reward yourself with it.

A week after, I have finished my project and submitted it! The feeling of joy almost made me think I am superior. I will be using all those simple small methods in my future coming studies and projects.

Focus on your goals, and start acting now, creating a future you will never regret.