At ReputationDefender I serve as Managing Director with responsibility for P&amp;L ownership, global sales, operations, marketing, product, and go to market strategy. Our work centers on protecting leaders in high visibility roles who face real reputational and privacy risk. For many of our clients, trust and discretion are not optional. They are the entire mandate.My focus at ReputationDefender is centered on three priorities that define how we operate:1. Protecting clients who face constant public scrutiny and need a trusted partner when the pressure is highest2. Advancing a product suite that keeps pace with the speed of digital risk and the complexity of modern search, social, AI, and underground data exposure3. Building a scalable commercial and operational model that delivers a consistent and high touch client experience across executives, board directors, family offices, and public figuresAlongside this mission I run the business. I unify GTM, product, operations, marketing, and forecasting into one operating system built for predictable growth. I drive clarity and accountability across the organization. I develop leaders who can support turnaround environments. And I install the discipline and inspection rhythm required for sustained performance.My background spans Fortune 250 companies, PE backed transformations, and technology organizations where execution matters. I have led teams of more than two thousand employees. I have built large scale outbound engines responsible for significant new revenue growth. I have rebuilt commercial and operational systems inside digital marketplaces and BPO environments. I have strengthened partnerships with law firms, financial institutions, and governance leaders who rely on us to manage digital visibility, misinformation, and privacy exposure.A few experiences that shape how I operate today:• Graduate of the Stanford Executive Program with a focus on global leadership• Participant in the FBI Citizens Academy with emphasis on cybersecurity and digital risk• Leader of multiple large scale turnarounds across public, private, and PE owned companies• Advisor to executives navigating complex visibility and reputation challengesWhether I am guiding a client through a sensitive visibility issue or leading a commercial transformation, my approach stays consistent. Clear strategy. Strong execution. The right people in the right roles.If you are navigating digital visibility, reputational risk, or broader commercial challenges, I am always open to connecting.

Chad Angle

Great to be in San Diego this week at the American Bar Association White Collar Crime Conference.

If you’re here, stop by and say hello. Our team is offering complimentary professional headshots throughout the event. Always happy to help lawyers upgrade their LinkedIn photo.

A big theme already emerging in conversations this morning is how much exposure attorneys themselves are facing online. Many are asking about ways to protect not only their own privacy, but also their spouses and children as their public profiles grow.

We’re also seeing strong interest in:
🔎 Reputation repair when damaging or inaccurate information spreads online
🛡️ Executive privacy protection for attorneys, their families, and their clients

Both issues often surface long before a matter is resolved and increasingly need to be addressed alongside the legal strategy.

If you’re around the conference, come meet the team or stop by for a headshot.

If you want to grab a few minutes while we’re here, feel free to send me a DM.

#WWCCI2026 #WhiteCollarDefense #CorporateInvestigations #ExecutivePrivacy #OnlineReputation

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The common thread across all of these wasn’t the mistake. It was the response speed.

ReputationDefender by Norton 

Gen™ 

American Bar Association

Managing Director | Executive Reputation Risk, Online Defamation & Digital Privacy | Supporting Defense Counsel and Boards in High-Exposure Reputation Matters

Spent the week at the American Bar Association’s White Collar Crime Institute.

It’s one of the few conferences where you can have serious conversations with defense attorneys, investigators, and advisors all thinking about the same problem from different angles.

One theme that kept coming up in hallway conversations:

Reputation risk now moves faster than the legal process.

Issues often surface first in search results, forums, or AI summaries before they ever show up in a courtroom or a headline.

For attorneys handling investigations and high-stakes matters, that online layer is becoming part of the strategy whether they want it to be or not.

Great conversations all week and always interesting to see where the profession is headed.

#ABA #WhiteCollarCrime #ReputationRisk #LegalStrategy #ExecutiveRisk

Cyber risk is no longer just about protecting systems.

It’s about protecting decision integrity.

Boards used to ask:
“Are our controls strong enough?”

Now the question is:
“Can we trust what we’re seeing, hearing, and acting on?”

AI can clone voices.
Synthetic identities can pass verification checks.
Narratives can spread before facts catch up.

When leadership communications can be impersonated, the risk moves beyond operations. It becomes governance.

This isn’t about chasing every new threat. It’s about asking sharper questions:

🔐 Who owns authenticity during a crisis?
🚨 How quickly can misinformation reach the board?
🗣️ What verification standards protect leadership communications?
🛡️ Are directors personally protected in ways that align with enterprise risk?

Digital risk now impacts credibility, fiduciary responsibility, and institutional trust.

That’s a board-level discussion.

🌐 Cyber risk in 2026 won’t just target your systems. It will target you. 🎯

AI can already clone voices and simulate video. Synthetic identities are sophisticated enough to pass verification checks. Misinformation spreads faster than facts.

For non-executive directors, this shifts cyber from an operational concern to a governance risk.

If leadership communications can be impersonated…
If board decisions can be distorted online…
If directors’ digital identities can be replicated…

A new article, written by Chad Angle at Reputation Defender by Norton for NEDonBoard, outlines 5️⃣ cybersecurity predictions every board must understand.

If you sit on a board, click on the link to access the article now. 

https://lnkd.in/eFd9q6jv

#NonExecutiveDirectors #Leadership #Boards #BoardDirectors #Directors #Cyberthreat #Cybersecurity #Cyberresilience

Managing Director | Executive Reputation Risk, Online Defamation & Digital Privacy | Advising Boards and Counsel on High-Exposure Matters

Quick question for board directors...

If someone asked AI to summarize your background today, what would it say?

Not what you hope.
What it would actually say?

Across Europe I am seeing directors run digital exposure reviews before accepting board seats.

That is governance evolving in real time.
🌍 Digital footprint is now part of fiduciary responsibility.
🛡️ Executive privacy is becoming a board level conversation.

The risk is personal.
The impact is corporate

Breaking: Netflix pulls its bid for Warner Bros. Discovery.

