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

You're STILL a data analyst, even if...

- Excel is your main tool
- SQL syntax slips your mind
- You didn't ace that important interview
- Programming isn't your strong suit
- You made a mistake in your analysis
- You can't recall the exact function name
- You need to Google how to redo something simple
- You're NOT a data analyst when...

You decide to quit
SO DON'T QUIT! KEEP PUSHING FORWARD!

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

I am excited to share an essential resource: Structured Query Language (SQL) by Fang Ying, Sham!

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

Key topics include:
- Creating and using databases and tables
- Inserting, updating, and deleting records
- Basic SQL queries: SELECT, WHERE, ORDER BY, DISTINCT
- Advanced SQL operations include JOINs, UNIONS, INTERSECT, and EXCEPT.
- Working with views, temporary tables, and common table expressions (CTEs)
- String functions, subqueries, and stored procedures
- SQL ranking functions: ROW_NUMBER, RANK, DENSE_RANK, NTILE
- Handling NULL values and using CASE statements
- Aggregation functions: COUNT, SUM, AVG, MAX, MIN
- Data type conversions and partitioning

Enhance your SQL skills and streamline your database management tasks with this detailed 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/

SQL Commands II

I am excited to share an invaluable resource: Introduction to Programming with Python!

This comprehensive guide covers the fundamental concepts of programming using Python. 

Perfect for students, beginners, and anyone looking to enhance their programming skills, this document provides clear explanations and practical examples.

Key topics include:
- Programming basics: source code, syntax, and output
- Compiling and interpreting Python code
- Working with expressions, arithmetic operators, and precedence
- Handling integer and real number operations
- Using variables and assignment statements
- Printing output and reading user input
- Control flow: loops (for, while) and conditionals (if/else)
- Data types: strings, lists, tuples, sets, and dictionaries
- Functions and higher-order functions
- Object-oriented programming: classes, objects, methods, and inheritance
- File processing and text manipulation


Enhance your Python programming knowledge and build a solid foundation with this detailed guide!

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Introduction to Programming with Python

Excited to share an invaluable resource: Top 50 Machine Learning Interview Questions & Answers!

This comprehensive guide covers the most frequently asked questions in machine learning interviews. Perfect for students, professionals, and anyone preparing for a machine learning interview, this document provides detailed answers and explanations to help you succeed.

Key topics include:
- Basics of Machine Learning: Definition, Differences between Data Mining and Machine Learning, and Key Algorithms.
- Overfitting: causes, avoidance techniques, and cross-validation.
- Types of Machine Learning: supervised, unsupervised, semi-supervised, and reinforcement learning.
- Common Algorithms: Decision Trees, Neural Networks, Support Vector Machines, and More.
- Model Evaluation: Training and test sets, model building, testing, and application.
- Algorithm Techniques: Inductive Learning, Probabilistic Networks, and Clustering.
- Advanced Topics: Genetic Programming, Bayesian Networks, PCA, ICA, and Ensemble Methods.
- Practical Applications: Real-world use cases in computer vision, speech recognition, and recommendation systems.


Enhance your interview preparation and machine learning knowledge with this detailed guide!

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Microsoft Azure - AZ 900 Introduction

Excited to share an essential resource: Model Evaluation and Selection Cheatsheet by Waleed Mousa!

This comprehensive cheatsheet covers crucial techniques and best practices for evaluating and selecting machine learning models. 

Perfect for data scientists, ML engineers, and anyone working with machine learning, this document provides quick references to help you optimize your models.

Key topics include:
- Data Splitting: Techniques for splitting datasets into training, test, and validation sets.
- Model Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC AUC, MSE, MAE, R2 score, and more.
- Cross-validation: K-fold, stratified K-fold, leave-one-out, and multi-metric cross-validation.
- Hyperparameter Tuning: Grid search CV, randomized search CV, and Bayesian optimization.
- Model Selection: Comparing classifiers, feature importance, and pipeline creation.
- Ensemble Methods: Random Forest, Gradient Boosting, AdaBoost, and stacking classifiers.
- Dimensionality Reduction: PCA, t-SNE, and feature selection.
- Data Preprocessing: Scaling, encoding, imputing, and generating polynomial features.
- Advanced Model Evaluation: ROC and precision-recall curves, confusion matrix visualization, adjusted R2.
- Handling Imbalanced Datasets: Under-sampling, over-sampling, SMOTE, and class weights.
- Advanced Feature Selection: Recursive feature elimination, mutual information, SelectKBest.
- Model Interpretability: Permutation importance, SHAP values, LIME.
- Time Series Analysis: Rolling and expanding window features, time series cross-validation.
- Working with Text Data: CountVectorizer, TF-IDF, and word embeddings.
- Neural Networks: Keras/TensorFlow, early stopping, custom loss functions, fine-tuning pre-trained models.


