Opinion

Table of Contents

1. Introduction
2. Programming
3. Business Savvy and Intelligence
4. Statistics and Mathematics
5. Machine Learning
6. Visualization
7. Summary
8. References

Introduction

As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role. …

Opinion

Table of Contents

1. Introduction
2. Data Scientist
3. Computer Scientist
4. Similarities and Differences
5. Summary
6. References

Introduction

Data Science and Computer Science often go hand-in-hand, but what really makes them different? What do they have in common? After experiencing several different roles in Data Science at various companies, I have realized general themes of the Data Science process, along with how Computer Science is incorporated into that process as well. It is important to note the differences between these two positions, as well as when one requires the other, and vice versa. Usually, a Data Scientist will benefit from learning Computer Science first, and then specializing in Machine Learning algorithms. However, some Data Scientists start straight into statistics before learning how to code, focusing on the theory of Data Science and Machine Learning algorithms. That was my approach, with learning Computer Science and programming afterward. That being said, does a Data Scientist need to know Computer Science? The short answer is yes. While Computer Science can encompass Data Science, especially critical in artificial intelligence, I believe the main theme of Computer Science is software engineering. Keep on reading if you would like to learn more about the differences between (these two roles), as well as their respective similarities. …

Opinion

Table of Contents

1. Introduction
2. Business Problem Statement
3. Isolating Business Metrics
4. Algorithm Comparison
5. Code Review
6. MLOps and DevOps
7. Summary
8. References

Introduction

Working professionally in Data Science for a few years now, I have discovered some best practices from a variety of different experiences. I will be highlighting my top five Data Science practices in hopes of helping you in your future endeavors. While there are countless ways to improve your Data Science process, these are key methods to improve not only your everyday work as a Data Scientist, but as an employee as well. That being said, some of these practices can be applied to more than just Data Science, including, but not limited to Data Analytics, Machine Learning, Software Engineering, Data Engineering, and DevOps. …

Opinion

Table of Contents

1. Introduction
2. Data Scientist
3. Machine Learning Operations Engineer
4. Similarities and Differences
5. Summary
6. References

Introduction

While I have written articles on Data Science and Machine Learning Engineering roles, I wanted to compare the specific positions of Data Scientists and Machine Learning Operations Engineers, often referred to as MLOps Engineers. Machine Learning itself can be incredibly broad, so as a result, a newer career has emerged that solely focuses on the operations rather than the research that goes behind the algorithms themselves. Data Scientists ironically focus more on Machine Learning algorithms than does an MLOps Engineer. You could even go as far as saying an MLOps Engineer is a Software Engineer traditionally who has then added the specialization of deployment and production parts of the overall Data Science process. …

Opinion

Table of Contents

1. Introduction
2. Random Forest
3. XGBoost
4. CatBoost
5. Summary
6. References

Introduction

With the year 2021 in full effect, I wanted to discuss the updated list of my top three favorite Machine Learning algorithms and why. In the past year, I have gained more professional experience as well as practical experience from studying and playing with different algorithms on my own in my free time. New use cases, Kaggle examples, videos, and other articles have led me to focus on my favorite three algorithms, which include Random Forest, XGBoost, and CatBoost. There are benefits to them all and you can certainly produce impressive results with all three. While one is older and dependable, another is powerful and competitive, and the last is new and impressive, these three algorithms stand strong at the top of my list, and it will be interesting to see what three top your list. …

Opinion

Table of Contents

1. Introduction
2. CatBoost
3. Short Tutorial
4. Summary
5. References

Introduction

With Data Science competitions becoming more and more popular, especially on the site, Kaggle, so are new Machine Learning algorithms. Whereas XGBoost was the most competitive and accurate algorithm most of the time, a new leader has emerged, named CatBoost. There has been an open-source library that is based on gradient boosting decision trees from the company Yandex [2]. In their documentation, they include GitHub references and examples, news, benchmarks, feedback, contacts, tutorial, and installation. If you are using XGBoost, LightGBM, or H2O, CatBoost documentation has benchmarked and proved that they are the best with both tuned and default results. Of course, if you have more categorical variables, then CatBoost is the way to go. …

Opinion

Table of Contents

1. Introduction
2. Automated Preprocessing
3. Remote Work
4. New Boosting Algorithm
5. New Educational Experiences
6. Airflow
7. Summary
8. References

Introduction

With the events of 2020 comes a normalization of different ways we learn and work, some may revert back to how it used to be, while some are here to stay. Some of the things we can look forward to in 2021 are not necessarily dependent on 2020 but are new technologies that will most likely become prominent. I will be discussing both new things to look forward to because of the events of 2020, while also examining some possible increase in popularity of certain technologies. Keep reading below if you would like to learn more about five things to look forward to as a Data Science in 2021. …

Opinion

Table of Contents

1. Introduction
2. Production Training
3. GitHub Examples
4. Creative Thinking
5. Kaggle Examples
6. Recent Technology
7. Summary
8. References

Introduction

There are several ways to learn Data Science or Machine Learning, whether that be from a traditional university or online courses. Additionally, there are some alternative ways to learn, like from peers, YouTube videos, or articles like this one. My goal is to shed some light on some of the expectations and ways to improve yourself as a Data Scientist. These points include production training, GitHub examples, creative thinking, Kaggle examples, and recent technology.

Keep on reading if you would like to learn more about some advice from a Data Scientist in the field. …