Spotfire Data Science (formerly TIBCO Data Science) is a comprehensive platform for operationalizing data science, allowing users to scale data science across an organization to solve complex challenges faster and speed innovation. It is designed to enable data scientists to create innovative solutions using the latest machine learning techniques and open source developments. Create ML pipelines using a point-and-click UI or code. Orchestrate analytics using the tools, languages, and any…
I prefer Spotfire Data Science's approach. It is more natural and fits the way I think. I prefer to use Spotfire Data Science's VB for writing macros. It is real code, meaning that I do not need to trick the software to do what I need and there are no implied loops over …
I think Spotfire Data Science does two things better than these other software tools. First, Spotfire Data Science is perfect for exploratory analyses. It's suggested models are great, and the visualizations provide a nice look at the data before any modeling is done. Second, …
We used Excel sheets before Data Science came into the organization. The main difference is that I cannot dynamically change the data in Excel sheets, which eats away at least 2 to 3 workers' time. Now, with Data Science, dynamic reporting can be done pretty quickly, which …
Data Science is a more robust analytical platform. Alpine Data is similar but not as powerful. Spotfire is a great combination to visualize information before modeling.
Microsoft Office is a classic. Older employees are much more familiar and comfortable with Office than new cloud-based applications. The drawback to Microsoft Office is it isn't cloud-based, and we end up with countless versions of documents and data all over the place. It …
I personally like the collaborative and team-centered aspects of Spotfire Data Science better than most other programs. In this way I think it stands out among competitors with both its ease of use and fluid userface. Some of the other programs, such as MATLAB, seem to do a …
Used Mathematica (not in the list), which has greater overall functionality (outside of statistics), but Data Science is much easier to use, especially for analysts with limited experience.
In the past, cost, ease of use, and versatility were what made me choose Data Science. However, the cost concern may be leading my company to pursue solutions like R programming instead.
It's well suited for companies who are working long-distance and who have lots of data to organize and present. My company does extensive field work collecting thousands of data points at a time, in many cases, so this is an excellent way to share data collected in the field with those still in the office so that reports can begin before our travels are really finished. It makes the office much more productive! Spotfire Data Science is less suited for companies with older employees or companies that are generally in one office and not doing distance work. It can be a little tricky for older people to grasp the interface and really utilize the program to its full potential so of course spending more time learning the software than using it effectively isn't a great use of time or money.
The interface is screen based and much easier for teams of non-experts to use. Can, however, directly utilize in scripts and/or programming languages, so best of both worlds.
Has an extensive range of statistical methods, as well as extensive statistics around those methods.
Although the graphics can be copied into Word or Excel, the copying and pasting process is buggy. Sometimes I basically have to do a Capture Rectangle of a graphic and paste it as a jpg, rather than having an embedded and editable graphic in my Word document.
The dialog boxes for some of the common tests, like multiple regression and ANOVA, use quirky terminology that is not intuitive to new users coming out of grad school with a more updated education. I have been using it for over ten years, and the dialogs have not updated in all of that time.
The tests like NMDS and PCA are clunky and difficult to use and get the appropriate outputs, especially compared to specialized software like PRIMER.
The company is hesitant to spend this much on software. They are primarily an engineering firm, and they don't understand the use of analytical software for environmental professionals.
I prefer Spotfire Data Science's approach. It is more natural and fits the way I think. I prefer to use Spotfire Data Science's VB for writing macros. It is real code, meaning that I do not need to trick the software to do what I need and there are no implied loops over solving simple problems. The graphs are publication quality and can be edited by hand or using a macro if I am building hundreds of them. Spotfire Data Science had a user-friendly approach to building lengthy data processing streams (in its workspaces). It is just so fast for analyzing a dataset that you have never seen before and efficient for ongoing work on the same data.
Our company has had the program for less than 1 year. We don't expected a positive return this year. The goal is for Data Science to led to defined projects by the end of the end of the year and implementation in the following two. Overall, we are planning on 4 years to fully recoup the cost of the software and the cost of implementing identified projects.