After the board deemed Paramount’s $31 per share offer superior, Netflix walked, calling the deal no longer financially attractive.

The stock jumped!

Hot take:
This was not about losing a bidding war.
It was about refusing to inherit legacy drag.

$31 was not just a higher price.
It meant owning the full linear bundle in 2026.
That is not scale. That is margin pressure.

The streaming wars just shifted.
It is no longer land grabs.
It is structure, cash discipline, and long term operating leverage.

Structure decided this, not ego.

Netflix

Warner Bros. Discovery

Paramount

Preparing for a white collar crime compliance event where regulatory violations and reputational damage intersect. Executives managing FINRA infractions face compounding risks: immediate regulatory consequences plus lasting stakeholder trust erosion. The conversation around proactive reputation recovery is critical for boards overseeing compliance frameworks.

For those navigating these complex challenges, the real value lies in integrated approaches that address both regulatory remediation and stakeholder confidence simultaneously.

This is the type of work that makes me proud to be part of Gen.

Scams are no longer a niche cybersecurity problem. They affect families, retirees, small business owners, and executives every single day.

Putting real time scam detection directly into a tool people already use lowers the barrier to protection and gives people help in the exact moment they need it.

Important work. Real impact.

In our mission to make the digital world safer, Gen has integrated the world’s first AI-powered anti-scam and cyber safety assistant, Norton Genie, into ChatGPT.

We’re bringing real-time scam detection and practical security guidance directly into one of the most widely used AI platforms in the world.

OpenAI makes the world more intelligent. Gen ensures everyone stays safe.
Second coffee’s on me, Sam Altman.

#TrustAI #NortonGenie #GenAIFoundry

Reputation impacts revenue in ways most dashboards miss.

Consider:
📈 Higher trust reduces sales friction
⏳ Cleaner search shortens deal cycles
🤝 Strong executive presence improves partnership velocity

You cannot always measure the lift.
You absolutely feel the drag.

In tight years, operational leaders look for hidden friction.

Digital reputation is often one of them.

Ignore it and you tax your own growth.

In San Diego this week for the White Collar Crime Institute.

Most people think reputation risk peaks at indictment.
It does not.
It peaks after resolution.

When the case is over and page one still reads:
⚖️ Fraud conviction
📉 FINRA violation
🏛️ Regulatory sanction
📑 Deferred prosecution agreement

That page one becomes the long tail of the case.

Permanent. Indexed. Searchable.
That is where we work.

Not changing facts.
Managing how those facts live online long after court adjourns.

The courtroom closes.
The internet does not.

#WCCI26 #ABAWhiteCollarCrime #WhiteCollarDefense

Private equity is not broken.
The easy money era is over.
The operator era is here.

Bain & Company’s latest report shows distributions near 2008 lows. Roughly $3.8T of unsold assets sit on balance sheets. Exits remain constrained. LPs are more selective.

That is not collapse. It is a reset.

When distributions stall and exits slow, three things matter more than ever:
📈 Real EBITDA growth, not multiple expansion
🏛️ Governance discipline, boards that ask harder questions
🛡️ Executive risk management, protecting the leaders who carry the valuation narrative

In tight capital cycles, perception affects valuation more than most models admit.

I have worked alongside boards, PE backed operators, and executives navigating scrutiny, litigation, and public visibility. One pattern is consistent:
Stress exposes fragility.

The firms that win this cycle will not be the most aggressive.
They will be the most operationally mature.

Capital will continue to flow.
It will simply flow toward disciplined execution.

#PrivateEquity #ValueCreation #CorporateGovernance #OperationalExcellence #CapitalMarkets

Bain & Company

Last week, Netflix was expected to win Warner Bros. Discovery.
Today, Paramount Skydance owns it in a $110B transaction.

If this were an MBA case study, here is what we would dissect.

1. This was leverage battle.
Netflix entering the process changed price psychology overnight. The moment a credible buyer steps in, the asset reprices. Paramount did not simply outbid Netflix. They had to outstructure them.
💡Lesson: The first serious bidder sets the anchor. The second bidder often sets the ceiling.

2. Structure beats headline price.
Termination fees. Regulatory protections. Ticking fees. Debt assumption.
This was not about $31 per share. It was about certainty of close. Boards choose deals that can survive regulators and financing friction.
💡Lesson: Best and final means nothing if it cannot close.

3. Netflix showed discipline.
They had matching rights. They walked.
That tells you their internal model broke before their ego did.
💡Lesson: Winning the asset and winning the market are not the same thing. Discipline compounds.

4. Debt is the real story.
The deal includes roughly $29B in debt. This is a balance sheet bet.
The combined entity now must integrate cultures, merge streaming platforms, rationalize content spend, cut costs, and navigate regulators, all while competing with the largest global streamer.
💡Lesson: Post merger execution risk is always underestimated during announcement week.

5. Regulatory modeling is now core strategy.
Antitrust scrutiny is no longer a side variable. It directly affects valuation and structure.
💡Lesson: In modern M&A, regulatory probability is as important as revenue projections.

Big takeaway for future M&A:
The highest bidder rarely wins long term.
The durable winner is the buyer that can absorb integration risk without destabilizing its core engine.

And sometimes the most strategic move in a bidding war is walking away.

If you were in the boardroom, how would you grade Netflix’s exit?
Was Netflix disciplined, or did it surrender strategic optionality?

#MergersAndAcquisitions #CorporateStrategy #CapitalAllocation #StreamingWars #Boardroom

Skydance

Reputation is no longer a PR function. It is a risk category.

Here is what has changed:
📊 Boards now ask about digital exposure during risk reviews
🤖 AI systems summarize leaders before investors do
⚖️ Activists use search results as leverage
💰 Investors screen executives online before meetings
📉 Sales cycles slow when leadership surfaces noise

Reputation now impacts valuation, capital cost, trust velocity and hiring strength.

If something can affect revenue, it is not communications. It is operations.

The organizations that treat reputation as infrastructure instead of optics will outperform.

That shift is already underway.