Enhance your model evaluation and selection process with this detailed cheat sheet!

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ML - Model evaluation and selection

I am excited to share an essential resource: Big Data Engineering Interview Questions & Answers!

This comprehensive guide is perfect for anyone preparing for big data engineering interviews. 

It covers a wide range of topics, from SQL and Scala programming to tools like Sqoop, Hive, and Spark, providing valuable insights and detailed answers to help you ace your interviews.

Key topics include:
- SQL Interview Questions: Understanding query execution sequence, normalization, database objects, constraints, transactions, and various SQL operations.
- Scala Programming: Basics of Scala, Tail-Recursion, Traits, Case Classes, and Higher-Order Functions.
- Sqoop Interview Questions: Data Import/Export, Connectors, and Performance Tuning.
Hive Interview Questions: Hive architecture, data types, partitions, and optimization techniques.
- Spark Interview Questions: RDDs, DataFrames, Spark SQL, and Performance Tuning.
Top 250+ Interview Questions: Covering a broad spectrum of big data tools and technologies.
- Miscellaneous Interview Questions: Addressing various aspects of big data engineering, including data modelling, ETL processes, and big data ecosystems.


Enhance your interview preparation and big data engineering skills with this detailed guide!

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Big Data Engineering Interview Questions and Answers

Excited to share an invaluable resource: Machine Learning and Deep Learning by Sven-Patrik Hallsjö!

This comprehensive guide delves into the fundamental concepts and advanced techniques of machine learning and deep learning. 

Perfect for students, professionals, and enthusiasts, this document provides a thorough overview of key topics, supported by practical examples and insights from leading resources.

Key topics include:
- Introduction to Machine Learning: Supervised vs. Unsupervised Learning, Linear Regression, and Motivating Deep Learning.
- Deep Learning: Forward Feed, Backfitting, Learning vs. Pure Optimization, and Convolutional Neural Networks (CNN).
- Real-life Applications and Current Research: Practical applications and ongoing research in the field.
- Neurons and Perceptrons: Understanding the basics of neural networks and their implementation.
- Universal Approximation: Theoretical foundations and practical implications of deep learning models.
- Backpropagation: Detailed explanation of the backpropagation algorithm and its importance in training neural networks.
- Convolutional Networks (CNN): Specialized neural networks for processing grid-like data, such as images and time-series data.
- Online Applications and Resources: Practical tutorials and resources for applying machine learning and deep learning in real-world scenarios.
- Further Learning Resources: A curated list of courses, tutorials, and lectures from top institutions and platforms.


Enhance your understanding of machine learning and deep learning with this detailed guide and take your skills to the next level!

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Machine Learning - Deep Learning

Job rejections are tough.

But here's why you shouldn't take them personally.

Many factors can be beyond your control:

- The company may have cancelled the role
- The hiring manager could have left the company
- The role might have been filled internally
- Unconscious bias might have influenced the decision
- Another candidate might have more experience than you
- Your application might have arrived after they shortlisted candidates

Instead of dwelling on uncontrollable factors, focus on what you can control:

- Create relevant portfolio projects to demonstrate your technical skills
- Ask for feedback on your applications and interview performance
- Participate in mock interviews to improve your interview skills
- Network and build relationships within your industry
- Enhance your LinkedIn profile and online presence
- Practice coding regularly

The job market seems to be improving daily.

If you're searching for a job, I'm cheering you on. 

Is this helpful? Share it with others!

I am excited to share an insightful resource: Apache Spark (CS240A) by T. Yang!

This comprehensive guide explores the powerful capabilities of Apache Spark for distributed computing. 

Ideal for data scientists, engineers, and anyone working with big data, this document provides a deep dive into Spark's architecture, APIs, and applications.

Key topics include:
- Introduction to Hadoop and MapReduce
- Overview of Spark and its advantages over MapReduce
- Python examples and list comprehension
- Key concepts of RDDs (Resilient Distributed Datasets)
- Language support for Python, Scala, and Java
- Creating RDDs and Spark transformations
- Basic actions and working with key-value pairs
- Detailed explanation of the DAG scheduler
- Setting the level of parallelism
- Interactive shell and standalone application usage
- Example application: PageRank algorithm using MapReduce


Enhance your understanding of distributed computing and harness the full power of Apache Spark with this detailed guide!

