Building, evaluating, deploying, and monitoring machine learning models can be a complex process. That’s why there’s been an increase in the number of data science tools. Data scientists use many types of tools, but one of the most common is open source notebooks, which are web applications for writing and running code, visualizing data, and seeing the results—all in the same environment.
Some of the most popular notebooks are Jupyter, RStudio, and Zeppelin. Notebooks are very useful for conducting analysis, but have their limitations when data scientists need to work as a team. Data science platforms were built to solve this problem.
To determine which data science tool is right for you, it’s important to ask the following questions: What kind of languages do your data scientists use? What kind of working methods do they prefer? What kind of data sources are they using?
For example, some users prefer to have a datasource-agnostic service that uses open source libraries. Others prefer the speed of in-database, machine learning algorithms.
At most organizations, data science projects are typically overseen by three types of managers:
Business managers: These managers work with the data science team to define the problem and develop a strategy for analysis. They may be the head of a line of business, such as marketing, finance, or sales, and have a data science team reporting to them. They work closely with the data science and IT managers to ensure that projects are delivered.
IT managers: Senior IT managers are responsible for the infrastructure and architecture that will support data science operations. They are continually monitoring operations and resource usage to ensure that data science teams operate efficiently and securely. They may also be responsible for building and updating IT environments for data science teams.
Data science managers: These managers oversee the data science team and their day-to-day work. They are team builders who can balance team development with project planning and monitoring.
But the most important player in this process is the data scientist.
As a specialty, data science is young. It grew out of the fields of statistical analysis and data mining. The Data Science Journal debuted in 2002, published by the International Council for Science: Committee on Data for Science and Technology. By 2008 the title of data scientist had emerged, and the field quickly took off. There has been a shortage of data scientists ever since, even though more and more colleges and universities have started offering data science degrees.
A data scientist’s duties can include developing strategies for analyzing data, preparing data for analysis, exploring, analyzing, and visualizing data, building models with data using programming languages, such as Python and R, and deploying models into applications.
The data scientist doesn’t work solo. In fact, the most effective data science is done in teams. In addition to a data scientist, this team might include a business analyst who defines the problem, a data engineer who prepares the data and how it is accessed, an IT architect who oversees the underlying processes and infrastructure, and an application developer who deploys the models or outputs of the analysis into applications and products.
IDSA courses are designed to prepare students for real commercial work in the area of data science, AI and big data.
IDSA trainers are actively working in the industry and will teach you how to practice data science and advanced analytics.
The member network offers support and guidance from mentors and direct connections to the industry. You will meet employers in-person at IDSA events.