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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- Add your comment!

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

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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/

Introduction to Programming with Python

I am excited to share an essential resource: An Introduction to SQL by Alkistis Pourtsidou from Queen Mary, University of London!

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

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

Key topics include:
- What is SQL and why we need databases?
- Ways to use SQL: console commands, GUI interfaces, and programming language interfaces
- Basic SQL commands: SHOW DATABASES, SHOW TABLES, SHOW COLUMNS, DESCRIBE
- Variable types supported by SQL: Numeric, Character, Binary, and Temporal
- Hands-on examples using SQLite3 in Python
- Exploring tables and combining information using JOIN
- Exercises and tasks to practice SQL skills
- Introduction to PostgreSQL: installation, creating databases, and running queries
- Handling NULL values, creating views, string concatenation, and subqueries


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

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- Add your comment!

An Introduction to SQL

I'm excited to share an essential resource: the Python Basics Cheatsheet by Waleed Mousa!

This comprehensive cheatsheet covers fundamental Python concepts and commands, making it perfect for beginners and experienced programmers alike. 

It's a handy reference guide to enhance your coding skills and boost your productivity in Python.

Key topics include:
- Basic Syntax and Operations
- Data Structures: Lists, Dictionaries, Tuples, and Sets
- Control Flow: If statements, loops, and comprehensions
- Functions and Modules: Defining, importing, and using functions
- Error Handling: Try-except blocks and raising exceptions
- File Handling: Reading, writing, and managing files
- Object-Oriented Programming: Classes, objects, inheritance, and more
- Advanced Topics: Lambda functions, generators, decorators, and context managers
- Popular Libraries: NumPy, Pandas, Matplotlib, and more
- Date and Time Operations
- File Paths and Directories
- Networking and HTTP Requests
- Debugging and Profiling
- Testing and Writing Unit Tests
- Virtual Environments and Packaging
- Regular Expressions
- Data Serialization: JSON and Pickle
- Multithreading and Multiprocessing
- Asynchronous Programming


Enhance your Python programming knowledge and keep this cheatsheet as a quick reference to streamline your coding tasks!

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- Add your comment!

Python Basics

Cloud Computing Tutorial

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 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

7 Clear Signs You’re Being Undervalued at Work:

(And what to do about it)

1.	You’re Excluded from Decisions
↳ Major decisions are made without your input.
↳ This makes you feel sidelined and unimportant.
↳ Being part of key discussions should be the norm, not the exception.

2.	They Give You Busywork, Not Impactful Projects
↳ You’re handed small, non-impactful tasks while others take on critical projects.
↳ Your potential is being wasted on work that doesn’t move the needle.
↳ Push for projects that align with your strengths and goals.

3.	You’re Not Set Up to Succeed
↳ You lack the resources, support, or clarity needed to perform well.
↳ This leads to frustration and sets you up for failure.
↳ Success requires the right tools and direction—don’t settle for less.

4.	Your Role Expands Without Rewards
↳ You’re constantly taking on more responsibilities, but your title and pay stay the same.
↳ If you’re doing more, you deserve to be compensated accordingly.
↳ Speak up about aligning your role with fair recognition.

5.	No Recognition for Your Hard Work
↳ Your contributions go unnoticed, and there’s no appreciation for what you do.
↳ Recognition is key for morale and motivation, and everyone deserves it.
↳ It’s time to request regular feedback and acknowledgment.

6.	Your Growth Stalls
↳ You’re not being given opportunities to develop new skills.
↳ If you feel stuck, it’s a sign your potential isn’t being nurtured.
↳ Start exploring options that align with your growth ambitions.

7.	Inadequate Compensation
↳ Your salary doesn’t reflect your contributions to the company.
↳ If others in similar roles are being paid more, it’s time to negotiate.
↳ Know your worth and advocate for fair compensation.

________________________________________

Feeling undervalued can be draining, 
but recognizing the signs is the first step to change.

Do any of these resonate with you? 
Let me know in the comments! 👇

Thanks, Dora Venourek for sharing.

♻️ Repost to help others recognize their worth.
🔔 Follow Emmanuel Ogungbemi for more workplace and career insights.

7 Signs You Are UnderValued At Work

When I first entered the world of data, I discovered the power of SQL and databases to transform raw data into meaningful insights.