I’ll be in San Diego March 10–13 for the ABA White Collar Crime Institute.

In nearly every major investigation today, the legal strategy runs parallel to a digital reality.

🔎 Search results don’t wait for verdicts.
📰 Headlines outlive settlements.
🤖 AI amplifies narratives long after cases close.

Even when matters resolve favorably, the online footprint often doesn’t.

I’m looking forward to connecting with white collar defense attorneys, in-house counsel, and compliance leaders who are thinking about reputational exposure during investigations, executive privacy in high-visibility matters, and what happens online after the legal work is done.

If you’ll be at #WCCI26 and want to connect, send me a message.

#WhiteCollarDefense #ExecutivePrivacy

Last week, Anthropic drew a red line.
OpenAI signed the deal.

Within hours:
⚖️ Employees questioned leadership
📣 Critics accused OpenAI of compromise
📲 Users downloaded Claude in protest
📊 App Store rankings shifted overnight

This isn’t just an AI ethics story.
It’s a case study in executive decision-making under public scrutiny.

Three things boards should be watching:
!. Values vs. law
There’s a difference between complying with existing regulations and publicly setting a higher standard. Once a company signals a principle, walking it back becomes a reputational event.
2. Talent risk
In AI, your workforce is your moat. If employees believe leadership crossed a moral boundary, retention becomes a strategic issue, not an HR issue.
3. Narrative velocity
Public opinion now moves faster than contracts. Anthropic positioned itself as values-first. OpenAI positioned itself as pragmatic. The market responded immediately.

Whether you agree with either company is almost beside the point.

In the AI era, governance decisions are brand decisions.
And brand decisions are financial decisions.

The next generation of AI leaders won’t just be measured by model performance. They’ll be measured by moral consistency.

🔗 Bloomberg Opinion: https://lnkd.in/gnswWeCE

Anthropic

OpenAI

Online Reputation Management is not one business.

It is four:
⭐ Review Management
🔎 Monitoring
📈 Search Suppression
📄 Legal grade Removal

Each has different margins.
Different complexity.
Different operational requirements.

Blurring them together leads to bad pricing and weak strategy.

Strong operators choose their segment intentionally.

A former colleague told me the other day how rough the job market feels right now.

Plenty of talented people. Fewer openings. Longer hiring cycles.

Which raises an interesting question: what actually stands out when leaders evaluate candidates?

I was recently asked by Forbes what trait matters most when hiring today.

My answer was simple: ownership.

Skills evolve and roles change, but people who take responsibility for outcomes stay valuable. They seek context, act without waiting to be told, and surface problems early with solutions.

What I liked about this panel was the range of perspectives from leaders across industries. A few that stood out to me:
🧠 Systems thinking — Khadija from Grupo Unicomer / Unicomer Group emphasized connecting everyday decisions to broader business outcomes. That mindset compounds performance across teams.
💪 Resilience and grit — Julie Michael from Team One highlighted the importance of people who can recover quickly when conditions shift. In fast-moving environments, that trait is invaluable.
🤝 Servant leadership — Kent Ingle from Southeastern University shared a perspective that leadership is fundamentally about lifting others. Teams that operate that way tend to outperform over time.

Different industries. Different perspectives. But a common theme: how someone thinks and shows up matters more than any single technical skill.

Worth the read if you're thinking about how hiring is evolving.

Curious what others prioritize when hiring. What trait stands out to you every time?

#Leadership #Hiring #TalentStrategy #ExecutiveLeadership #Management

social-www.forbes.com

20 Skills And Traits Smart Leaders Look For In Every New Hire

Forbes

Award-Winning Global Thought Leader & Senior Director | Accelerating Workplace Performance & Strategy via Organizational Effectiveness, Technology & Business Transformation | Cambridge ExecutiveMBA | Forbes Council

Grupo Unicomer / Unicomer Group

CEO at Team One, Publicis Groupe

Team One

President at Southeastern University

Southeastern University

Most defense attorneys still think reputation management is “snake oil.”

I heard that more than once last week at the ABA White Collar Crime Conference.

But in high-profile litigation, something happens before any motion is filed or discovery begins.

People Google the defendant.
⚖️ Jurors
📰 Reporters
🏛️ Regulators
🧑‍⚖️ Opposing counsel

Those search results quietly become the first dossier in the case.

Not because they’re supposed to.
Because they’re available.

After the conference, I wrote a short piece on why digital exposure is increasingly shaping litigation strategy and outcomes.

#WhiteCollarDefense #LitigationStrategy #ReputationRisk

theleaderreport.com

The Hidden Risk in High-Profile Litigation: Digital Exposure - The Leader Report

A federal lawsuit is usually a legal event.
In the AI era, it is also a reputational event.

Anthropic suing the Pentagon (United States Department of War) over a “supply chain risk” designation may end up becoming a defining governance moment for the AI industry.

That label is rarely applied to U.S. companies. When it appears, it immediately changes how governments, partners, and investors assess risk.

We have seen the ripple effects before with companies like Huawei, TikTok, and Kaspersky. The legal fights played out in court, but the reputational consequences moved much faster.

The courtroom debate here will focus on administrative law, due process, and constitutional claims.

Outside the courtroom, the question will be simpler.
Who controls the narrative around AI risk?

Once disputes like this move from internal policy debates to federal court filings, they become global headlines overnight.

Watching this one closely.

If the government can apply national security designations to domestic AI firms, where should the guardrails be?

#AI #ArtificialIntelligence #Governance #NationalSecurity #TechPolicy

United States Department of War

Huawei

TikTok

Kaspersky

Yesterday I was featured in Forbes Biz Pulse alongside several cybersecurity leaders.

What stood out wasn’t the individual articles.
It was the pattern.

AI is making attacks easier to scale.
Human error is still driving most outcomes.
And leadership teams are now directly exposed.

From where I sit, this is the real shift:
Cybersecurity is moving from a systems problem to a trust problem.

When AI can impersonate leadership and shape narratives in real time, the question is no longer just “are we secure?”

