The Various Data Science Career Paths

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The field of data science has exploded in the past couple years as businesses become increasingly reliant on data. Along with this, there’s been a surge in the various branches of data science and career paths that follow. Obviously, we’ve heard of a data scientist, but what about a data analyst or a business analyst? Or a data engineer or a data architect? While there’s much crossover between these roles, each one has its own general focus. Let’s examine these career paths in a bit more detail:

Data Scientist

A data scientist is concerned with designing and evaluating data processes, performing statistical analysis and building models. This person is involved in developing the methodology behind the data collection, storage, cleaning and modeling process. A data scientist will work with various stakeholders across the organization to identify opportunities to use data to drive business outcomes. They may use machine or deep learning techniques to perform regression or classification tasks, usually for predictive purposes.

Data Analyst

Data analysts work on a more micro level than data scientists. They perform statistical analysis, clean the data, and create meaningful visualizations or dashboards from said data, with the goal of highlighting trends or anomalies. They help companies make better business decisions by analyzing information such as sales numbers or market research and synthesizing their findings into interpretable visual representations.

Data Engineer

A data engineer is focused on building the architecture around data collection and storage. The data engineer is more focused on upstream part of the data science process, that usually involves the conversion of raw data into a usable format. While these tasks may intercept with a data scientist or data analyst, the systems and methodologies behind the collection, storage and maintenance of the data is their specialty.

Data Manager

Data managers have a strong awareness of the business side of a company’s data science initiatives and are responsible for the flow of data between teams. They supervise the company’s systems and networks, organize stored data, and may participate in some data analysis aspects. They also must pay close attention to data security protocols throughout the process. Overall, this role is focused on the operational aspects of the data science process and works to ensure efficiency in these processes.

Data Architect

Data architects design, manage and implement an organizations data architecture. They are responsible for the overall framework of how an organization will acquire, create, maintain, retrieve and process data within the organization. Data architects have to coordinate with multiple departments to understand and document the flow of data within an organization and design a framework around it. They may also be involved specifically in data warehousing, database design, and big data architecture.

Business Analyst

A business analyst acts as the intersection between the business and technology side of a company. While they may be more focused on strategic business initiatives than a data analyst, they use data science techniques and processes to drive these decisions. The end goal for a business analyst may be to answer a business question via the use of data whereas the end goal for a data analyst may be in analyzing the data itself.

Machine Learning Engineer

A machine learning engineer acts as the intersection between data science and software engineering, as the role focuses specifically on using machine learning to create software. This involves creating AI systems and machines, usually for predictive purposes. For this role, one must have a mastery of the various machine learning and deep learning algorithms, programming and modeling. They work close at hand with data scientists to feed data into the models they create.

The following table summarizes the main data science career paths, and the corresponding technical skills and minimum education requirements for each:

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