The challenge? 

Many professionals struggle to grasp the foundational concepts and practical applications of SQL.

Here's how I mastered SQL and databases and advanced my career:

1. Learning the Basics
I started with the core concepts of relational databases and understanding tables, relationships, and critical constraints.

2. Hands-On Practice
Wrote optimised SQL queries to extract, manipulate, and analyse data across various projects.

3. Complex Problem Solving: 
Designed and implemented efficient database schemas and complex queries to support large-scale data platforms.

4. Continuous Improvement
Stayed updated with the latest database technologies and best practices through courses, certifications, and community involvement.

Now, let's talk about you.

Are you looking to enhance your skills in SQL and databases but don't know where to start?

Here's a structured approach to help you:

1. Understand the Fundamentals
- Learn about relational database concepts and structures.

2. Get familiar with SQL syntax and basic queries (SELECT, INSERT, UPDATE, DELETE).

3. Practical Application
- Practice writing SQL queries to solve real-world problems.
- Use database management systems like MySQL, PostgreSQL, and Microsoft SQL Server.

4. Advanced Techniques:
- Explore advanced SQL topics such as joins, subqueries, indexing, and transaction management.
- Learn how to denormalise database schemas for efficient data storage and retrieval.

5. Continuous Learning and Networking
- Join SQL and database forums to ask questions and share knowledge.
- Attend webinars and workshops to stay updated with industry trends.

I've created a comprehensive Data Analyst Roadmap to support your learning journey with essential steps and resources for mastering SQL and databases.

To get a link to download your free Data Analyst Roadmap, do this:
1. Repost 
2. Comment "share".

You have the potential to excel in SQL and database management, and I'm here to help you achieve your goals.

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

Excited to share an essential resource: Data Engineering Cert Prep - Notes!

This comprehensive guide is perfect for anyone preparing for a data engineering certification. It covers a wide range of topics, from Databricks basics to advanced ETL processes with Spark SQL, making it an invaluable resource for data engineers.


Key topics include:
- Databricks Basics: Creating clusters, using notebooks, and running different languages.
- Delta Lake: Managing Delta tables, merge operations, and advanced options like Optimize and Z ORDER.
- Relational Entities: Databases, tables, views, and common table expressions (CTEs).
- ETL with Spark SQL: Querying files, creating Delta tables, and writing to tables.
- Advanced SQL Transformation: Working with JSON data, arrays, joins, pivot tables, and higher-order functions.
- Python for Spark SQL: Using Python for data manipulation and control flow within Spark SQL.
- Incremental Data Processing: Utilizing Autoloader for incremental data processing.
- Medallion Architecture: Understanding the Bronze, Silver, and Gold architecture for data processing.
- Delta Live Table: Using declarative language for ETL processes, quality control, and referencing streaming tables.
- Task Orchestration: Running jobs and managing dependencies with DAG.
- DB SQL & Dashboard: Executing SQL queries, creating dashboards, and setting up alerts.
- Managing Permissions: Using Data Explorer to set and modify permissions.


Enhance your preparation for data engineering certifications and streamline your data engineering processes with this detailed guide!

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Data Engineering Certificates Preparation

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

I am excited to share an invaluable resource: CI/CD Pipeline-as-Code with Jenkins and Docker by Kishore Bhatia!

This comprehensive guide explores how to effectively use Jenkins and Docker together to create seamless CI/CD pipelines. 

Perfect for DevOps engineers and developers, this document covers essential techniques and best practices for building, testing, and deploying applications.

Key topics include:
- Running Jenkins Masters and Slaves in Docker
- Building, testing, and deploying Docker images from Jenkins
- Custom build environments for reproducibility and isolation
- Managing Docker host configurations with CloudBees Docker Shared Config
- Traceability of Docker images and containers in Jenkins
- Integrating Docker Hub notifications to trigger Jenkins jobs
- Implementing Jenkins Pipeline-as-Code for streamlined workflows


Enhance your CI/CD processes and streamline your DevOps practices with this detailed guide!

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CICD Pipeline-as-code with Jenkins and Docker

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 900 Introduction

The 8 Software Development Life Cycle Models You Should Know

Choosing the right software development model can significantly impact the success of your project. 

