The need for gender and racial equality in the workplace is paramount for a successful organization, and data science fields are no different.
Data-driven, data-first–what every organization likes to be called, but only a few fit the description. Today, organizations cannot implement modern data-centric initiatives without formulating robust data engineering strategies.
In this article I’m going to discuss the design thinking process and how it relates to creating dashboards. Using the design thinking process enables you to create user-centric dashboards that empower your stakeholders to make effective decisions.
Building a successful machine learning model is no mean feat. It involves an arduous model-building phase and what comes next requires another rigor of maintaining the model output quality. A machine learning model once trained cannot live up to the changing data dynamics on its own, if not.
The retail industry has always been at the forefront of innovation. It started with the massive pivot of the customers from the brick and mortar to online shopping. Covid has further exacerbated this shift as is evident in the change in consumer preferences and buying behavior. This change calls.
Nearly one-third of enterprises admit they don't use AI-based solutions. Tackling the data, tech and deployment-related challenges in a large company can be overwhelming, but not impossible. Filipa Castro, Data Scientist at Continental shared some good practices with us on how to successfully.
We are thrilled to announce that DSSelevate (DSSe), our initiative to help close the gender gap in tech, will be part of this year's DataConnect Conference in Columbus, Ohio, hosted by Women in Analytics (WIA) this coming June 2nd and 3rd!
AI is everywhere and its rate of adoption has significantly increased since the digital acceleration seen through the usher of the covid period. But the aesthetics get worn out soon as the aspiring AI/ML projects do not live up to the potential and end up under-performing.
If you are planning to choose machine learning for your business problem assuming its predictions are always correct, then there is something you should know. Machine learning algorithms are probabilistic by nature and are not perfectly accurate. Then what should we do when no ML model is perfect?.
The role of customer experience is vital and known for a long time. According to the data gathered by CGS, 30% of customers are willing to pay more for excellent service. This can be delivered by listening to the voice of customers and constantly monitoring their feedback.