It is “are we still credible?”

Different perspectives. Same conclusion.

The surface area of risk is expanding.

And trust is now part of the attack surface.

#Cybersecurity #AI #BoardGovernance #RiskManagement #Trust

https://lnkd.in/gzX_ceaZ

Forbes Business Council

Cybersecurity’s Trust Crisis

The internet does not operate on the same timeline as the legal system.

Courts evaluate evidence.
Search engines surface narratives.

After a week of conversations with white collar defense attorneys, one theme stood out:

Many cases are now influenced by what appears on page one of Google long before trial.

Look at what’s happening right now in the market.

Executives tied to regulatory investigations or enforcement actions are being defined by:
🔍 headlines
📄 summarized allegations
🤖 AI-generated overviews

Often before any formal resolution.

That digital narrative shapes media coverage, influences stakeholder pressure, and can even impact settlement posture.

Long before a court reaches a conclusion.

The legal system determines the outcome.

The internet often shapes the environment around it.

Dr Emmanuel Ogungbemi

Head of Data Engineering | I help you improve your productivity with data and AI

From being a data analyst to leading the development of data pipelines, my journey in data engineering and analytics has been both challenging and rewarding.

When I began my career in data, I faced the daunting task of turning vast amounts of raw data into actionable insights.

The problem? Many organisations struggle with fragmented data and inefficient processes.

Here's how I tackled these challenges and built a successful career in data:

1. Crafted Data Solutions: Designed and implemented a Common Data Platform, ensuring seamless data integration and access.
2. Led Innovative Projects: At NATS, I contributed to the world's first AI system for air traffic control, utilising digital twinning and machine learning.
3. Developed Future Talent: Shaped data science and engineering education at Multiverse by developing comprehensive curricula for degree-level apprenticeships.
4. Consulted on High-Impact Projects: Provided AI and data science consultancy for industry giants like BP, Sword and Chaucer (now BIP UK), driving data-driven decision-making and innovation.

Now, I'd like to talk about you.

Are you aspiring to become a successful data analyst/data scientist/data engineer but need help figuring out where to start?

Here's a roadmap to guide you:

1. Master the Fundamentals:
- Learn key programming languages like Python and SQL.
- Understand data structures, algorithms, and database management.

2. Gain Practical Experience:
- Work on real-world projects to apply your skills.
- Use tools like Power BI and Tableau for data visualisation.

3. Join the Community:
- Participate in data science forums and attend industry events.
- Network with professionals to exchange knowledge and opportunities.

4. Continuous Learning:
- Stay updated with the latest trends and technologies in data analytics.
- Take advanced courses to deepen your expertise.

To help you get started, I've created a comprehensive Data Analyst Roadmap that outlines the essential steps and resources you need to succeed.

Join my email list to register your interest in my free Data Analyst Roadmap.

You have the potential to excel in the data industry, and I'm here to support your journey.

Let's connect and share our experiences in data engineering and analytics!

Head of Data Engineering

Excited to share an essential resource: Linear Regression!

This comprehensive guide delves into the fundamentals and advanced techniques of linear regression. 

Perfect for students, data scientists, and anyone interested in understanding this crucial statistical method, this document provides detailed explanations and practical examples.

Key topics include:
- Introduction to Linear Regression: Understanding the basics and importance of linear regression in data analysis.
- Mathematical Foundations: A detailed explanation of the linear regression model, including equations and assumptions.
- Data Visualization: The Importance of Visualizing Data and Interpreting Linear Regression Results.
- Model Evaluation: Techniques for evaluating the performance of linear regression models.
- Practical Applications: Real-world examples and applications of linear regression in various fields.


Enhance your understanding of linear regression and apply this knowledge to your data analysis projects with this detailed guide!

- Repost to share with others
- Save to revisit down the road
- Add your comment!

Dr Emmanuel O.

I help you break into data science and AI with practical tips, real-world insights, and the latest trends.

Linear Regression

Excited to share an essential resource: Python Machine Learning!

This comprehensive guide delves into the fundamentals and advanced techniques of machine learning using Python. 

Perfect for students, data scientists, and anyone looking to enhance their understanding of machine learning, this document provides detailed explanations and practical examples.

Key topics include:
- Getting Started with Python Machine Learning: Introduction to machine learning and Python, essential libraries, and the basics of data handling.
- Classification with Real-world Examples: Using the Iris dataset, visualization techniques, building and evaluating classification models, and cross-validation.
- Clustering: Finding related posts, preprocessing text data, using bag-of-words and KMeans clustering.
- Topic Modeling: Latent Dirichlet Allocation (LDA), building and evaluating topic models, and applying them to real-world datasets.
- Regression and Recommendations: Building recommendation systems, using regression models, and improving recommendations with advanced techniques.
- Computer Vision and Pattern Recognition: Introduction to image processing, pattern recognition, and building classifiers for image data.
- Dimensionality Reduction: Techniques for feature selection and extraction, principal component analysis (PCA), and other dimensionality reduction methods.
- Handling Big Data: Strategies for processing large datasets, using tools like Jug and Amazon Web Services (AWS).


Enhance your machine-learning skills and apply this knowledge to real-world projects with this detailed guide!

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- Save to revisit down the road
- Add your comment!

Building Machine Learning Systems with Python

Understanding SQL: How We Write It vs. How It's Executed

Have you ever wondered why your SQL queries don't always run as expected? 
- There's a difference between how we write SQL and how the database processes it. 

Let's break it down:

When we write SQL queries, we typically follow this sequence:

- SELECT
- TOP
- DISTINCT
- FROM
- WHERE
- GROUP BY
- HAVING
- ORDER BY
- LIMIT

Execution Order:
However, the database engine processes these commands in a different order:

- FROM - Identifies the data source.
- WHERE - Filter records based on conditions.
- GROUP BY - Aggregates data into groups.
- HAVING - Filter groups based on conditions.
- SELECT - Retrieves specified columns.
- ORDER BY - Sorts the result set.
- TOP/LIMIT - Limits the number of rows in the result set.