Here are 8 SDLC models and when you should consider using them:

1.	Waterfall
↳ Sequential phases: design, build, test, deploy.
↳ Best for projects with clear, unchanging requirements.
↳ Simple but rigid—ideal for small projects.

2.	Spiral
↳ Iterative and risk-driven approach with continuous refinement.
↳ Useful for complex projects that involve high risk and frequent changes.
↳ Allows for continuous evaluation and improvement.

3.	Agile
↳ Focuses on delivering incremental updates through short sprints.
↳ Ideal for projects requiring flexibility and collaboration.
↳ Prioritizes customer feedback and adaptive planning.

4.	Iterative
↳ Builds software in iterations, improving with each cycle.
↳ Great for large projects that need continuous refinements.
↳ Each iteration includes design, testing, and implementation.

5.	V-Model
↳ Developers and testers work in parallel—strict validation and verification process.
↳ Best for projects where validation at each step is essential.
↳ Ensures thorough testing before progressing to the next phase.

6.	Incremental
↳ Delivers the software in smaller, manageable increments.
↳ Ideal for large projects where full deployment isn’t feasible from the start.
↳ Each increment brings a working version closer to the final product.

7.	Big Bang
↳ Everything is developed at once—no formal planning.
↳ High risk but suitable for small projects or experimental prototypes.
↳ Requires significant time and resources upfront.

8.	Rapid Application Development (RAD)
↳ Focuses on quick development and frequent iterations through prototypes.
↳ Best for projects with tight deadlines or evolving requirements.
↳ Encourages user feedback and fast adaptation to changes.

________________________________________

Which SDLC model fits your project needs? Let me know in the comments! ⬇️

♻️ Repost to help your network understand SDLC models.
🔔 Follow Emmanuel Ogungbemi for more insights on software development best practices.

No alternative text description for this image

Transform Your Life Today! 

Discover These 11 Unbelievable Tricks to Boost Your Productivity and Success! 🌟

Feeling stuck in your routine? 

It's time to shake things up! 

Here are 11 simple yet powerful changes you can make right now to drastically improve your personal and professional life:

1/ Set Clear, Achievable Goals - Break your dreams down into actionable steps.

2/ Rise Early - Win the morning, win the day!

3/ Exercise Daily - Boost your energy levels and clear your mind.

4/ Read Every Day - Expand your knowledge and escape the mundane.

5/ Prioritize Your Tasks - Focus on what really moves the needle.

6/ Practice Gratitude - Keep a journal and reflect on what you appreciate every day.

7/ Learn to Say No - Protect your time and commit to what truly matters.

8/ Take Regular Breaks - Prevent burnout and keep your mind fresh.

9/ Network Proactively - Surround yourself with people who inspire you to grow.

10/ Embrace Lifelong Learning - Stay curious and always be willing to learn something new.

11/ Visualize Success - Spend time each day imagining your life as you want it to be.

Don't just dream about a better life—make it happen! 

Start incorporating these habits into your daily routine and watch how they open doors to new opportunities and paths to success. 

What’s your first step going to be? 

Let me know in the comments below!

11visuals

36 Ways to Improve Yourself

Improving oneself is a continuous journey, and there are many avenues to explore that can lead to personal growth. 

Here are 36 ways to work on self-improvement:

