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What has stayed the same and what’s different?

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Photo by Jude Beck on Unsplash [1].

Table of Contents

  1. Introduction
  2. What has stayed the same?
  3. What is different?
  4. Summary
  5. References

Introduction

2020 has of course had a plethora of unfortunate events affecting nearly everyone. But how was the tech industry been affected, and more specifically, how has Data Science in 2020 been affected? Depending on where you live, which industry you work in, and what type of Data Scientist you are, these similarities and differences may or may not apply to you. Below, I will be discussing these effects and how it may still affect you for this rest of the year.

What has stayed the same?

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Photo by Gabriel Benois on Unsplash [2].

Because Data Science is a part of the tech field most of the time (or the role itself does not nearly require as much in-person work compared to other jobs), there have been a few parts of the day-to-day job that have fortunately been able to stay the same without negative disruption. Here are the similarities or parts of the Data Science process that have stayed the…


Opinion

Which one is right for you?

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Photo by Alex on Unsplash [1].

Table of Contents

  1. Introduction
  2. Data Science Modeler
  3. Machine Learning — Platform Engineering
  4. Machine Learning — Software Engineering
  5. Data Engineer
  6. Analyst and Insights
  7. Summary
  8. References

Introduction

While there are countless sources for the formal title, amount, and percent of Data Science roles when compared to others, I wanted to focus more on personal experience of the roles themselves. A Data Science job description is one thing, and the actual role, day-to-day, is another thing. Over the course of a few years, along with working alongside others, I have realized there are different types of Data Science roles — and, any of the roles I will be discussing can sometimes all have the same job title of Data Scientist. Depending on the company, you make see these more specific roles highlighted word-for-word. Which roles are right for you, and which ones have you already worked in? Have you seen a difference in Data Science positions, or do you generally see all of them as a part of one role? …


How can be Tableau be useful with and without programming?

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Photo by Kaitlyn Baker on Unsplash [1].

Table of Contents

  1. Introduction
  2. Quick and Simple
  3. SQL, R, Python, and MATLAB
  4. k-means Algorithm
  5. Cross-functional
  6. Summary
  7. References

Introduction

Tableau [2] is becoming more and more useful for Data Scientists as it incorporates more finely tuned functionality. If you are not familiar with Tableau, it is essentially a tool that is widely used by several different types of people in their respective careers. You can visualize data and share it with others so you can see how it is not only beneficial to a Data Scientist, but also to a Product Manager, SQL Developer, Data Analyst, Business Intelligence Analyst, and many more — the list goes on and on. …

About

Matt Przybyla

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

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