The tools used for data science are rapidly changing at the moment, according to Gartner, which said we're in the midst of a "big bang" in its latest report on data science and machine learning platforms. "The data science and ML market is healthy and vibrant, with a broad mix of vendors offering a range of capabilities," Gartner says in its Magic Quadrant for Data Science and Machine Learning Platforms published January 28. "The market is experiencing a'big bang' that is redefining not only who does data science and ML, but how it is done." The analyst group defines a data science platform as an integrated place where data scientists, citizen data scientists, and developers can get all of the core capabilities that they need to not only build data science application, but to embed them into existing business processes and manage and maintain them over time. Integration and cohesion are keys, in Gartner's view, and applications that simply bundle various packages and libraries – especially open source offerings -- are not considered true platforms.
Gartner published its latest Magic Quadrant for data science and machine learning platforms last week. Sixteen vendors made cut for Gartner's report this year, the same number as last year. However, there were some important changes, including some vendors who made big jumps and some who lost ground. The biggest difference arguably was the addition of "machine learning" to the name of Gartner's report. "Although data science and machine learning are slightly different," the Gartner analysts write, "they are usually considered together and often thought to be synonymous."
Predictive analytics has emerged as a major force in the business world. These solutions--which typically encompass data mining, business intelligence and machine learning components--help organizations understand how to better focus research, development, marketing, maintenance, cybersecurity and numerous other tasks. Using statistical techniques and specialized algorithms, they provide insights and models that would otherwise fly beneath the radar, thus helping an enterprise understand conditions and allocate resources more effectively. According to research firm MarketsandMarkets, the global predictive analytics market is expected to grow from $4.56 billion in 2017 to $12.41 billion by 2022. It noted that, among other things, predictive analytics helps companies spot anomalies, anticipate events, use what-if-simulations and understand customer behavior.
Enterprise decision-makers look up to Gartner for its recommendations on enterprise software stack. The magic quadrant report is one of the most credible, genuine, and authoritative research from Gartner. Since it influences the buying decision of enterprises, vendors strive to get a place in the report. Gartner recently published its magic quadrant report on data science and machine learning (DSML) platforms. The market landscape for DS, ML and AI is extremely fragmented, competitive, and complex to understand.
This article on data visualization tools was written by Jessica Davis. Data visualizations can help business users understand analytics insights and actually see the reasons why certain recommendations make the most sense. Traditional business intelligence and analytics vendors, as well as newer market entrants, are offering data visualization technologies and platforms. Tableau Software is perhaps the best known platform for data visualization across a wide array of users. Some Coursera courses dedicated to data visualization use Tableau as the underlying platform.