1. Read daily: commit to reading books that challenge your perspective.

2. Set clear goals: define what you want to achieve in different areas of your life.

3. Exercise regularly: keep your body healthy with routine physical activity.

4. Meditate: spend time in quiet reflection to improve mindfulness and reduce stress.

5. Wake Up Early: start your day with purpose and quiet time to plan.

6. Learn a New Skill: whether it’s a language or a craft, learning keeps your brain sharp.

7. Keep a journal: write down your thoughts and reflections to understand yourself better.

8. Drink more water: hydration is key to maintaining energy and health.

9. Eat healthier: focus on a balanced diet that fuels your body and mind.

10. Stretch daily: maintain flexibility and prevent injuries.

11. Practice gratitude: remind yourself daily of what you’re thankful for.

12. Declutter: reduce stress by keeping your living and working spaces organized.

13. Travel: experience new cultures and ideas.

14. Volunteer: give back to the community to gain perspective and feel more connected.

15. Limit social media: be mindful of the time you spend online.

16. Set a budget: manage your finances to reduce stress and achieve your financial goals.

17. Practice public speaking: improve your communication skills and confidence.

18. Listen actively: Enhance your relationships by really listening to others.

19. Start a side project: channel your passions into a project.

20. Rest Effectively: Ensure you get enough sleep and take breaks to recharge.

21. Say no: honour your boundaries and spend time on what truly matters to you.

22. Ask for feedback: Understand how others perceive you and where you can improve.

23. Take courses: online courses can help you progress professionally and personally.

24. Stay Inspired: Watch TED talks, listen to podcasts, and seek inspiring content.

25. Network: Connect with people who can influence and motivate you.

26. Forgive: Let go of grudges to free yourself from negative emotions.

27. Be patient. Understand that personal growth takes time.

28. Seek Challenges: Step out of your comfort zone to grow.

29. Practice Positive Self-Talk: Encourage yourself and avoid negative thoughts.

30. Stay Organized: Keep track of your tasks and responsibilities.

31. Reduce Stress: Identify stressors and find ways to mitigate them.

32. Follow Your Curiosity: Allow yourself to explore new topics.

33. Improve Your Body Language: Communicate confidence and openness.

34. Build resilience: learn from setbacks and keep moving forward.

35. Maintain friendships


36. Celebrate small wins

These steps can help guide you on a path to a more fulfilling and balanced life. 

Each step taken is a step toward becoming the best version of yourself.

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!

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.

Found this useful? Share it with others!

Top AI Voice | Certified AI Innovator | I Recommend & Teach Individuals & Businesses AI/AITools that yield atleast 90% efficiency

Awa K. Penn here,  With a substantial following across various platforms, including over 224,000 on Instagram, 650,000 on Facebook, 40,000 on Threads, 3,000+ on TikTok, and over 40,000 newsletter subscribers, I help people build their online brand and share tips that drive growth and success with AI.

I specialize in:

➡️ AI Consulting: 
➡️ Training & Workshops: 
➡️ Custom AI Solutions: 

Let's connect and explore

Awa K. Penn

Stop prompting Claude Co-Work like ChatGPT. 

That’s so wrong.

That’s why your results feel weak.

Claude Co-Work works best when you give clear instructions. 

Just like a new assistant.

Use this simple format every time:

- Context = What you already have
- Task = What you want done
- Output = What you want back
- Constraints = Rules to follow

Four lines. Big difference.

Most people write:

“Help me summarise this.”

Instead, write:

Context = I have 10 documents
Task = Read and summarise them
Output = One text file per summary
Constraints = 100 words each. Do not change originals

Now Claude Co-work knows exactly what to do.


Grab the full Claude Co-work Guide here: https://lnkd.in/dh4JQcdi

--------------------------
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Free courses on AI agents to help you upskill in 2026.

It covers;

✳ Multi AI Agent Systems with crewAI
 Learn how to build multi-agent systems using crewAI.
 Link: https://lnkd.in/eM-hFBqJ

✳ Foundations of Prompt Engineering (AWS)
 Master prompt basics with this free AWS course.
 Link: https://lnkd.in/e5PhYtiX

✳ Introduction to LangGraph
 Learn graph-based agents with Harrison Chase.
 Link: https://lnkd.in/eAXK-Nw5

✳ Large Language Model Agents MOOC
 Deep dive into LLM agents with Dawn Song.
 Link: https://lnkd.in/e5mDib3P

✳ AI Agents in LangGraph
 Build agents with LangGraph + LangChain.
 Link: https://lnkd.in/eZDwizet

✳ Building AI Agents with Multimodal Models
 Use vision + language models with Nvidia.
 Link: https://lnkd.in/eW3-rSJ7

✳ AI Agentic Design Patterns with AutoGen
 Learn agent design patterns from Microsoft.
 Link: https://lnkd.in/eefeXCfC

✳ LLMs as Operating Systems: Agent Memory
 Use LLMs as operating systems with memory.
 Link: https://lnkd.in/eDK_i8Yi

✳ Building Agentic RAG with LlamaIndex
 Build fast RAG agents with LlamaIndex.
 Link: https://lnkd.in/eJ5fmKX3

--------------------------
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NotebookLM just dethroned many AI startups.

This isn’t just a chatbot.

