Opinion

Table of Contents

1. Introduction
2. Advertise on LinkedIn
3. Know Your Price
4. Always Looking for the Next Best Platform
5. Quality Quantity Quickly
6. Final Thoughts
7. 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

Table of Contents

1. Introduction
2. Data Science
3. Artificial Intelligence
4. Summary
5. 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

Table of Contents

1. Introduction
2. Data Scientists
3. Machine Learning Scientists
4. Summary
5. 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

Table of Contents

1. Introduction
2. Jira
3. Looker
4. Confluence
5. Summary
6. 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

Table of Contents

1. Introduction
2. Add-ons
3. Text Editing
4. End-to-End Machine Learning
5. SQL Adaptability
6. Visualization Display
7. Summary
8. 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…

Opinion

Table of Contents

1. Introduction
2. Data Science
3. Machine Learning
4. Summary
5. References

Introduction

Overlap between these two popular tech roles is sure to happen, so let’s dive deep into what skills are required for both roles, and what makes them different. In general, data scientists can expect to work on the modeling side more, while machine learning engineers tend to focus on the deployment of that same model. Data scientists focus on the ins and outs of the algorithms, while machine learning engineers work to ship the model into a production environment that will interact with its users. …

Table of Contents

1. Introduction
2. Installation and Imports
3. Define Dataset
4. Apply Model
5. Predict
6. Summary
7. References

Introduction

CatBoost [2] has beaten many other popular machine learning algorithms on benchmark datasets where logloss was the error metric. It beat mainly LightGBM and XGBoost, which have recently been the standard before in not only data science competitions, but also in professional settings as well. Now is the time to learn this powerful library, and below is how you can implement it in four easy steps.

Installations and Imports

This tutorial will be using popular data science tools like Python and Jupyter Notebook. First, we will start off with the three simple…

Opinion

Table of Contents

1. Introduction
2. Natural Language Processing
3. Business Intelligence
4. Machine Learning Operations
5. Summary
6. References

Introduction

While there are countless impactful courses associated with specific universities, I want to discuss the three encompassing courses you should look for, as well as to make sure are at your data science program. These courses represent key topics of data science. It is important to get experience in these as soon as possible so that you know what type of data scientist you want to be. The profession is quite vague and broad, so narrowing down your data science job type is important to do sooner rather than…