This document summarizes building a data solution for a telecom company. The objectives were to personalize recommendations, increase customer retention and loyalty. The data included large volumes of static and dynamic customer data. The pipeline involved clustering customers into groups, identifying high and low profit customers as outliers, and feature selection before building learning models. Insights from the models included clustering customers by revenue, profit, usage, discounts and costs to identify optimization opportunities.
The Business Analytics Value PropositionEric Stephens
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
The Business Analytics Value PropositionEric Stephens
Presentation made to the Nashville Technology Council Analytics Peer Network meeting on May 30, 2013. Discussion of the impact of analytics to an organization, along with use cases that can help convey the value of the practice to executives and other managers.
Machine intelligence data science methodology 060420Jeremy Lehman
Machine learning and artificial intelligence project methodology that focuses on business results, builds alignment across the entire business, and forms enduring capabilities.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Data Science Introduction by Emerging India AnalyticsAyeshaSharma29
This is the data science basic introduction which covers Big data ,machine learning including supervised machine learning & unsupervised machine learning. This presentation also covers Hadoop tool and its landscape. This will help in deciding where to start your career in data science. It has all the skills you require to build a career in data science industry.
Big data includes large volumes of data, both unstructured and structured,however the volume of data is not important but the execution is. How organization's perceive those data and implements the understanding, resulting in change- is what matters. HashCash Consultants assists organization's to analyze the data for insights that result in better decisions and strategic business moves.
Machine intelligence data science methodology 060420Jeremy Lehman
Machine learning and artificial intelligence project methodology that focuses on business results, builds alignment across the entire business, and forms enduring capabilities.
Predictive Analytics & Decision Solutions [PrADS], a subsidiary of Dun & Bradstreet provides cutting edge analytics solutions and actionable insights to leading organizations globally , The following presentation provides an overview of the services offered
Presentation on "A Complete Overview of Data Driven Decision Making in a Quickly Changing Business Environment" given by Isaac Aidoo, Head of Data Analytics, Zoona.
A lack of trust is inhibiting the adoption of #AI. This presentation discusses approaches to delivering trusted data pipelines for AI and machine learning
Data Science Introduction by Emerging India AnalyticsAyeshaSharma29
This is the data science basic introduction which covers Big data ,machine learning including supervised machine learning & unsupervised machine learning. This presentation also covers Hadoop tool and its landscape. This will help in deciding where to start your career in data science. It has all the skills you require to build a career in data science industry.
Big data includes large volumes of data, both unstructured and structured,however the volume of data is not important but the execution is. How organization's perceive those data and implements the understanding, resulting in change- is what matters. HashCash Consultants assists organization's to analyze the data for insights that result in better decisions and strategic business moves.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
1. Story of Building a Telecom
Data Solution
Sawinder Pal Kaur, PhD
Data Scientist, SAP Labs
2. Outline
1. Define business objectives and translating business
problem into data science problem
2. Introduction to Telecom data - data scale, volume,
continuous and categorical variables, static and dynamic
data
3. Architecture and data processing pipeline: Big data
handling and data science methods for Categorical
feature selection
4. Solution Engineering: How to keep project managers do
feature selection and identify the opportunities to
optimize the existing plans and services?
4. Business Objective
• Personalize
recommendation
• More customer satisfaction
• Improved Customer
retention
• Increased frequency of
selling
• Better mix of products
• Increased customer loyalty
• Better decision on coupons
and discounts
• Develop effective strategy for
new product launches
• Better offers to specific
customer profile
• Better product design /
pricing
• Improve quality of service
for highest margin
customers
• Invest where highest
margin customers are
using the network
resources
Recommend
Plans and Services
Grouping/
Clustering
Identify Profit
Maximization
Opportunities
6. Data
• How much data is available?
• Data infrastructure
• Data dashboards
• Data preparation for
Machine learning
• Data protection and privacy
7. Partitioning the data into similar groups
Multi dimensional clustering
Grouping customers-
One dimensional
binning/clustering
8. High, low, and normal
profitable customers -
One dimensional outlier
detection
Multi dimensional outlier detection
9. • Dealing with missing –
• Delete the rows with missing
• Replace missing using
• mean/median
• Other number
• Conditional mean
• Model like K nearest neighborhood
10. • Filter Methods – used as independent feature selection e.g.
Pearson correlation, Mutual Information, MRMR
• Dimensionality reduction – PCA, Variational autoencoder
• Feature Engineering
• Creating new variables – Polynomials, Interaction variables, Ratios
• Wrapper and Embedded methods - used in the model building
process
Feature
selection
Base set
Learning
Model
Performance