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Apache Spark

Microsoft Azure - AZ 104 Introduction

The worst part of working in SQL? Waiting for your query to run.

Here are 9 tips for optimizing your SQL queries 

- Reduce Unnecessary Data Processing

Delay using ORDER BY until the final SELECT statement. Subqueries and CTEs usually don�t need ordering.

Use DISTINCT sparingly. It can be computationally expensive, especially on large datasets.

Opt for UNION ALL instead of UNION when duplicates are acceptable or unlikely.

SELECT only the specific columns you need instead of using SELECT *.

- Take Advantage of Indexes on Your Tables

Filter on the index key whenever possible to minimise the data size processed.

Limit the use of OR conditions, as they can lead to inefficient full table scans.

Avoid using functions in WHERE clauses, as they can interfere with indexing.

- Reduce Cardinality of JOINS

Use a CTE or subquery to filter and reduce table sizes before performing a JOIN.

Apply filter conditions in the ON clause rather than the WHERE clause to reduce rows before the JOIN operation.

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10 Data Science Managers said this about their WORST interview candidates.

Here's what they said

- Lacked Self-Awareness and Were Not Self-Critical
Some candidates couldn't reflect on a time when a project went wrong or discuss what they learned and what they would do differently.
They showed no evidence of self-improvement or reflection when asked about their gaps.
A major red flag is when a candidate insists a project went perfectly and claims there's nothing they would change in hindsight.

- Overconfident and Un-Coachable
"One candidate was overconfident, didn't listen carefully to questions, and refused to answer them directly."
A significant concern is when a candidate struggles to articulate their thought process. This indicates potential difficulties in coaching and development.

- Not Genuinely Interested in the Company
"They asked very basic questions that showed no research on the role or the company."
When given a chance to ask questions, one candidate only asked about the quality of the food at the company.

- Unstructured in Their Communication & Thought Process
Scattered thinking, repeating themselves, and switching between topics makes conversation disjointed and hard to follow.
Providing overly detailed answers beyond what was asked leaves less time to complete the interview.

- Not a Team Player
A red flag is when a candidate tries to take solo credit for an entire project, suggesting a lack of collaboration and team spirit.

Did you find this helpful? Please share it!

Data Science is NOT All About Coding. 
 
 
When I first entered the data science arena armed with solid coding skills, I thought I was fully equipped.  
 
But as I navigated real-world data challenges, I quickly realized that my coding prowess was just one piece of the puzzle.  
 
The journey into data science demanded much more! 
 
Data science is indeed a mosaic of skills, not a monolith built solely on coding. It thrives on a blend of technical skills and strategic thinking. 
 
DOs: 
 
- Embrace Statistics 
 
Master statistical methods to extract meaningful insights from raw data. 
 
- Cultivate Domain Expertise 
 
Deepen your understanding of the industry you're serving to make your analyses more impactful. 
  
- Enhance Problem-Solving Skills 
 
Engage in projects that push your analytical boundaries and require novel solutions. 
 
DON'Ts: 
 
- Don't Focus Solely on Coding 
 
While important, don't neglect the power of analytical reasoning and contextual knowledge. 
 
- Don't Ignore Soft Skills 
 
Communication and teamwork are crucial in translating data insights into actionable business decisions. 
 
- Don't Stop Learning 
 
The field is evolving rapidly; staying updated is key to staying relevant. 
 
Did you ever encounter a moment in your data science journey where coding alone wasn't enough?  
 
What other skills have you found essential? 
 
If this resonates with you, like, comment, and share your thoughts!  
 
How do you balance data science's technical and strategic demands in your career? 
 
Stay tuned for more myths busted and tips shared.  
 
Follow me, and let's connect to explore the multifaceted world of data science together!

Interviews are only 50% about getting the right answer.

The other 50%? Effective communication.

The main reason for poor communication: not understanding your audience.

We often focus solely on what we want to say.

Let's shift our perspective.

Focus on what matters to your audience.

Instead of: How do I list my past professional experience?
Ask yourself: What does the recruiter look for when screening candidates?

Instead of: How do I explain my current job duties?
Ask yourself: Who does a manager want to hire to make their job easier?

Instead of: How do I solve this SQL question?
Ask yourself: What will a business partner want to know from this data point?