Why This Matters:
- Misunderstanding the execution order can lead to unexpected results or inefficient queries.
- Knowing the execution order helps you write more effective and optimized SQL queries.
- Improved query performance and more accurate data retrieval.

Tips for Writing Efficient SQL:
- Always start by understanding your data and defining clear requirements.
- Use the execution order to optimize your queries for better performance.
- Practice writing and analyzing different queries to see how changes affect execution.

Mastering the execution order of SQL not only makes your queries more efficient and enhances your ability to troubleshoot and optimize complex data operations.

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Querying data with SQL

I am excited to share an essential resource: Exam Ref 70-761, Querying Data with Transact-SQL by Itzik Ben-Gan!

This comprehensive guide is perfect for anyone preparing for the Microsoft Exam 70-761, covering key topics in T-SQL querying and programming constructs. 

Whether you're a developer, DBA, or data analyst, this book provides the knowledge and practice you need to succeed.

Key topics include:
- Creating Transact-SQL SELECT Queries: Understanding logical query processing, filtering data, sorting data, and combining sets with set operators.
- Querying Multiple Tables Using Joins: Cross joins, inner joins, outer joins, and composite joins.
- Implementing Functions and Aggregating Data: Type conversion functions, date and time functions, character functions, CASE expressions, and aggregate functions.
- Modifying Data: Inserting, updating, deleting data, merging data, and using the OUTPUT option.
- Querying Data with Advanced T-SQL Components: Subqueries, APPLY operator, table expressions, pivoting, and window functions.
- Programming Databases Using Transact-SQL: Creating views, user-defined functions, stored procedures, error handling, transactions, and handling NULLs.


Enhance your T-SQL skills and prepare effectively for your certification with this detailed guide!

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- Save to revisit down the road
- Add your comment!

Querying Data with Transact-SQL

Understanding the key terms in data analysis is essential for anyone working with data. 

This concise guide breaks down fundamental concepts, making it easier for you to navigate the world of data. 

Whether you're a beginner or looking to refresh your knowledge, this resource is perfect for you.

Key terms include:
- Data Profiling: Examining and summarizing data characteristics
- Data Interpretation: Making sense of data and deriving insights
- Data Manipulation: Transforming data for analysis
- Exploratory Data Analysis (EDA): Visualizing data to uncover patterns
- Data Mining: Discovering patterns in large datasets
- Data Storytelling: Conveying insights in a compelling narrative
- Data Visualization: Graphical representation for better understanding
- Data Validation: Ensuring data accuracy and reliability

Master these terms and enhance your data analysis skills!

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Data Analysis Vocabulary

Excited to share an invaluable resource: Docker Tutorial by Anthony Baire!

This comprehensive tutorial covers everything you need to know about Docker, from the basics of managing containers to advanced security and ecosystem considerations. 

Whether you're new to Docker or looking to enhance your skills, this guide provides clear and detailed insights into effective containerization practices.

Key topics include:
- Introduction to Docker and its components
- Managing Docker containers: creation, interaction, and lifecycle
- Handling inputs/outputs and data volumes
- Building and managing Docker images
- Security considerations and best practices
- Exploring the Docker ecosystem and future trends


Unlock the full potential of Docker for your projects with this detailed tutorial!

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Docker Tutorial

Struggling with Data Management? Discover SQL!

Managing and analyzing data effectively is crucial. Are you finding handling large datasets challenging or generating meaningful insights from your data?

Here’s a powerful tool that can help: SQL.

1. What is SQL?
- Structured Query Language (SQL) is a standard language for accessing and manipulating databases.
- It lets you retrieve, update, and manage data in a relational database.
- SQL is widely used across various industries for data analysis, reporting, and business intelligence.

2. Why Learn SQL?
- Efficient Data Handling: Quickly query and manipulate large datasets.
- Versatile Skills: SQL is used in various fields, from web development to finance.
- Career Growth: High demand for SQL skills in the job market.

3. Getting Started with SQL
- Basics First:
Learn fundamental commands like SELECT, INSERT, UPDATE, and DELETE.
Understand how to create and manage database tables.

- Practice with Real Data:
Use sample databases to practice your queries.
Explore platforms like Kaggle for datasets to work on.

- Advanced Techniques:
Dive into complex queries with JOIN, GROUP BY, and SUBQUERIES.
Learn about database optimization and indexing.

4. Why You Should Care:
- Problem: Handling data manually is time-consuming and prone to errors.
- Solution: SQL provides a systematic approach to data management, making it easier to analyze and derive insights.
- Value: Mastering SQL can streamline your data processes, improve accuracy, and enhance your decision-making capabilities.

SQL is an essential tool for anyone dealing with data.
Start learning today to unlock its full potential and take your data management skills to the next level.

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Basic SQL

Are you struggling to manage and process large volumes of data efficiently? 

Understanding the foundation of data engineering is key to building robust data systems that can handle today's data challenges.

These are essentials in Data Engineering;

1. Data Collection
- Collecting data from diverse sources can be chaotic and inconsistent.
- Use ETL (Extract, Transform, Load) processes to streamline data collection.
- Ensures that data is gathered in a structured and reliable manner.

2. Data Storage
- Storing vast amounts of data securely and accessibly can be daunting.
- Implement scalable data storage solutions like data lakes and warehouses.
- Provides a central repository that supports high availability and durability.

3. Data Processing
- Processing large datasets can be time-consuming and resource-intensive.
- Utilize distributed processing frameworks such as Apache Spark.
- Enables efficient and quick data processing, even with large volumes of data.

4. Data Quality
- Poor data quality can lead to inaccurate insights and decisions.
- Establish data quality checks and validation procedures.
- Ensures that your data is accurate, complete, and reliable.

5. Data Integration
- Integrating data from various sources can create inconsistencies.
-  Use data integration tools to combine data into a cohesive format.
- Facilitates seamless data analysis and reporting.

