- Cloud Computing
- Machine Learning
- Query Languages
- Python Coding
- Data Wrangling
This article is intended for those who are looking to become a data scientist or are generally interested in what the most marketable skills are to have as a data scientist. This information was gathered by Indeed  as 12 marketable skills for data scientists, and I have picked five of those 12 that I find particularly important. The job of a data scientist can require a variety of skills ranging from spreadsheets to public speaking to DevOps. With that being said, there are still some key skills that every data scientist should either be aware of or employ themselves. Some of these skills can be learned on your own, while some can be practiced in a more formal academic setting like graduate school or a bootcamp. Below, I will be discussing five of the top 12 skills for data scientists, and providing examples of each skill as it pertains to the job of data science.
After seeing several lists and articles, including some of my own, I think this particular skill is perhaps the newest and most unique of these here. Servers, databases, analytics, and storage are all included in what defines cloud computing. The benefits include scalability, efficiency, security, and costs.
With that being, let’s give some examples of cloud computing in regards to data science:
- Amazon Web Services or AWS includes tools like Amazon RedShift for data warehousing and Amazon SageMaker. With these tools, you can query your data for preprocessing, then implement training for your machine learning algorithms, to then deploy endpoints into your production environment.
- Microsoft Azure allows for similar processing and data science use cases with tools like Azure Databricks with Apache Spark, as well as utilizing their data lakes and SQL databases.
- Google Cloud is another popular cloud computing tool that includes services like BigQuery for data querying, Google Data Studio for easy visualization of data or data science model results, and Google Cloud Comoser for workflows, DAGs, tasks so that you process your data science pipeline.
As you can see, all of these major players in the cloud computing space offer similar benefits, which usually consist of storage and processing data to automatic deployment of data science models. The choice of which sub-skill, in particular, you should use depends on what you are interested in using as well as what…
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