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Complexity of the Work
In addition to the organizational aspect, in order to be able to keep these professionals in the company for at least 5-6 years so that they add value, the technological aspect also comes to the rescue of the complexity of the work required.
New no-code tools arrive on the market for the classification and statistical analysis of data, and also for the robotic development of code in Python and R in order to relieve these professionals of the most repetitive tasks.
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A Complex Job
Data Scientist, a complex job. The digital sector has greater mobility than the others, thanks to the fact that the same technologies are applied in a fairly similar way from one industry to another.
It is therefore easy to change jobs, but in the case of Data Scientists the t...
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Training Models
The no-code and low-code approaches are really important in the company: on the one hand they allow "business" colleagues to be increasingly independent in the creation of reports and statistical analyzes, on the other hand they help digital staff to increase their productivity and quality of the...
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Natural Language
Tools that, by independently testing which machine learning technique is best suited to the problem, to research the key parameters and also to evaluate the model, allow the Data Scientist to concentrate on creating the model.
Recently, solutions have emerged that can process, understand an...
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Data into Profit
Every company has at its disposal a huge amount of unstructured data, in separate silos, processed for different reasons, and the hope of mixing it all with a sprinkle of artificial intelligence, thanks to the gurus who are the data scientists, is often misplaced.
The dream of transforming ...
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New Scenarios
If until yesterday we were talking to Alexa to turn on the light or look for a piece of music, now we are at the point of asking these tools to help us in the creation of artificial intelligence models.
Think of the advantage of being able to talk to a machine and arrive together with a for...
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More like this
The main difference between R and Python
They approach data science differently:
- R is used for statistical analysis.
- Python provides a more general approach to data.
- R is built by statisticians and leans heavily into statistical model...
Other key differences
Data collection
- Python supports all kinds of data formats.
- R is designed for data analysts to import data from Excel, CSV and text files.
Data exploration
- In Python, you ...
The conservation of complexity
To do a difficult thing in the simplest way, we need a lot of options. When looking an any set of tools for a task, such as a digital photo editing program, a novice will see complexity. A professional sees a range of different tools, each of which is easy to use. They know h...
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