6. Data Security
- Protecting sensitive data from breaches and unauthorised access is critical.
- Implement robust security measures, including encryption and access controls.
- Safeguards your data and maintains compliance with regulatory standards.

Why It Matters:
- Without a strong foundation in data engineering, organisations struggle to manage and leverage their data effectively.
- A solid foundation ensures data is collected, stored, processed, and secured properly.
- Leads to better data insights, improved decision-making, and a competitive edge.

Understanding and applying these data engineering fundamentals can transform how you handle data and unlock its full potential.

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Foundations of Data Engineering

Excited to share an invaluable resource: Comprehensive Python Cheatsheet by Jure Šorn!

This extensive cheat sheet covers the fundamental concepts and advanced techniques of Python programming. 

Perfect for beginners and experienced developers, this document provides a detailed reference for a wide range of Python functionalities.

Key topics include:
- Collections: Lists, dictionaries, sets, tuples, ranges, and more.
- Types: Strings, regular expressions, numbers, and datetime.
- Syntax: Arguments, inline functions, closures, decorators, and classes.
- System Operations: Exit, print, input, command-line arguments, file handling, and OS commands.
- Data Handling: JSON, Pickle, CSV, SQLite, bytes, and memory views.
- Advanced Topics: Threading, operators, metaprogramming, evaluation, and coroutines.
- Libraries: Progress bars, plotting, tables, web scraping, profiling, NumPy, and more.


Enhance your Python programming skills and keep this comprehensive cheatsheet as a quick reference guide!

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The award of this certificate is in recognition of my participation in the SEEP 2019 conference held between 18 - 21 November 2019 at the University of Sharjah, United Arab Emirates where I delivered two (2) papers.

Certificate of Participation

The certificate is a result of my participation in the 5th International Conference on Material Science & Smart Materials where I presented a poster titled "Characterisation of Solar Materials and Electronic Devices using Cadmium Telluride (CdTe) and Zinc Sulphite (ZnS) as a Case Studies"

Certificate

The certificate is a result of my participation in the SEEP 2018 conference where I presented a paper titled "Effect of Membrane Thickness on the Performance of PEM Fuel Cell"

Certificate of National Service

I received this letter due to my invaluable contribution to the successful delivery of the first historic comprehensive training programme for the Volunteer Cadets in Nigeria, the OYES Volunteer Cadets' Training Course.  

Letter of Appreciation

The certificate is a recognition of my service to the club as the Vice-President (Business) in 2010.

Certificate of Fellowship

The award is a recognition of my service to the student society as General Secretary - 2007/2008, Vice-President - 2008/2009, President - 2009/2010 and National Vice-President (West) - 2010/2011.

Certificate of Service

This certificate is due to my participation in the 40th annual general meeting and conference of the society.

Certificate of Attendance

I received this certificate as a result of my participation in the international essay competition organised by the World Bank. The title is "How does youth unemployment affect you? How can you tackle youth unemployment through youth-led solutions"

Certificate of Merit

Marketing: Copywriting for Social Media

Developing CI/CD Solutions with Azure DevOps

Python Projects

Microsoft Certified: Azure Data Engineer Associate

Microsoft Certified: Azure AI Fundamentals

Microsoft Certified: Azure Data Fundamentals

Microsoft Certified: Azure Fundamentals

Data Engineering Foundations

[PCAP-31-03] PCAP – Certified Associate in Python Programming

Agile at Work: Building Your Agile Team

Scrum: The Basics

Stay Lean with Kanban

Machine Learning Essentials for Seismic Interpretation

Starting a Memorable Conversation

Artificial Intelligence Foundations: Neural Networks

Diversity, Inclusion, and Belonging

Machine Learning and AI Foundations: Classification Modeling

Body Language for Leaders

Industrial/Oilfield Health, Safety & Environmental Management Appreciation

Introduction to Auditing and Evaluating Behaviour Change (IEMA approved training)

International Association of Engineers

Institution of Chemical Engineers (IChemE)

The Institution of Engineering and Technology

Emmanuel is dedicated to helping you break into data science and AI with practical tips, real-world insights, and the latest trends.

Emmanuel Ogungbemi | Substack

We are delighted to be back for the second edition of ML Club Live!

This event is all about getting hands-on with GenAI, with some coding examples and also some discussions of interesting papers.

As always, this is community-driven, so if you want to come and speak about anything related to ML or AI, even for 10 minutes, please reach out to Emmanuel (bunmieogungbemi@gmail.com) or Andy (andrewpmcmahon629@gmail.com).

ML Club - July 2024 - Hands on with GenAI, Tue, Jul 2, 2024, 5:30 PM | Meetup

I coordinate Real Projects Talk for AFBE-UK Scotland

Organising Data Science and Engineering events
- To help newbies get into data science
- Organise boot camps to support learning and development
- Support working professionals by providing mentorship and advice
- https://www.meetup.com/ml-club/

Comprehensive Python Cheatsheet

I am excited to share an essential resource: Numpy, Numerical Python!

This comprehensive guide covers the powerful capabilities of Numpy for numerical computing in Python. 

Perfect for students, data scientists, and anyone working with data, this document provides detailed explanations and examples of Numpy's functionalities.

Key topics include:
- Basics of Numpy: Arrays, nd-arrays, and their advantages over lists
- Linear algebra operations with Numpy
- Fourier Transform for signal processing
- Matrix operations: addition, multiplication, transpose, and determinants
- Creating arrays: zeros, ones, full, identity, arrange, linspace, and logspace
- Random number generation and array initialization
- Array manipulation: reshaping, slicing, indexing, and broadcasting
- Statistical functions: mean, median, standard deviation, and variance
- Advanced topics: universal functions (ufuncs), complex arithmetic, and data type abbreviations


Enhance your numerical computing skills and streamline your data processing workflows with this detailed guide on Numpy!

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Understanding NumPy

I am excited to share an invaluable resource: Basic SQL!

