Data scientists use their skills in technology and social science to discover trends and manage data. They have knowledge in algorithm design, analysis, algebra, statistics, and scripting languages. They are also commonly skilled in data modeling languages like Python, R*, and others. Additionally, machine learning, version control, and database knowledge are also crucial for their work. They work in conjunction with other professionals to deliver AI projects in specific industries. Some of the projects that data scientists can engage in are in the area of banking and finance, search engines, and digital advertising.
Deep learning scientist
This professional specializes in deep learning. They should have skills in deep learning theory like word2vec, long short-term memory networks (LSTM), convolutional neural networks (CNNs), recurrent neural networks, and generative adversarial networks. They also have skills in deep learning frameworks. They specialize in different domains like computer vision, natural language processing, audio processing, and time series analysis. They work on projects such as image tagging, object detection, machine translation, AI for a self-driving car, and music generation, among others.
A data analyst gathers, cleans, and interprets data to answer questions or solve a specific problem. Their skillset is similar to that of a data scientist. They can work on descriptive data but may not be good enough on predictive analysis of data like machine learning and mining. Some of the projects they typically work on include metric and reporting, building an analytical dashboard, and modeling tasks.
A data engineer is a specialist who develops algorithms that will make raw data useful to the business. Their work involves integrating, consolidating, cleansing, and structuring data for use in analytics. They make data easily accessible and optimize the big data ecosystem within the organization. Although the skills of the data engineers and those of data scientists overlap, the differences can be seen in their skills. For example, a data engineer must have algorithm design and analysis skills, scripting language, and be familiar with machine learning theory. Data engineers should be familiar with distributed machine learning frameworks, relational databases, and data management. Most data engineers are familiar with container technologies like Docker*.
DevOps Engineer (Development operations)
This individual has extensive software engineering, operating systems, distributed systems, and cloud computing skills and is responsible for maintaining the infrastructure of a project. The DevOps engineer's critical skills include algorithm design, databases, scripting language, distributed systems, version control, container technology, cloud computing, and computer security skills. They also have computer networking skills and know about continuous deployment.
Despite each professional having their role, AI is often a team effort that requires members to work together to achieve a common goal. Therefore, when rolling out AI projects, companies must ask themselves if their teams are diverse enough or have the right skills to deliver quality projects and help the organization meet customer expectations. The team must be organized to enhance efficiency and improve the delivery of results.