Key Attributes of a Data Scientist
So who is a data scientist? A person employed to analyse and interpret complex digital data, such as the usage statistics of a website, especially in order to assist a business in its decision-making
But what makes a quality user interface?
1) Statistical Thinking –
Turning data into information, Knowledge of algorithms. Knowing your algorithms and how and when to apply them is arguably the central task to a data scientist’s work. However, to do this well can be an art and a science. This makes a great data scientist.
2) Technical Knowledge –
Data scientists write code and work with teams to produce tools, pipelines, packages, modules, features, dashboards, websites, and more. We write code on the back end and the front end. To be really successful as a data scientist, the programming skills need to comprise both computational aspects — dealing with large volumes of data, working with real-time data, cloud computing, unstructured data, as well as statistical aspects — working with statistical models like regression, optimization, clustering, decision trees, random forests, etc.
The job is to ask questions of data and of people. Most people don’t know or care about the limitations of data, so a data scientist must be curious about what they are doing and what they want to achieve out of it. And our field is evolving so quickly that we have to maintain our interest to maintain our edge.
4) Machine Learning, Deep Learning, AI
A data scientist needs to stay in front of the curve in research, as well as understand what technology to apply when. Data scientists need to have a deep understanding of the problem to be solved, and the data itself will be the answer. Being aware of the project cost to the ecosystem, bandwidth, and other system boundary conditions — as well as the knowledge of the customer — itself helps the data scientist understand what technology to apply.
Here are some of the tools needed for data scientist role are.