Data science is a wide field with many specializations, and an individual can have a great career with a data science degree. However, curriculums vary between schools, and the specific data science classes taught in one school may not be taught in another.
There are several core skills in the data science field that recruiters and hiring managers are looking for, and you need to be sure your online data science course offers hands-on experience with those skills.
Read on to see what core data science skills are most attractive to recruiters and hiring managers and whether your online data science course has them!
1: Statistical and Machine Learning methods.
Statistical and machine learning methods are important in any data science career. Statistics is the ability to find patterns and trends in data and transform that data into a clear, understandable format. Machine learning is a subset of statistics that involves training computer programs to learn and recognize patterns from the data you feed it.
Many data science jobs require advanced statistics knowledge, so it is important to learn these methods from a data science course. It’s also true that many data science online programs focus on statistical and ML methods, so you want to be sure that the particular program you choose ranks highly in post-graduate satisfaction.
2: Programming in multiple languages.
Programming in data science requires knowledge of several different coding languages, including Python, R, Scala, Julia, MATLAB, and SQL.
An online course should familiarize you with the concepts of programming in all of these languages and offer exercises when using them individually and in scenarios where you need to pipe out, for example, a Python script into Scala or other ways that multiple languages can be usefully mixed.
3: Experience with different data science tools.
The best data science jobs require you to learn multiple tools and workflows, and you should be familiar with them all.
For example, a data science job may require you to learn how to use Jupyter, RStudio, MATLAB, etc. The in-house tools one company uses may not be the same tools used at another company, and you should be prepared to learn new tools on the job.
4: Data analysis and presentation.
Data analysis and presentation are core skills in data science. Summarizing data for a layman’s audience or presenting information in a way that people understand are important skills.
While a data science course can teach you all the important in-depth technical know-how needed for a data scientist career, it’s also important that you know how to get your data visualized and explain it to people.
There should be some focus on artistic communication, such as color theory and the presentation of data in charts and graphs, and it is also important that you know how to use Google Slides, Tableau, and similar online tools to create presentations.
Machine Learning Operations is a core skill of ML Engineering, and a data scientist who possesses knowledge in this area is in high demand.
MLOps focuses on streamlining the entire process of training, running, and monitoring machine learning models. Machine learning is often a complex, complicated process, and a data scientist must know how to get the best performance out of these models.
Continuous Integration and Continuous Deployment (CI/CD) is a big part of many data science jobs. Your deployment pipelines need to be quick and easy to set up and be highly automated to spend more time doing what you want to be doing, like solving hard problems.
As a data scientist, you want to integrate your pipelines into your workflow, so it’s important to know how to set up a CI/CD pipeline that will work for you and your company.