Instead of: How do I show I�m a good culture fit for this company?
Ask yourself: Based on my conversations, what behaviors does this company value?

Instead of: How do I stand out from other candidates?
Ask yourself: What unique value can I bring that aligns with the company's goals?

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Analysts do more than write code and make dashboards.

In reality, it's a very collaborative role.

Here's an overview of what to expect:

1. Gathering the Data:

This often means meetings.

Depending on the team and project scope, it could be a lot of meetings.

This is necessary to gather requirements for the project.

2. Analyzing the data:

Here's where the heavy lifting often begins.

- Make sure you have access to the data.
- Establish data connections
- Begin data manipulation

If you don't have access to the data, get ready for more meetings to make it happen.

3. Presenting the data:

After you've validated the data, you can begin creating your dashboards/reports.

What about data quality errors?

This happens often. Here comes another meeting with your stakeholders and other team members.

Once validated...

- Complete report
- Seek feedback
- Iterate if necessary

The collaborative nature of the role is why soft skills are stressed so much.

Yes, you need to be technically saavy...

But you also need to be a well-rounded teammate.

Found these helpful?

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10 Advanced SQL Interview Questions

Hint: Proficiency with Window functions is essential to tackle these.

Q1: You have two tables: 'response_times' (request_id, response_time_ms, device_type_id) and 'device_types' (device_type_id, device_name, manufacturer). Write a query to find the 95th percentile of response times for each device manufacturer.

Q2: For the table 'daily_visits' (visit_date, visit_count), create a query to compute the 7-day moving average of daily visits for each date.

Q3: Given 'stock_prices' (date, stock_symbol, closing_price), write a query to determine the cumulative change in stock price relative to the year's starting price.

Q4: You have 'products' (product_id, product_name, category_id, price) and 'categories' (category_id, category_name). Write a query to find the price difference between each product and the next most expensive product within the same category.

Q5: For the table 'customer_spending' (customer_id, total_spend), write a query to divide customers into 10 deciles based on their total spending.

Q6: Using 'daily_active_users' (activity_date, user_count), write a query to calculate the day-over-day change in user count and the growth rate.

Q7: Given 'sales' (sale_id, sale_date, amount), write a query to compute the total sales amount for each day of the current month, along with a running total of month-to-date sales.

Q8: You have 'employee_sales' (employee_id, department_id, sales_amount) and �employees� (employee_id, employee_name). Write a query to identify the top 5 employees by sales amount in each department.

Q9: Using 'employee_positions' (employee_id, position, start_date, end_date), write a query to find employees who have been promoted (i.e., changed to a different position) within 6 months of their initial hire date.

Q10: You have 'customer_transactions' (customer_id, transaction_date, transaction_amount) and 'customer_info' (customer_id, customer_name, signup_date). Write a query to calculate the moving average of transaction amounts for each customer over their last 3 transactions, only including customers who have been signed up for more than a year.

Which question did you find the most challenging?

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Feeling swamped by machine learning's complexity? 

You're in good company.

Here's your step-by-step guide to conquering machine learning basics:

1. Grasp the Essentials
   - Master key programming languages: Python and R are your new best friends.
   - Wrap your head around vital concepts: think statistics, linear algebra, and calculus.

2. Conquer Core Algorithms
   - Dive deep into the world of linear regression, decision trees, and clustering.
   - Learn not just the 'what', but the 'how' and 'when' to deploy each algorithm.

3. Practical Application
   - Take on real-world projects to bring theories to life.
   - Develop and evaluate models with the help of TensorFlow, Scikit-learn, and PyTorch.

4. Keep Your Knowledge Fresh
   - Stay ahead with the latest industry trends and breakthroughs.
   - Engage in forums, workshops, and research papers to keep your edge sharp.

5. Foster a Supportive Community
   - Connect with fellow enthusiasts and experts.
   - Exchange insights, ask for feedback, and collaborate on exciting projects.

Machine learning is more than just crunching numbers�it�s about leveraging your know-how to tackle real challenges.

Kickstart your journey with these foundational steps and make your mark on the machine learning universe.

What�s been a game-changer in your machine-learning adventure?

Drop your thoughts below! 

Did you find this helpful, or do you know someone who might? Don't hesitate to like, comment, and share.

Let�s dive into the machine-learning cosmos together!

Top 5 ETL Pipeline Mistakes to Avoid � A Guide for Data Engineers! 

