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Most people — if they’re looking to make more money, they think they have to work more hours, and while that is a way to make more money, it’s kind of a lousy solution to this problem.

Often, in big companies, team leaders in charge of people with specialized skills are data scientists; their skill set allows them to overlook a project and guide them from start to finish.

Data engineers also work on batch processing of collected data and match its format to the stored data. In short, they make sure that the data is ready to be processed and analyzed.

Before we start, I must say that these titles are not fixed and may change in the future. Also, some roles may overlap and have more or fewer responsibilities based on the company hiring. However, this article should help you explore the top 10 data science roles for the most part.

You probably think what a boring way to make passive income, but it’s honestly one of the best opportunities that you have potentially available to you right now in your 20s.

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Let’s start with the most general role, data scientist. Being a data scientist entails, you will deal with all aspects of the project. Starting from the business side to data collecting and analyzing, and finally visualizing and presting.

I hope this process has simplified a portion of your investing strategies. Be sure to check out my next articles that will be focused on building predictive models with these technical indicators. Also, connect with me on LinkedIn here. I’m always happy to make some new connections!

The second most known role is a data analyst. Data scientist and data analysis and somewhat sometimes overlapped a company will hire you, and you will be called a “data scientist” when most of the job you will be doing is data analytics.

Data analysts are responsible for different tasks such as visualizing, transforming, and manipulating the data. Sometimes they are also responsible for web analytics tracking and A/B testing analysis.

I hope this process has simplified a portion of your investing strategies. Be sure to check out my next articles that will be focused on building predictive models with these technical indicators. Also, connect with me on LinkedIn here. I’m always happy to make some new connections!

You’re in a bit of a unique situation here because you generally have a lot of time on your hands if you don’t have kids or a family yet — but you probably don’t have a lot of money, and when it comes to earning passive income, you either have to invest your time or invest your money.

Data engineers are responsible for designing, building, and maintaining data pipelines. They need to test ecosystems for the businesses and prepare them for data scientists to run their algorithms.

A data scientist knows a bit of everything; every step of the project, because of that, they can offer better insights on the best solutions for a specific project and uncover patterns and trends. Moreover, they will be in charge of researching and developing new algorithms and approaches.

We now have the historical data for Microsoft going back to 1986 as well as 40 technical indicators to analyze alongside it. A sample of our final results can be seen below.