It’s an AI brain that reads your sources and thinks for you.

I just compiled the 21 best things you can now do with it.

→ Upload sources
→ Use Deep Research
→ Chat with your sources
→ Build a big source library
→ Paste raw text as a source
→ Extract info into a Data Table
→ Share notebooks with others
→ Turn sources into a Mind Map
→ Turn sources into Infographics
→ Add any website link as a source
→ Add a YouTube video as a source
→ Turn sources into Audio Overview
→ Turn sources into a Video Overview
→ Track notebook analytics and activity
→ Add sources directly from Google Drive
→ Generate Quizzes from your own content
→ Control notebook settings and behaviour
→ Write your own notes inside NotebookLM
→ Turn sources into a clean report/document
→ Generate Flashcards from your own content
→ Create a Slide Deck presentation from your sources

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Master NotebookLM in 5 minutes

How to create infographics from YouTube with NotebookLM

Here's the exact workflow I use:

- Go to NotebookLM
- Create a notebook
- Go to YouTube and copy any video link
- Paste it into NotebookLM as a YouTube source
- Then, Prompt NotebookLM:
“Summarise the content perfectly for creating an infographic.”
Hit Infographic (top right)

That’s it. You’ll instantly get a clean visual infographic based on the video.

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How to create inforgraphics from Youtube  with NotebookLM

How to prompt ChatGPT in 2026

If your prompt looks like a casual question

You're doing it wrong

Here’s the 6-part structure OpenAI recommends:

1) Start with a role
Give the model a job title
Let it wear one hat at a time

2) Set a real task
Be exact about what you need
Describe the outcome, not the journey

3) Feed it real context
Don't make it guess
Give it data, limits, goals

4) Ask it to reason
Don’t just say “Do this”
Say “Think this through first”

5) Control the output
Tell it how to answer
Short list? Table? Markdown?

6) Define an exit
Decide what’s “done”
Prevent GPT from wandering

----------------------
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How to prompt ChatGPT in 2026

Everyone is obsessed with Vibe Coding

But no on shows you the best ones

So, I teamed up with my man Alex Cinovoj

To show you the best with some concrete examples

10 AI tools that turn plain prompts into full projects.

[🔖 save this to build faster with AI + no code/code]

1) Lovable
Turns plain English into React apps.

2) Replit
AI pair programmer that writes and debugs code in real time

3) V0 by Vercel
Builds full web apps from a text prompt.

4) Emergent
Creates full-stack projects from natural language specs.

5) Manus AI
Turns ideas into content, visuals, or voice with one prompt.

6) Blink dot new
Builds, edits, and previews code in your browser instantly.

7) Rocket dot new
Creates full-stack AI agents and deploys them to the web.

8) Windsurf
AI-powered autonomous coding and debugging assistant.

9) Cursor
An AI-powered code editor built on GPT models.

10) Bolt dot new
One-click AI coding environment for rapid prototyping.

----------------------
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Gemini Vs Chatgpt Vs Midjourney Vs Grok

Which AI image model is the best?

I tested them side by side using the SAME prompts. 

And the results were… not even close. 

Here’s the real ranking (based on the images):

→ Midjourney

✔ Advantages
Best visual quality
Cinematic lighting
High-end textures
Polished, ad-style look

✘ Disadvantages
Poor text handling
Bad with consistency
Hard to control for workflows

→ ChatGPT

✔ Advantages
Reliable prompt accuracy
Clean and usable visuals
Good consistency
Fewer distortions

✘ Disadvantages
Less cinematic than Midjourney
Slightly flatter output

→ Gemini

✔ Advantages
Useful for quick drafts
Simple and fast

✘ Disadvantages
Lower visual quality
Flat and soft textures
Feels less premium

→ Grok

✔ Advantages
Bold and stylized visuals
High contrast
More graphic and “designed” look

✘ Disadvantages
Weaker realism
Not ideal for product shots

--------------------------

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Which ai image model is the best

AI is not replacing project managers.

But project managers who use AI?

They’ll replace the ones who don’t.

Here’s the stack that works.

