What are the main differences between data analyst and data scientist?

[tanjimajuha20]

xperts in data analysis and processing, data analysts and data scientists are professionals whose skills are particularly in demand on the job market. Thanks to targeted and technical training, learners are directly operational and able to establish predictive models that are essential for decision-making within a company or for research purposes. What are the main differences between data analysts and data scientists? Let's take stock.

Technical skills
There is a fundamental
cameroon phone data difference between the technical skills of a data scientist and those of a data analyst. Both experts manipulate data in order to cross-reference the main elements and group them together in order to make them usable. To do this, the data analyst must be able to perfectly master data analysis software. He also has an in-depth knowledge of probabilities and statistics. To be able to process all types of information, the expert also knows coding languages ​​such as Python. Finally, among his technical skills, the analyst has an excellent ability to communicate and disseminate information, as well as an abstract visibility of all the data processed.

While the data scientist has an excellent conception of complex algorithms. This unique faculty makes him an expert in machine learning, but also in deep learning. Thanks to his programming skills, and more particularly with the mastery of the C++, R, and Python languages, his use of artificial intelligence is complementary to all the advanced knowledge in mathematics and linear algebra that he possesses.

Professional responsibilities
When it comes to professional responsibility, the difference between a data analyst and a data scientist lies in the purpose of their respective data analysis . Indeed, the data analyst's primary responsibility is to ensure the accuracy of the data collected. The second part of their mission is to organize all of the data in order to facilitate analysis. To do this, they use structured formats after performing statistics, trend identification explorations, and finally, explicit connections. It is then in a detailed and popularized report that the specialist highlights their conclusions as well as the most relevant key points according to the objectives of their data collection. The role of the data analyst also includes the dissemination and simple communication of the results obtained, the aim being to carry out usable predictive analyses that will actively participate in decision-making sessions.

On the other hand, the data scientist designs complex algorithms in order to solve targeted problems, either through machine learning or artificial intelligence. It is therefore his expertise in the field of computer science, but also in data science, which allows him to create relevant prediction models, or even intuitive recommendation solutions. In his daily life, the data scientist experiments with all the new technologies such as data processing software or automatic extraction tools, evaluates cutting-edge approaches, and deploys models in order to integrate them into existing systems. His main collaborators are essentially the engineering teams and product managers.