Analytics & Automation
Time Series Analysis with ARIMA: Part 2
This is a continuation of the Time Series Analysis posts. ARIMA stands for Autoregressive Integrated Moving Average. These models aim to describe the correlations in the data with each other. You can use these correlations to predict future values based on past observations and forecast errors.
Time Series Analysis with ARIMA: Part 1
PART 1: Introduction to Time Series At Cisco, our partners and clients want ways to track and monitor their Cisco routers, switches, and other such devices. An important avenue of my work as part of the Customer Experience Data Incubation Team is to help track device utilization over time. One such way to think about […]
Answering The Big Three Data Science Questions At Cisco
Data Science Applied In Business Today. In the past decade, there has been an explosion in the application of data science outside of academic realms. The use of general, statistical, predictive machine learning models has achieved high success rates across multiple occupations including finance, marketing, sales, and engineering, as well as multiple industries including entertainment, […]
Is Data Science a Pre-Requisite for AI? The Data Science & AI Hierarchy of Success
Is Data Science a universal prerequisite for AI initiatives? Cisco's Chief Data Evangelist, Jennifer Redmon, proposes AI and Data Science can and should be pursued simultaneously based on a common foundation.
Fostering a Data-Driven Culture: The Data Science & AI Hierarchy of Success
What role does culture play in the achievement of Data Science and AI-driven transformation and innovation? Cisco's Chief Data Evangelist, Jennifer Redmon, proposes that mindset, and ultimately, culture, are a critical foundation.
Data Science & AI for Good Launch
Cisco's Chief Data Evangelist, Jennifer Redmon, launches its global Data Science for Good program.
Network Assurance with Machine Reasoning and Machine Learning
The AI technology landscape includes both ML and MR. The two provide complimentary capabilities and combined together they enable AIOps to cover all aspects of network assurance. ML captures the patterns in the data, and MR captures human expertise, facts and logic.