1. ChatGPT
↳ Where It Fits: Brainstorming, decision support, writing briefs
What It Frees Up: Faster planning, sharper decisions

2. Perplexity
↳ Where It Fits: Research vendors, trends, competitive tools
What It Frees Up: Instant clarity for better choices

3. Fellow AI
↳ Where It Fits: Meeting agendas, notes, action tracking
What It Frees Up: Smoother meetings, better execution

4. Gamma
↳ Where It Fits: Sprint reviews, stakeholder decks, project updates
What It Frees Up: Fast, polished slides without design work

5. Guidde AI
↳ Where It Fits: SOPs, tool walkthroughs, async video guides
What It Frees Up: Scalable onboarding, fewer repeated explanations

6. Notion AI
↳ Where It Fits: Project docs, sprint plans, team knowledge base
What It Frees Up: Centralized knowledge, clean collaboration

7. ClickUp AI
↳ Where It Fits: Smart task updates, daily summaries, project reports
What It Frees Up: Less admin work, faster updates

8. Magical AI
↳ Where It Fits: Autofill emails, project updates, recurring data entry
What It Frees Up: Hours saved on routine typing

9. Zapier AI
↳ Where It Fits: Automate workflows and agents, pull data, update project tools
What It Frees Up: Hands-off execution, powerful integration

--------------------------
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Everyone is talking about Google's Nano Banana Pro

But no one is showing how to make money with it.

Here are 8 ways to start (Anyone can do it):

→ Edit photos for people.
→ Create marketing images fast.
→ Make product photos that sell.
→ Help real estate agents with staging.
→ Sell digital art on popular sites.
→ Teach others with simple tutorials.
→ Build tools with the API if you code.
→ Offer bundles with clear prices.

Watch the full breakdown here, plus where to sell: https://lnkd.in/eYHHHgQj


--------------------------
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This prompt stops ChatGPT from agreeing with everything.

I’ve tested many (this one actually works).

It teaches AI to stay clear, direct, and honest.

I added clear rules:

“DO NOT agree automatically with user statements.”
“Challenge weak logic and point out gaps in reasoning.”

AI needs both:
- Auto-agreeing hides mistakes.
- Weak logic goes unchecked.

Both lead to bad answers.

This prompt forces AI to:
- Ask for evidence when claims lack support.
- Separate facts from opinions.
- Offer alternative views.
- Explain reasoning step by step.
- Focus on practical, real-world thinking.
- Avoid clichés, vague language, and filler.
- Stay calm and professional.

This prompt now lives in my Claude and ChatGPT.
 (You can add it to your custom instructions, too.)

--------------------------
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Claude Co-work can turn days of work into minutes

Copy and use these 7 prompts;

Clean & Organise Downloads Folder
Create a Meeting Summary Document
Build a Meeting Presentation
Research & Write a Topic Paper
Extract Receipt Data into Excel
Improve a To-Do List
Turn a Blog Post into Social Content

Grab the full Claude Co-work Guide here: https://lnkd.in/dh4JQcdi

--------------------------
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2025: Brands spent 7 figures on salaries to test 10 ad concepts 
2026: AI agent trained on $300M sends winning Slack ads

Just tested Parker
(Running it through a few projects).

WHAT IT DOES:
→ Connects to Meta account, reviews, & ad comments
→ Searches TikTok for viral videos in your niche and top-performing hooks 
— Rewrite them as scripts mapped to your brand voice
→ Scans thousands of reviews, extracts pre-purchase phrases
→ Connect to your Meta ad account and surface performance insights
→ Daily Slack briefs deliver fresh hooks overnight

Here are prompts I’ve been running:
- "Monitor every viral TikTok in the creatine niche, find the top-performing hooks, and rewrite them as scripts mapped to creators who match our brand."
- "What are 5 angles I should test that constantly appear in reviews?"
- "What phrases repeatedly show up across our 5-star reviews?"
- "Find me 5 TikToks to recreate as ads for [product]."

How it works:
- Connect your ad account + upload reviews (CSV)
- Parker reads thousands of reviews in seconds
- Ask for angles, hooks, and scripts. Get them instantly
- Parker sends Slack insights from Continuous Research
- Wake up to fresh hooks and scripts ready to test

QUICK TAKEAWAYS:
- One brand attributed $21k of $34k weekly spend to Parker-generated hooks
- Trained on real creative strategy SOPs from Huel ($100M+), 
- The Perfect Jean ($20M), Motion ($300M) 
- Outputs feel like a senior strategist, not generic AI fluff 
- Up to 40x more ad output without hiring creatives


Want to see it in action? Check out Parker: https://heyparker.ai/

P.S. I'm also giving away a list of 100 AI strategists for hire. 