As someone who's navigated the tricky terrain of data engineering for years, I've seen it all. The pitfalls that catch even the most experienced among us can snag your ETL pipelines and throw a wrench in the works.

Here�s how to steer clear and amp up your data game:

1. Data Quality Checks � Non-Negotiable!
- Skipping these is playing with fire. 
- Keep your data pristine by validating early and often.

2. Think Big, Scale Smart 
- Ignoring future growth is a recipe for disaster. 
- Design with scalability in mind and keep your pipelines fluid and flexible.

3. Vigilance is Key: Monitoring & Alerting
- Set up your sentinels. 
- Monitoring tools and alerts are your first line of defence against data gremlins.

4. Simplicity in Transformation
- Complexity isn't a virtue. 
- Aim for elegance and simplicity in your transformations to dodge those needless headaches.

5. Documentation = Sanity Saver
- Leave no one in the dark. 
- Crystal clear documentation ensures your team can follow your lead without missing a beat.

Steering clear of these common mistakes propels you towards engineering ETL pipelines that not only last but excel.

Remember: Perfection is about the journey, not the destination. Continually hone your skills and adapt to the evolving world of data.

What pitfalls do you think we should all be wary of in ETL development? Let�s pool our knowledge and level up together! 

Your experiences? Let's exchange insights and conquer the data world as one!

Avoid These ETL Pipeline Blunders. A Guide for Every Data Engineer!

Being deep in data engineering, I've clocked that even the top brass can trip over these frequent ETL mishaps.

Here's your blueprint to sidestep these snags and boost your data magic:

1. Never Gloss Over Data Quality Checks
- Skipping these can wreak havoc on your outcomes. Vigorously validate your data to preserve its integrity.

2. Scalability - Your North Star
- Not prepping for expansion can jam your pipeline. Craft with growth in mind to ensure seamless operations.

3. Stay Alert with Monitoring & Alerts
- Arm yourself with monitoring tools and alerts to spot and squash bugs at lightning speed.

4. Embrace Simplicity in Transformations
- Complex isn't always best. Go for clean, straightforward transformations to dodge potential migraines.

5. Documentation - Your Trusty Sidekick
- Don't leave your crew in the lurch. Stellar documentation is a lifesaver, slashing time and stress.

Steering clear of these pitfalls primes you for crafting bulletproof, enduring ETL pipelines.

Perfection's a Marathon, Not a Sprint.

Keep honing your skills and navigating the dynamic data landscape.

I am eager to hear your insights! What other pitfalls should we be vigilant of in ETL development?

Let's pool our knowledge and ascend together!

📩 For collaborations or LinkedIn growth support → DM me or email paul_akira_storm@icloud.com
⸻

🌍 WHO I AM

I’m Paul Storm, CMO & Co-Founder of Digital Storm Weekly — one of the fastest-growing AI newsletters worldwide, with 1.3M+ LinkedIn followers and 530,000+ subscribers.

I help founders, creators, and tech leaders build magnetic LinkedIn brands that turn visibility into influence, leads, and opportunity.

⸻

🚀 WHAT I DO

✔️ Daily LinkedIn content in your authentic voice
✔️ Carousels, visuals & authority-driven formats
✔️ Full content calendar and seamless scheduling
✔️ Profile optimization that builds trust & drives conversions
✔️ More reach, more leads, more inbound opportunities

We’ve worked with global brands like NVIDIA, Huawei, and BlackBerry to amplify their growth and authority.

⸻

🎯 WHO I HELP

You’re building something big. But LinkedIn feels like a burden.

❌ Don’t know what to post
❌ Too busy to stay consistent
❌ No system to turn visibility into growth
❌ Your brand deserves attention — but your time is limited

That’s where I come in.

⸻

💡 WHY IT WORKS

This isn’t “posting for the sake of posting.”
It’s high-performance brand positioning designed to scale your presence, pipeline, and reputation.

With Digital Storm Weekly, we’ve built one of the largest AI-driven growth platforms — read by decision-makers at Apple, Meta, Google, and Microsoft.
I know how to turn visibility into authority, clients, and long-term impact.

⸻

✨ Want to grow without burning out?
DM me “GROW” or email digitalstormweekly@gmail.com
.

Let’s make LinkedIn your biggest growth asset.