This comprehensive guide provides a deep dive into SQL fundamentals, covering everything from data definition and constraints to basic retrieval queries and set operations. 

Perfect for students, professionals, and anyone looking to strengthen their SQL skills, this document offers detailed explanations and practical examples.

Key topics include:
- SQL Data Definition and Data Types: Creating tables, specifying data types, and understanding schemas.
- Specifying Constraints in SQL: Primary keys, unique keys, foreign keys, and check constraints.
- Basic Retrieval Queries in SQL: Using SELECT statements, WHERE clauses, and basic data retrieval techniques.
- Set Operations in SQL: UNION, EXCEPT, and INTERSECT operations.


Enhance your database management skills and master SQL with this detailed guide!

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Understanding Git: Your Ultimate Version Control System 🚀

Ever wondered what makes Git so essential?

Let’s break it down for you:

1/ What is Git?
↳ Git is a version control system that tracks code changes.
↳ It’s a tool used by developers to collaborate on projects.
↳ With Git, you can revert to previous versions of your project anytime.

2/ Git vs. GitHub
↳ Git is NOT the same as GitHub.
↳ GitHub is a hosting service that makes use of Git.
↳ GitHub allows developers to collaborate globally.

3/ Why Git Matters
↳ Over 70% of developers use Git!
↳ It tracks every change made to your project.
↳ It allows multiple people to work on the same project from anywhere.

4/ Key Git Features
↳ Every time a file is changed, Git tracks it.
↳ You can see the entire history of changes (commits).
↳ Git allows you to revert back to earlier versions easily.

5/ Git Branching
↳ Create separate branches to work on different features.
↳ Merge branches when the feature is complete.
↳ Switch between branches without disrupting the main project.

6/ Git Commands to Know
↳ $ git init: Initializes a new Git repository.
↳ $ git add <filename>: Stages files for commit.
↳ $ git commit -m “message”: Commits changes with a message.

7/ Pushing to GitHub
↳ After committing changes locally, push them to GitHub.
↳ $ git push origin master: Sends your code to the master branch on GitHub.
↳ GitHub hosts your project so others can contribute or review it.

Git makes collaboration easier and code management more efficient. 
- Mastering Git is essential for every developer!
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🔔 Follow Emmanuel Ogungbemi for more insights on Git and development tools.

How to Build Useful Dashboards!

Creating effective dashboards is key to making data-driven decisions. 

This guide provides step-by-step instructions and best practices to help you design dashboards that deliver clear, actionable insights. 

Whether you're new to dashboard design or looking to improve your skills, this resource is for you.

Key takeaways include:

- Principles of effective dashboard design
- Choosing the right visualization tools
- Best practices for data presentation
- Tips for ensuring user-friendly and interactive dashboards

Transform your data into meaningful insights with this comprehensive guide!

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How to Build dashboards

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Follow @eoogungbemi for more data engineering contents.

Discover the Big Book of Data Engineering!

Are you overwhelmed by the complexity of data engineering? 

"The Big Book of Data Engineering" is your go-to resource for mastering data engineering.

Key Insights

1. Understanding Data Pipelines
- Building and managing data pipelines can be confusing and error-prone.
- Learn about the architecture and components of data pipelines.
- Streamlines the process of moving and transforming data efficiently.

2. Mastering ETL Processes
- ETL (Extract, Transform, Load) can be time-consuming and complex.
- The book provides step-by-step guidance on designing and implementing ETL processes.
- Ensures data is clean, consistent, and ready for analysis.

3. Data Storage Solutions
- Choosing the right data storage solution can be challenging.
- Explore various storage options, including data lakes, warehouses, and cloud storage.
- Helps you select the best storage strategy for your needs.

4. Advanced Data Processing
- Processing large volumes of data quickly and accurately is demanding.
- Discover advanced processing techniques using frameworks like Apache Spark and Hadoop.
- Enhances your ability to handle big data workloads efficiently.

5. Ensuring Data Quality
- Inaccurate or incomplete data leads to poor insights.
- Learn best practices for maintaining high data quality.
- Improves the reliability and validity of your data.

6. Data Security and Governance
- Protecting data from breaches and ensuring compliance is critical.
- Get insights into implementing robust security measures and governance policies.
- Safeguards your data and maintains regulatory compliance.

Why You Need This Book:
- Navigating the complexities of data engineering without a solid guide can be overwhelming.
- "The Big Book of Data Engineering" provides comprehensive knowledge and practical advice.
- Empowers you to build robust data systems and make data-driven decisions with confidence.

Start your journey towards data engineering mastery with this indispensable resource.

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The Big Book of Data Engineering

Excited to share an essential resource: SQL Fundamentals!

This comprehensive guide covers the foundational concepts of SQL, making it perfect for students, professionals, and anyone looking to deepen their understanding of SQL and database management. 

From basic queries to advanced techniques, this document provides a thorough introduction to SQL.

Key topics include:

Introduction to SQL SELECT Statements: Learn how to retrieve data from databases using basic SELECT statements.
Basic SELECT Statement Syntax: Understand the structure and components of a basic SELECT statement, including SELECT, FROM, and WHERE clauses.
Writing SQL Statements: Guidelines for constructing clear and readable SQL statements.
Selecting All Columns and Rows: Techniques for retrieving all data from tables.
Selecting Specific Columns: How to display specific columns of a table.
Column Heading Defaults and Aliases: Customizing column headings for better readability.
Arithmetic Expressions and Operator Precedence: Using arithmetic operators and understanding their precedence in expressions.
Handling Null Values: Defining and working with null values in SQL.
Concatenation and Literal Character Strings: Combining columns and adding literal strings in SQL queries.
Eliminating Duplicate Rows: Using the DISTINCT keyword to remove duplicate rows in query results.
SQL and SQL*Plus Interaction: Overview of SQL*Plus commands and their differences from SQL statements.
Displaying Table Structure: Using the DESCRIBE command to view table structures.
Enhance your SQL skills and master database management with this detailed guide!

