Data Science Platforms Overview
What is Data Science Software?
Data science technology can help organizations turn their data into a valuable resource in the creation of business value. Data science tools are capable of handling data volumes that are too big for traditional databases or statistical tools.
Data science tools create value by mining large amounts of structured and unstructured data to identify patterns can help an organization to more effectively manage costs and achieve competitive advantage.
Data science tools incorporate a variety of component technologies such as machine learning, data mining, data modeling, data mining, and visualization.
Why did Data Science Technology Emerge?Data science is an emerging response to the unprecedented volumes of data that are available to businesses for decision-making purposes. The desire to derive social and economic value from this newly available data is limited only by the lack of expertise and tools. Data science tools have emerged to fill this gap.
Data Science Software Features & Capabilities
Data Science platform features vary considerably from one product to the next. Available features also differ between features for bona fide data science platforms and machine learning tools which are really a subset of data science.
Data access and ingestion
Data visualization and discovery
Integration with big data tools like Hadoop
What Skills are Required?
The skill sets required for true data science are in short supply. This puts pressure on tool developers to reduce complexity to increase the potential user pool. In general, data scientists have advanced analytics skills like actuaries, who calculate insurance risks and premiums. They are quantitative researchers who typically have strong programming skills.
What do Data Scientists Do?
Although data scientists write code, they are quite different to developers. One of the main differences is that data scientists are primarily engaged in research, rather than in building products.
The job performed by data scientists is largely experimental. They build models that are designed to be predictive and then run them to see how well they perform. Then they tweak them and run them again.
The kinds of problems data scientists try to solve use huge amounts of unstructured data. Some examples are models that accurately predict customer churn or models to optimize vacation rental pricing based on location, time of year, etc.
Collaboration with Business
Agile development processes require a user or client to articulate the business need and provide constant feedback on what is being built. Data science teams are similar. They are most effective when they work closely with the people who actually use the models that they build. Without this alignment, the models built may not effectively solve the business problem that the business unit is struggling with.
Types of Data Science Platforms
Some platforms are primarily about model development and contain coding language capabilities to this end. Data Science models are typically quite complex and require advanced coding skills and often specialized hardware. Data scientists also frequently utilize many machines concurrently by spreading work across them.
A number of platforms do not contain languages for writing code but, instead, allow products like R, SAS, or Python to execute model code. Instead, they function as a system of record for all the models being developed by an entire data science team.
Data scientists use a wide variety of tools some of which, like Python and Apache Spark and Hadoop, are open-source. Data scientists also use data mining tools, NoSQL databases, statistical computing tools like R, and others.
Enterprise data science platforms that keep track of projects and automate some of the code writing are relatively new arrivals. These platforms charge for an annual subscription. The price can vary depending on whether the software is running in the cloud or on-premise. There are also likely to be usage fees for compute time.