Opinion

A Step-by-Step Process for Every Use Case

Photo by Green Chameleon on Unsplash [1].

Table of Contents

  1. Problem Statement
  2. Data Collection
  3. Exploratory Data Analysis
  4. Feature Engineering
  5. Model Comparison
  6. Results Discussion
  7. Summary
  8. References

Introduction

There is a certain trend in all technical processes, and data science is no exception. As you obtain more and more experience in any job, you start to notice a trend, which tends to make the job a little easier. The goal of this article is to make your data science job a little more streamlined because the process that I will outline below applies to every data science use case (or at least most), and for those that it is not 100% applicable…


A tutorial using Saturn Cloud’s Data Science platform

Photo by Mathew Schwartz on Unsplash [1].

Table of Contents

  1. Getting Started with PyTorch on Saturn Cloud
  2. Setting up LSTM Model Training
  3. Model Training and GPU Comparison
  4. Model Inference
  5. Final Thoughts
  6. References

Introduction

Disclaimer: I worked with Saturn Cloud to make this example.

A hurdle data scientists often face is waiting for a training process to finish. As a quick solution, some may limit the data they use, reduce the features they have, or use a model that is less complex. These are nice workarounds, however, it is not the best solution. The best way to train your model is how you intended without shortcuts. The real solution is to…


Opinion

… from a Technical Writer and Data Scientist.

Photo by Possessed Photography on Unsplash [1].

Table of Contents

  1. Formatting For the Eye
  2. Examples, Examples, Examples
  3. Article Length
  4. Summary
  5. References

Introduction

Technical writing is meant to bridge the gap between facts and entertainment, in my opinion. If it was simply technical only, then it would be a peer-reviewed paper proving a theory, for example. It was just an entertainment piece, then it would be a blog about the best movies in 2021. With that being said, there is a certain way of going about writing when you merge these two concepts of facts and entertainment.

It comes down to the goal of a technical article — which is usually…


A deep dive into data science employment levels and salaries by state

Photo by Jp Valery on Unsplash [1].

Table of Contents

  1. Employment Level
  2. State Salary Breakdown
  3. Summary
  4. References

Introduction

Data Science salaries vary from state to state, as well as industry to industry. The occupation, employment, and wage statistics discussed in this article were gathered from the U.S. Bureau of Labor Statistics [2]. Whether you are in the industry of Computer Systems Design and Related Services, or Management of Companies and Enterprises, you can expect a data science salary to be one of the most competitive salaries in the workforce. …


Opinion

A deep dive into the features and benefits of this popular Data Science platform.

Photo by Jan Kopřiva on Unsplash [1].

Table of Contents

  1. Python Training
  2. Video Tutorials
  3. Learning Webinars
  4. Community Events
  5. Community Forum
  6. Summary
  7. References

Introduction

Data scientists often use Anaconda Navigator [2], which houses popular and useful applications like JupyterLab, Jupyter Notebook, and RStudio. It is usually at these three applications where we tend to stop looking into this platform for other tools. As you navigate out of the home page or the home dashboard, you will see that there are the Environments, Learning, and Community sections. The latter two features are ones that we may miss, because they are not directly related to writing your own immediate code and working on…


Opinion

4 steps for successful freelance writing

Photo by 金 运 on Unsplash [1].

Table of Contents

  1. Advertise on LinkedIn
  2. Know Your Price
  3. Always Looking for the Next Best Platform
  4. Quality Quantity Quickly
  5. Final Thoughts
  6. References

Introduction

Now that working remotely has become commonplace, people are starting to look for more opportunities to learn how to make money from home, sometimes, in addition to their day job. There is a lot of freedom that comes from working on side projects where you are, for the most part, your own boss. Another benefit is, of course, making money remotely. …


Opinion

A comparison between real job descriptions

Photo by Possessed Photography on Unsplash [1].

Table of Contents

  1. Data Science
  2. Artificial Intelligence
  3. Summary
  4. References

Introduction

Data Science and Artificial Intelligence are oftentimes used interchangeably. Many have opinions on what defines each one. To settle the ongoing debate, I will not compare definitions, but instead, compare real job descriptions of data scientists and artificial intelligence engineers. This way, there are real examples to compare, so that you can know the difference in a professional setting. These differences may not be the end-all or represent all of data science or all of artificial intelligence. However, I think it is important to outline what actual recruiters and hiring managers have to…


Opinion

Similarities and differences between two popular roles

Photo by Fitore F on Unsplash [1].

Table of Contents

  1. Data Scientists
  2. Machine Learning Scientists
  3. Summary
  4. References

Introduction

These two roles can sometimes be interchangeable amongst recruiters, however, if you are specialized in either of these roles, you know there is a difference. Both roles share a focus on machine learning algorithms, yet their day-to-day can be very different. Data scientists tend to focus more on use cases like credit card fraud detection, product classification, or customer segmentation, whereas machine learning scientists focus on use cases like signal processing, object detection, automobile/self-driving, and robotics. …


Opinion

A deep dive into less-talked-about tools

Photo by JESHOOTS.COM on Unsplash [1].

Table of Contents

  1. Jira
  2. Looker
  3. Confluence
  4. Summary
  5. References

Introduction

While there are more involved or complex tools for both data analysts and data scientists, there are also some more general ones that are often overlooked when reviewing common skills between these two popular roles. Knowing these tools can either help you in your interview process, or can help you on the job once you have been hired. Two of these skills are more organizational and process-oriented whereas the other is more insight- and analytics-focused. As a data analyst and data scientist, a lot of your job is communication. Knowing complex machine learning algorithms…


Opinion

A deep dive into essential Jupyter Notebook functionalities

Photo by Greg Rakozy on Unsplash [1].

Table of Contents

  1. Add-ons
  2. Text Editing
  3. End-to-End Machine Learning
  4. SQL Adaptability
  5. Visualization Display
  6. Summary
  7. References

Introduction

If you are new to data science you may be asking yourself what in the world is a notebook incorrectly named after a planet, but if you are a current data scientist, then you know that this tool is a staple for everyday use. With that being said, I will be discussing the top five reasons to use a Jupyter Notebook [2] if you are a data scientist — and hopefully, some of these reasons are new to you. It is fortunately important to note that not…

Matt Przybyla

Sr. Data Scientist. Top Writer in Technology and Education. Author - Towards Data Science. MS in Data Science - SMU.

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