Posts Tagged ‘Data Science’

InForm

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 […]

Join the virtual AWS expert panel about data science

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 […]

MicroStrategy World 2019 Sessions

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 […]

Data Science Model Presentation

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 […]

Data Science = Synergistic Teamwork

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 […]

Accelerate business innovation with application modernization.

The Benefits of Using Cloud-Based Platforms for Data Science

As everything in this world matures and goes through the steps of evolution, Data Science is not much different. Even though it became more affordable for the companies to jump on the Data Science bandwagon and generate quick wins, Volume, Velocity, and Variety are still the bottleneck for most of the Artificial Intelligence (AI) projects. […]

Machine Learning Vs. Statistical Learning

Most of the time as a data scientist I get asked the question, what is the difference between Machine Learning and Statistical Learning? Even though you would think that the answer is obvious, there are a lot of novice data scientists that are still confused about those two approaches. As a beginner data scientist, it […]

The Tensorflow Weakness is a Gluon Strength

On October 12, 2017, Gluon became available to the public. Microsoft’s partnership with Amazon finally has a chance to outshine Google with its great TensorFlow. Gluon is a deep learning multiplatform tool which currently utilizes Apache MXNet and soon will work on Microsoft Cognitive Toolkit (CNTK) as well as other platforms to come shortly after. […]

Field of Data Science in 2018

It is no secret that a data science and analytics specialty was one of the hottest and fastest growing careers in 2017, leading to resource shortages (as denoted by the picture below). However, in 2018 and beyond, a data scientist will evolve into data engineer, a data steward, and a governance lead. Every field will […]

Cross-Industry Success with Tableau

Organizations are turning to self-service data visualization and analytics tools like Tableau to explore enterprise data without limiting users to pre-defined questions or IT requests. We’ve helped many companies across a variety of industries implement these data discovery solutions with great results. The following high-level overviews are a sample of these implementations, highlighting the power […]

5 Ways to Build a Data Lake and not a Data Swamp

In the last 6 months, my customers and I have been on a journey with some of the largest cloud data lake vendors, open source Big Data vendors, and a team of the smartest Big Data architects that I’ve worked with in my career. As we explore this journey, often our clients are looking to […]

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