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Writing Basic SQL Statements

I am excited to share an insightful resource: Improving Python and Spark Performance and Interoperability with Apache Arrow by Julien Le Dem and Li Jin!

This comprehensive guide delves into optimizing Python and Spark interoperability using Apache Arrow. 

Perfect for data scientists, engineers, and anyone working with big data, this document provides a deep dive into enhancing performance and efficiency in data processing.

Key topics include:

Current Limitations of PySpark UDFs: Understanding the constraints and performance issues with existing user-defined functions.
Overview of Apache Arrow: Introduction to Arrow, its goals, and benefits for in-memory columnar storage.
Performance Improvements: Real-world examples and benchmarks showcasing significant speedups in PySpark integration.
Vectorized UDFs: Leveraging pandas DataFrames for efficient data processing and reducing overhead.
Group UDFs: Techniques for improving group-based operations and data normalization.
Future Roadmap: Upcoming enhancements and support for Arrow in the Apache ecosystem.


Enhance your data processing workflows and achieve higher performance with this detailed guide on Apache Arrow and PySpark!

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Improving Python and Spark Performance and interoperabi

If I had to start learning #dataengineering all over again

(I will follow this plan, mostly in order)

1. Learn SQL
— Aggregations with GROUP BY
— Joins (INNER, LEFT, FULL OUTER)
— Window functions and Common table expressions

2. Learn about data modeling
— read about data normalization and 3rd normal form
— read about fact, dimension, and aggregate tables
— read about efficient table designs such as cumulative

3. Learn Python
— Learn loops, if statements
— Try out some really solid libraries like Pandas, numpy, scikit-learn
— Will not waste time on Machine Learning

4. Learn about data quality
— how do you write a good data check?
— how can you implement the write-audit-publish pattern in your pipelines?

5. Learn about distributed compute, and job orchestration
— Read about MapReduce 
— learn about partitioning, skew, and spilling to disk
— try a scheduler like Airflow

If you follow this, you’ll feel way more confident in interviews
#DataEngineering

I am excited to share an insightful resource: Data Engineering Teams: Creating Successful Big Data Teams and Products by Jesse Anderson!

This comprehensive guide explores the intricacies of building and managing data engineering teams, essential for tackling the challenges of big data. 

Whether you're a manager, VP, or individual contributor, this book provides valuable insights and strategies to ensure your data projects succeed.

Key topics include:
- The need for data engineering in the era of big data
- Essential skills required in a data engineering team
- The roles and responsibilities of data engineers
- Effective collaboration with data science and data warehousing teams
- Building and managing data pipelines
- Strategies for running successful data engineering projects
- Overcoming common pitfalls and challenges in big data projects
- Steps to creating robust big data solutions


Enhance your understanding of data engineering and lead your team to success with this detailed guide!

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Creating a Successful Data Engineering Teams

I am excited to share this Data Engineers' Handbook!

This handbook is a must-read for anyone looking to excel in the field of data engineering. 

From fundamental concepts to advanced techniques, it covers everything you need to build, maintain, and optimize data pipelines effectively. 

Whether you're a beginner or a seasoned professional, this resource will enhance your skills and knowledge.

Key takeaways include:

- Mastering data pipeline design and implementation
- Best practices for data warehousing and ETL processes
- Tools and technologies every data engineer should know
- Strategies for ensuring data quality and integrity

Don't miss out on this essential guide!

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Data Engineers' Handbook

25 Most Common Interview Questions for Data Analyst Positions 🚀

I will hire you if you can answer these questions.

Here are 25 key questions to help you prepare:

Fundamentals:
1.	What is data analytics, and why is it important for businesses?
2.	Explain the difference between structured and unstructured data.
3.	What are the key steps in the data analysis process?
4.	How do you handle missing or corrupted data in a dataset?
5.	What are the key differences between quantitative and qualitative data?

Data Tools & Techniques:
6.	What is the role of SQL in data analysis?
7.	What are some key Excel functions used in data analysis?
8.	Explain the use of JOINs in SQL.
9.	What is data normalization in databases, and why is it important?
10.	How would you explain the concept of a relational database to someone new?

Data Visualization:
11.	Why is data visualization important in data analysis?
↳ Helps present complex data clearly to stakeholders.
12.	What are some popular data visualization tools?
↳ Mention Tableau, Power BI, and Excel as key tools.
13.	When would you use a bar chart vs. a line chart?
↳ Bar charts for comparing categories, line charts for trends over time.
14.	What are some best practices for creating a dashboard?
↳ Focus on clarity, simplicity, and relevance to the audience.
15.	Explain the use of heatmaps in data visualization.
↳ Heatmaps represent data using colour gradients to show patterns or density.

Statistical Concepts:
16.	What is the difference between correlation and causation?
↳ Correlation shows a relationship, while causation implies one variable causes the other.
17.	Explain the concept of standard deviation.
↳ Measures the amount of variation or dispersion in a dataset.
18.	What is the p-value in hypothesis testing?
↳ The p-value helps determine the significance of your results.
19.	How do you determine if a dataset is normally distributed?
↳ Use histograms, Q-Q plots, and skewness/kurtosis measures.
20.	What is A/B testing, and how is it used in decision-making?

Practical Implementation:
21.	How do you clean and prepare data for analysis?
↳ Explain steps like handling missing data, removing duplicates, and outlier treatment.
22.	What are the challenges of working with large datasets?
↳ Mention issues like performance, storage, and data quality.
23.	How do you handle outliers in your data?
↳ Depending on the context, you can remove, cap, or transform outliers.
24.	Explain a situation where you identified a problem and solved it using data.
↳ Be prepared to walk through a real-life example with clear steps.
25.	How do you ensure data accuracy and integrity throughout your analysis?
↳ Double-check data entry, validate sources and perform consistency checks.

________________________________________

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So more data analysts can prepare for interviews!

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Good luck with the interviews! 💼

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Dr Emmanuel Ogungbemi

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Chad Angle's LinkedIn Performance

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