The right brand could hire one of these strategists and 5x their ad output. 

Comment “Parker”, and I'll send them to you.

#ParkerPartner

AI will replace busywork in 2026

But only if you know which tools to use

This cheatsheet gives you everything at a glance.

It covers;

AI Chatbots
AI Presentation
AI Coding Assistance
AI Email Assistance
AI Image Assistance
AI Spreadsheet
AI Meeting Notes
AI Writing Generation
AI Scheduling
AI Video Generation
AI Graphic Design
AI Data Visualization

--------------------------
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How to combine ChatGPT + Feynman Technique.

To learn a subject/Skill faster

Copy & paste this prompt: 

### ROLE ###

 Act like an expert learning coach and tutor who specializes in accelerated learning using the Feynman Technique.

### OBJECTIVE ###

Your objective is to help me learn any topic faster by breaking it down into simple explanations, identifying gaps, and building targeted practice until I can teach it clearly.

### TASK ###

Use the Feynman Technique to help me learn: [TOPIC].

Inputs (if any are missing, ask me up to 3 quick questions first):
- Topic: [TOPIC]
- My current level (beginner/intermediate/advanced): [LEVEL]
- My goal (exam, job skill, curiosity, project): [GOAL]
- Time available (today + weekly): [TIME]
- Target audience to “teach” (12-year-old, smart friend, colleague): [AUDIENCE]

### REASONING ###

1) Define the learning scope: 
- List the 5–8 core subtopics in the smallest sensible order. 
- Mark what is essential vs. nice-to-have for my goal.
2) Quick diagnostic: 
- Ask me 5 short questions to locate gaps, then adapt the plan.
3) Teach-back draft (Feynman Pass 1): 
- Write a plain-language explanation of the topic for [AUDIENCE] in 200–350 words. 
- No jargon unless you define it in one short sentence.
4) Gap extraction: identify every fuzzy term, missing step, or leap in logic from your explanation. Output a “Gaps to Fix” checklist.
5) Targeted micro-lessons: for each gap, give:
  - The simplest explanation
  - One analogy
  - One concrete example
  - One common misconception and correction
6) Teach-back refinement (Feynman Pass 2): rewrite to 120–200 words+fixes.
7) Active recall practice: generate:
  - 10 flashcards (Q/A)
  - 5 exam-style questions with answers
  - 3 exercises/problems (easy/medium/hard) with solutions
8)  Propose a 7-day spaced review schedule 

### OUTPUT FORMAT & STOP CONDITIONS ###

- Be precise; don’t invent facts. 
- Output in Markdown with clear headings and bullet points.
- Keep explanations simple, concrete, and example-driven.

Take a deep breath and work on this problem step by step.

----------------------
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The AGI moment for PowerPoint is here.

While most presentation tools only make slides,

Dokie AI built something much bigger.

A chat-based editor that understands your deck
And makes real changes.

1) Here’s how it works:
- It understands your presentation
- It doesn’t use fixed templates
- It reads your text and slide layout
- It follows your instructions in chat
- Then it updates content, layout, and motion

2) It adds interaction automatically
 • It inserts 3D models
 • It applies gravity and collision effects
 • It creates mini apps inside slides
 • It turns flat slides into clickable experiences

3) It works with your own templates
- These are not generic designs
- You upload your personal template
- It keeps your fonts and colours
- Your whole deck stays on brand

This is not a tool for your slides.
It is your slide team.
It’s what smart creators/brands are using.
To build faster, explain better, and finish decks on time.

If you want to create  interactive slides that scream ‘read me’

Start here: https://lnkd.in/dnhuS6Ry

-----------------------

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Manually testing vibe-coded apps is a silent productivity killer.

This AI has the cure, and it works

You don’t need to code to even do this.

Most teams today struggle with:
- Too many tests
- Fast updates
- Broken checks
- Login problems
- Tools that don’t talk to each other

Most people think testing needs code.
KaneAI by TestMu AI  proves it doesn’t.
KaneAI works like a smart helper for teams.
Not a replacement, just support.

