In this episode, Jim Hertzfeld speaks with Nico Frantzen, director of data and AI solutions at Perficient, as they explore the transformative role of generative AI in business intelligence (BI). Nico explains how generative AI and BI come together to create generative BI, which revolutionizes decision-making and data analysis and enables businesses to access insights […]
Posts Tagged ‘Data Science’
Journey Science : How Journey Science is Transforming Data Science and Customer Experience
Computer Science, Data Science, Journey Science. Computer Science: The Here and Now In the beginning, Computer Science was abstract. The theoretical concepts of Boole, Turing, Babbage, and Lovelace quickly gave way to real-life innovations. Their focus was ambitious but simple: Change the world by making our lives more efficient and less error-prone, relying […]
Four tips to solve harder data science problems with Jupyter Notebooks
Jupyter notebooks are versatile tools that data scientists can use for a variety of purposes. In this article, we will explore four ways that Jupyter notebooks can be used to improve your data science workflow. We will discuss how Jupyter notebooks can be used to learn new programming languages, document your code, debug code, and […]
8 Ways to Data Scientist’s Can Optimize Their Parquet Queries
Some data formats are columnar. This means they store information in columns or rows. They are popular because they can be used for certain types of queries more easily than row-based ones. Parquet supports parallel query processing, meaning it can split up your data into several files in order to read in multiple processors at […]
Koalas are better than Pandas (on Spark)
I help companies build out, manage and hopefully get value from large data stores. Or at least, I try. In order to get value from these petabytes-scale datastores, I need the data scientists to be able to easily apply their statistical and domain knowledge. There’s one fundamental problem: large datasets are always distributed and data […]
[Podcast] The Data Strategy Show
A good data strategy helps organizations get the most out of their data. In order to drive business value, you need to understand what your data means. In season 1 episode 3 of the Intelligent Data Podcast, host Arvind Murali and his guest Shamir Sharma, a Data Strategist, Speaker, Podcast Host, and an industry leader […]
Take advantage of windows in your Spark data science pipeline
Windows can perform calculations across a certain time frame around the current record in your Spark data science pipeline. Windows are SQL functions that allow you to access data before and after the current record to perform calculations. They can be broken down into ranking and analytic functions and, like aggregate functions. Spark provides the […]
Data Science Virtual Expert Panel Presented by AWS
Join us and our partner Amazon Web Services (AWS) for a virtual Q&A session on Wednesday, April 15. AWS will feature one of our experts to speak on a panel about the evolution and progress being made to solve critical business problems such as customer personalization and forecasting through the use of data science. Perficient […]
Perficient & Northwell to Showcase Analytics Platform at HIMSS
Perficient is excited to showcase a comprehensive analytics and data strategy solution developed in partnership with Northwell Health at the 2019 HIMSS Global Conference and Exhibition. In this session, Perficient and Northwell Health will discuss how the importance of aligning with business decision makers is integral to a successful analytics strategy and how incrementally building […]
Quick Tips on How to Sell Your Data Science Model
During a five-week IBM training program, I learned a few things about how to sell data science models that I’d like to share it with you. The program was explicitly designed to educate and familiarize IBM’s business partners on how to expand relationships with clients; an introduction to emerging tools; and a glimpse into IBM’s future so […]
What is the Future for Data Science Platforms?
What will the future hold for data science and machine learning platforms? Most of you already saw Gartner’s perspective in their latest Magic Quadrant on the evolution of data science that was released at the beginning of 2018 and are familiar with the outcome. However, did you ever ask yourself why SAS, KNIME, RapidMiner, and H2O.ai became […]
Modeling as Proof of Concept (POC)
Why do I give my precedence to build the model as Proof of Concept (POC) instead of following established methodologies such as CRISP-DM, SEMMA, AIE, MAD Skills, etc.? Even though most of the Data scientists will say that they are two different things and used for different purposes; one is a methodology or a step […]