A step-by-step, quick guide focusing on the Data Science facet of the job — Table of Contents Introduction Data Exploration, Data Aggregation, and Feature Engineering Algorithm Comparison Summary References Introduction While there are articles I have written encompassing the entire data science process, including facets like business understanding and stakeholder collaboration, I wanted to focus purely on the data science part itself instead. In this article, I will…

Including a rundown of a common step-by-step project outline — Table of Contents Introduction Responding, Planning, and Meeting Updates Expected Data Science Work Summary and Personal Thoughts References Introduction Target audience: This article is intended for those who are current data scientists and interested in becoming a senior data scientist. …

A result of healthy competition from Artificial Intelligence. — Table of Contents Introduction Business Stakeholder Understanding of AI Healthy Competition to Existing Data Science Space Summary References Introduction ChatGPT is at the forefront of discussion in the data science community, and for good reason. However, it’s even more so at the forefront of the general public who haven’t and aren’t actively practicing data…

Data Science isn’t only about algorithms. Here are recommendations on how to incorporate the business side. — Table of Contents Introduction Opportunity, Problem Statement, and Stakeholders KPIs and Testing Summary References Introduction While it feels amazing to get that 90% accuracy on your classification model test set, you will need to justify using the model in your business regardless. So much of data science education is focused on many things like…

and/or Machine Learning Algorithms — Table of Contents Introduction Algorithm Model Summary References Introduction Why is this important to know the difference? As data scientists, we have a lot of jargon to communicate between even other data scientists, as well as stakeholders. Two of those jargon words are algorithm and model. Both, are often used incorrectly, as they can…

A look into quantile loss and when to use it — business use cases — Table of Contents Introduction Regression and Loss Functions When To Use Quantile and When Summary References Introduction As a data scientist who has learned a lot online, I have seen a lack of discussions around loss functions that aren’t MAE or RMSE. For this reason, I am going to give a quick summary of…

Analysis of ZipRecruiter data. Are these what you expect? — Table of Contents Introduction Top 10 and Analysis Summary References Introduction Not only is knowing salary variability across states interesting, but it can also be useful for those looking to start their first job in data science, for moving to a new state, and for understanding where they stand in their current state. Perhaps…

Analysis of ZipRecruiter top 10 cities for Data Science Salary — Table of Contents Introduction Top 10 and Analysis Summary References Introduction Understanding the salaries for different cities can not only be interesting to know, but also beneficial in determining where to live or for reference when looking for your next position in data science. It is also important to note that a higher salary…

Table of Contents Introduction Startup Advantages and Disadvantages 3. Corporation Advantages and Disadvantages 4. Summary 5. References Introduction Being a data scientist at a startup is very different from being a data scientist at a corporation, or, in other words, a well-established, large company. The choice is ultimately up to you regarding which type…