Here’s what it does, in plain English:
✅ You type what you want to test
 KaneAI turns your words into test steps.
✅ It can read Jira tickets and files
It makes test cases from real work.
✅ Works for websites and mobile apps
One place. No switching tools.
✅ Helps check APIs too
Without writing scripts.
✅ If the app changes, tests adjust
You still review. KaneAI fixes small breaks.
✅ Finds problems early
So bugs don’t surprise you later.

What teams notice:
- Less busy work
- Fewer broken tests
- Faster setup
- Easier teamwork
- More time for real thinking

If your team is tired of messy testing, this is worth a look.
Try it today here: https://lnkd.in/dvJK8S8E

-----------------------


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How do I never run out of content? Simple - AI agents.

More specifically, Superagent. 

A new AI product launched by Airtable. 

Here’s what happens behind the scenes.

1️⃣ Tackling the Problem (Plan)
Superagent breaks down the problem into smaller tasks
How to approach the problem
What to research


2️⃣ Deploy Agents (Research)
Multiple AI agents work at the same time
Each one specializes on a different task
They search deeply and check sources

3️⃣ Synthesize (Combine)
All findings come together
Research becomes one clear report
Ideas turn into an action plan

Unlike most AI tools that give old links and surface answers,
Superagent takes more time, checks sources, and connects everything.

↳ What makes this agent workflow different
→ It researches, not just replies
→ It produces real plans, not loose ideas
→ It turns research into rich, interactive, and ready-to-use content

↳ Real example from my carousel

One prompt: build a content growth plan
Agents study Twitter, LinkedIn, TikTok, and Instagram
Patterns are analyzed
Tools are compared
Results are merged into one report

↳ What I get at the end
→ Content ideas
→ A 30-day posting plan
→ What works on each platform

↳ The building blocks are simple
→ Give one clear task
→ Let agents split the work
→ Review the final report

↳ Behind the scenes
→ Deep web research
→ Source checking
→ Platform comparison
→ One Super Report

Want to post without worrying about content research,
Get 2 months for free using AWA2FREE  discount code if you sign up using this link https://lnkd.in/dDiKKAMS

#AirtablePartner

How to automate content creation with AI agents

Everyone is talking about Claude code

But no one tells you it is too technical with few errors

Instead, Replit lets you build, test, and publish in one place.

I built a Research AI web app with Claude  + Replit...

This process replaces a full dev team.

The super simple steps:

1. Find a simple app idea
 - Go to Google and type:
 “AI app for”

2. Get Claude's prompt ready
 - Open Claude and ask it to write the build instructions for Replit.

3. Start in Replit
- Go to replit dot com and click Create.

4. Add your Claude API key safely
- In Replit, open Secrets and add:
 ANTHROPIC_API_KEY = your Claude API key

5. Test the paper generator
- Enter a topic and select:
 paper type, length, academic level, citation style

6. Publish and share
- Click Publish and get your live URL.

AI gets you 80% of the way there.

Start building here: https://lnkd.in/eW_DV5_y

--------------------------

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ChatGPT can act as your Legal Assistant.

Here are 9 prompts to turn ChatGPT into your legal assistant

- Contract drafting
- Clause explanation
- Risk check
- Contract comparison
- Negotiation help
- Policy writing
- Defence lawyer
- Legal email drafting
- Dispute explanation

--------------------------

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Claude just crushed ChatGPT and Gemini with this feature

This might be the best thing AI has ever done

 And it is Claude’s new “Cowork” feature

→ Claude doesn’t just give advice in a chat
→ Now it works with your real files

Here’s how it works:

→ You point Claude to a folder on your Mac
→ Claude can read your stuff
→ Claude can fix, sort, write, and create
→ All inside your own documents

No more copy-paste
No more one-file uploads
No more prompt packs
Just open a folder, & Claude gets to work

What can Claude's co-work do?
- Sort and rename messy downloads
- Turn screenshots into clean spreadsheets
- Write full reports from random notes

Anthropic calls it a “research preview”
But it feels like Claude just got a job

Right now, Cowork is:
→ In the Claude Mac app
→ For Claude Max users
→ Still being tested

If this goes wide
Office work may never be the same
What would you use Cowork for first?

--------------------------
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taplio

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The Battle

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