Plan a Digital Analytics Training Strategy for an Analytics AgencyPhil Pearce
This was the 2nd draft of a plan to develop a "training curriculum" for a Digital Analytics Agency to teach:
- Digital Analytics strategy
- GA & GTM implementation
- Reporting & Analysis best practices
To clients & other agencies with various levels of expertise, who could be project manager, marketers or developers.
This is the post-project summary of a 3 month SEO & Analytics setup for a publishing client. The outcome was 25% organic growth in 3 months! I explain how this was achieved…
Morphing GA into an Affiliate Analytics MonsterPhil Pearce
How to hack GA's native campaign tracking, leverage 1st party cookie power and align GA's sessionisation logic more closely with 30 day affiliate systems.
Plan a Digital Analytics Training Strategy for an Analytics AgencyPhil Pearce
This was the 2nd draft of a plan to develop a "training curriculum" for a Digital Analytics Agency to teach:
- Digital Analytics strategy
- GA & GTM implementation
- Reporting & Analysis best practices
To clients & other agencies with various levels of expertise, who could be project manager, marketers or developers.
This is the post-project summary of a 3 month SEO & Analytics setup for a publishing client. The outcome was 25% organic growth in 3 months! I explain how this was achieved…
Morphing GA into an Affiliate Analytics MonsterPhil Pearce
How to hack GA's native campaign tracking, leverage 1st party cookie power and align GA's sessionisation logic more closely with 30 day affiliate systems.
The Kamasutra of GTM container positionsPhil Pearce
As recommended position of the GTM container has changed & this has caused some confusion. Hence, I created these diagrams explain how to optimise your container making experience...
Phil recently completed a 400 man-hours GTM project & shares lessons learned. Migrating from GA Classic to Universal on 6 CMS platforms and 600 GA classic events is one thing, but facing a fine if the project is not complete within 3 months ads a touch of spice! Phil cleaned-up 2 years of in-house changes, including changes such as consolidated 74 pageview tags and centralizing 20 tags into easy to mange lookup table.
Phil provides Technical insights for Advanced Implementers, aswell as Tactical insights for project managers & business people on area such as QA automation, mistakes to avoid, process examples & knowledge sharing tips.
Take-aways:
- QA tool
- Planning tools
- Free GTM developer guide
This audit was conducted using publicly available data from GoogleNews, Adword KW tool, AHREF.com, MyWOT.com & other web content sources.
It was designed to find any possible “holes in the armour” and thus strength these holes.
You have my permission to use this template to help understand & strength other vendors tool.
Thanks
Phil
SEO analytics: How to report & improve performancePhil Pearce
This was slides from the Bath Digital Analytics meetup on how to report & improve SEO performance.
It also has tips for customChannel groupings.
Thanks
Phil.
Explaining the Rise of JSON-LD (machine readable JS data). Why its important and how to make sure your website has enabled…
future action buttons.
* Recent changes & examples in the wild
* Live demo of Googles mark-up validator
* GTM config files to take away & enable.
Analytics & Optimisation for University sitesPhil Pearce
Looking at the growing importance of Analytics, and Pitfalls to avoid, Quick wins, CMS specific issues, Internal issues (skills shortage or lack of inhouse buy-in), responsive web design an importance of Paid search in the awareness process.
Examples of overcoming objections and misconceptions about Google Tag Manager. Including overview of the settings for:
1. Security
2. Deployment Costs
3. Marketing Agility
4. Customer understanding
5. Advertiser Spend
Thanks
Phil.
Digital analytics upskilling & career tipsPhil Pearce
From Bristol Digital Analytics meetup on career.
We covered desirable Digital Analytics skills, Certifications, Training, Mentoring & Industry Salary surveys.
Thanks
Phil.
Google Tag Manager Flash Tips @ MeasureCampPhil Pearce
A list of "quick tips" for Google Tag Manager.
Please watch the video that accompanies this session:
https://www.youtube.com/watch?v=QX5eDg-Ti9Y
Thanks
Phil.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
The Kamasutra of GTM container positionsPhil Pearce
As recommended position of the GTM container has changed & this has caused some confusion. Hence, I created these diagrams explain how to optimise your container making experience...
Phil recently completed a 400 man-hours GTM project & shares lessons learned. Migrating from GA Classic to Universal on 6 CMS platforms and 600 GA classic events is one thing, but facing a fine if the project is not complete within 3 months ads a touch of spice! Phil cleaned-up 2 years of in-house changes, including changes such as consolidated 74 pageview tags and centralizing 20 tags into easy to mange lookup table.
Phil provides Technical insights for Advanced Implementers, aswell as Tactical insights for project managers & business people on area such as QA automation, mistakes to avoid, process examples & knowledge sharing tips.
Take-aways:
- QA tool
- Planning tools
- Free GTM developer guide
This audit was conducted using publicly available data from GoogleNews, Adword KW tool, AHREF.com, MyWOT.com & other web content sources.
It was designed to find any possible “holes in the armour” and thus strength these holes.
You have my permission to use this template to help understand & strength other vendors tool.
Thanks
Phil
SEO analytics: How to report & improve performancePhil Pearce
This was slides from the Bath Digital Analytics meetup on how to report & improve SEO performance.
It also has tips for customChannel groupings.
Thanks
Phil.
Explaining the Rise of JSON-LD (machine readable JS data). Why its important and how to make sure your website has enabled…
future action buttons.
* Recent changes & examples in the wild
* Live demo of Googles mark-up validator
* GTM config files to take away & enable.
Analytics & Optimisation for University sitesPhil Pearce
Looking at the growing importance of Analytics, and Pitfalls to avoid, Quick wins, CMS specific issues, Internal issues (skills shortage or lack of inhouse buy-in), responsive web design an importance of Paid search in the awareness process.
Examples of overcoming objections and misconceptions about Google Tag Manager. Including overview of the settings for:
1. Security
2. Deployment Costs
3. Marketing Agility
4. Customer understanding
5. Advertiser Spend
Thanks
Phil.
Digital analytics upskilling & career tipsPhil Pearce
From Bristol Digital Analytics meetup on career.
We covered desirable Digital Analytics skills, Certifications, Training, Mentoring & Industry Salary surveys.
Thanks
Phil.
Google Tag Manager Flash Tips @ MeasureCampPhil Pearce
A list of "quick tips" for Google Tag Manager.
Please watch the video that accompanies this session:
https://www.youtube.com/watch?v=QX5eDg-Ti9Y
Thanks
Phil.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
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
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).