Tools You’ll Use in a Data Science Career
It was forecast over 30 years ago that computers would someday run everything, and it’s safe to say that someday has arrived. Data science might be the area of computer science with the greatest potential.
Companies, governments, political campaigns, healthcare providers, and nonprofit organizations all yearn to gain deeper insights into what moves their target audiences and their own operations. The data science field is so valuable that even professional sports organizations employ big data to gain a competitive edge over the rest of the field. So if you’re interested in entering data science, you can rest assured data scientists aren’t going anywhere. Continue reading to learn about some of the many tools you’ll use throughout your career.
data extract is the process of collecting or retrieving disparate types of data from a variety of sources, many of which may be poorly organized or completely unstructured.
TIBCO Connected Intelligence Cloud
No matter what it is you intend to build, you must have a foundation. It’s a universal law that applies to everything from building houses to building companies, so you’d better believe it applies to building a data analytics infrastructure.
Every corporation looking to grow its operations and revenue will need an enterprise-level infrastructure that applies data analytics for the deployment of business intelligence operations and creating applications. It’s so important to have a stable foundation for your data analytics solutions because it enables encrypted cluster computing and creates pipelines for data flow.
The TIBCO data science platform is one of the most powerful and essential tools for young data scientists to employ when using data to program algorithms to enable machine learning and adjust entire companies’ strategies. As a company and community of citizen data scientists, TIBCO offers their TIBCO Connected Intelligence Cloud, including TIBCO Spotfire, to students in the data science field. As you can see, part of TIBCO’s investment in data science is expressed by investing in future data scientists.
Another great thing about TIBCO being a team of citizen data scientists is that they created their platform with application integration in mind. In other words, they wanted to make it easier for the deployment of a community of analytical programs on one potent cloud-based cluster of servers. Statistica is one of the programs you deploy, and it’s great for data mining, data management, and much more.
As you learn the TIBCO platform, you’ll notice that one of the most incredible tools you have at your disposal is predictive analytics. It’s a highly sought-after technology and so reliable that some of the country’s top law enforcement agencies use it to predict where a crime spree is heading.
Predictive analysis is the process of using information from historical events and predictive algorithms to predict future events. Law enforcement isn’t the only sector that employs predictive analysis. Manufacturing companies use it to predict when a machine will need maintenance and to forecast demand changes. Possibly, the greatest example of its use was as a tool to predict the spread of COVID-19 and develop strategies to prevent it. As you can see, the use cases for predictive analytics are numerous and critical.
One of the most time-consuming tasks for data scientists is integrating new applications into a business intelligence infrastructure. Using TIBCO Enterprise Runtime for R (TERR) is great for big data platforms like Spark and Hadoop and enhances data discovery. It’s developed especially for reading the R language TIBCO created for programming, but it can also read Python and other programming languages.
Machine Learning Model Management
When creating a new app for deployment into your enterprise infrastructure, you need analytic pipelines to track its progress along each step from development to deployment and beyond. Machine learning model management gives you valuable insights into your applications to allow your system to make changes in real-time.