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.
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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.
Upto 100% Placement
Average Salary : 12 LPA
4 Million+ Data Jobs
High Package Jobs
Data Science Tools
Who oversees the data science process?
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: 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.
TOP DATA SCIENCE PROFILES
DATA SCIENTIST
MACHINE LEARNING ENGINEER
DATA ANALYST
DATA ENGINEER
DATA STORYTELLER
BUSINESS INTELLIGENCE DEVELOPER
DATA ARCHITECT
ARTIFICIAL INTELLIGENCE ENGINEER
BUSINESS ANALYST
TECHNOLOGY SPECIALIZED ROLES
When a data science platform is the right move
Your organization could be ready for a data science platform, if you’ve noticed that:
A data science platform can deliver real value to your business. Oracle’s data science platform includes a wide range of services that provide a comprehensive, end-to-end experience designed to accelerate model deployment and improve data science results.
What a data scientist needs in a platform
If you’re ready to explore the capabilities of data science platforms, there are some key capabilities to consider:
The benefits of a data science platform
A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.
In general, the best data science platforms aim to:
Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.
The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.
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.
Upto 100% Placement
Average Salary : 12 LPA
4 Million+ Data Jobs
High Package Jobs
Top Recruiters
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: 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.
DATA SCIENTIST
MACHINE LEARNING ENGINEER
DATA ANALYST
DATA ENGINEER
DATA STORYTELLER
BUSINESS INTELLIGENCE DEVELOPER
DATA ARCHITECT
ARTIFICIAL INTELLIGENCE ENGINEER
BUSINESS ANALYST
TECHNOLOGY SPECIALIZED ROLES
The benefits of a data science platform
A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.
In general, the best data science platforms aim to:
Make data scientists more productive by helping them accelerate and deliver models faster, and with less error
Make it easier for data scientists to work with large volumes and varieties of data
Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible
Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.
The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.
A data science platform reduces redundancy and drives innovation by enabling teams to share code, results, and reports. It removes bottlenecks in the flow of work by simplifying management and incorporating best practices.
In general, the best data science platforms aim to:
Make data scientists more productive by helping them accelerate and deliver models faster, and with less error
Make it easier for data scientists to work with large volumes and varieties of data
Deliver trusted, enterprise-grade artificial intelligence that’s bias-free, auditable, and reproducible
Data science platforms are built for collaboration by a range of users including expert data scientists, citizen data scientists, data engineers, and machine learning engineers or specialists. For example, a data science platform might allow data scientists to deploy models as APIs, making it easy to integrate them into different applications. Data scientists can access tools, data, and infrastructure without having to wait for IT.
The demand for data science platforms has exploded in the market. In fact, the platform market is expected to grow at a compounded annual rate of more than 39 percent over the next few years and is projected to reach US$385 billion by 2025.
Improves Business Predictions
Business Intelligence
Helps in Sales & Marketing
Increases Information Security
Automating Recruitment Processes
Complex Data Interpretation
Improves Business Predictions
Business Intelligence
Helps in Sales & Marketing
Increases Information Security
Automating Recruitment Processes
Complex Data Interpretation