Paul Storm

50 LIFE RULES THAT ARE GOLDEN.
1. Always stand up while shaking hands with someone.
2. Never make the first offer in a negotiation.
3. Keep their secret if they have faith in you.
4. Return an automobile they lend you with the tank full.
5. Act passionately or don't act at all.
6. Shake hands firmly and stare the other person in the eye.
7. Take a solo trip and experience it.
8. There are clear reasons why you should never refuse a peppermint tablet.
9. If aging is your goal, heed advice.
10. Join the new individual at the office or school for dinner.
11. If you are furious and you are texting someone, complete, read, delete, and rewrite the message.
12. You avoid discussing business, politics, or religion at the table.
13. Set goals and work toward them in writing.
14. Present your case while showing people respect and tolerance.
15. Make a visit and phone your family.
16. Never look back and always learn from your experiences
17. Your personality ought to be one of loyalty and honor.
18. Never give money to someone you know won't be able to repay you.
19. Have faith in something.
20. Make your bed as soon as you wake up.
21. Take a shower and sing.
22. Maintain a garden or a plant.
23. Look up at the sky whenever you get the chance.
24. Recognize and utilize your strengths.
25. Embrace your work or quit it.
26. When you need assistance, ask for it.
27. Instruct someone in a value—ideally a modest one.
28. Express gratitude to the person who is reaching out to you.
29. Show your neighbors kindness.
30. If you brighten someone else's day, it will brighten yours as well.
31. Put yourself to the test.
32 Give yourself a treat at least yearly
33. Look after your well-being.
34. Always give a cheerful greeting.
35. Speak slowly, but think quickly.
36. Avoid speaking while full of mouth.
37. Always maintain a neat appearance, trim your nails, and shine your shoes.
38. Avoid voicing an opinion on topics you are unfamiliar with.
39. Don't treat others badly.
40. Approach each day as though it were your last.
41. Don't ever pass up the chance to keep silent.
42. Give someone credit for their work.
43. Have humility, even if it's not always present.
44. Always remember your origins.
45. Take trips whenever you can.
46. Resign from the step.
47 Take a rainy dance.
48. Keep going after your success.
49. Treat others fairly and defend those in need.
50. Acquire the skill of relishing solitude.

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Founder of one of the largest AI newsletters with ​>570,000+ readers.

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AI is no longer a buzzword 

it’s a business advantage.

But here’s the reality: building an AI-driven company isn’t just about training models.

It’s about aligning the right tools at the right time across your entire workflow — from strategy to scaling.

Here’s a stage-by-stage framework for integrating AI into your business operations:

🔹 1. Ideation & Strategy
  Use AI-powered research assistants, brainstorming tools, and visual planners to identify high-impact opportunities. This is where direction meets innovation.

🔹 2. Data Collection & Preparation
  Leverage AI scrapers and data wrangling tools to gather, clean, and structure your data — the fuel that powers your models.

🔹 3. Workflow Design & Automation
  Integrate no-code builders and automation platforms to streamline internal operations and bridge departmental silos.

🔹 4. Model Selection & Evaluation
  Evaluate and select the most effective AI models or APIs tailored to your business goals. Constant testing ensures accuracy and reliability.

🔹 5. Deployment & Feedback Loop
  Deploy with cloud or no-code solutions, and use monitoring tools to collect feedback, learn from results, and continuously optimize.

✅ Whether you’re just starting out or scaling a mature AI infrastructure, this framework helps you stay focused, efficient, and adaptable.

If you’re working on integrating AI into your business — save this for future reference.

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 Found this helpful? Share it!

Founder of one of the largest AI newsletters with ​>550,000+ readers.

Google, Harvard, and more are offering FREE AI courses (no payment required)

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OpenAI in collab with DeepLearning is offering this course taught by Isa Fulford and Andrew Ng.

Start off with best practices and finish with a better understanding of prompting with hands-on practice.

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6. LLMOps

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Go through the LLMOps pipeline of pre-processing training data and adapt a supervised tuning pipeline to train and deploy a custom LLM.

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In this 4 module course, the University of California - Davis covers big data and introduces IBM's Watson.

Start by learning about big data opportunities and end knowing the limitations of AI.

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8. AI Applications and Prompt Engineering

edX has an introductory course on prompt engineering that goes beyond the basics.

Start by learning the basics and end knowing how to create your own applications.

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Take Coursera courses without the trial:

First, go to the course you want to take and click 'Enroll for free', then 'Audit the course'.

Note: You'll need to create an account to take courses, but won't need to pay anything.

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And follow Paul Storm for more!

🚨 RIP ChatGPT The AI King has been dethroned.

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