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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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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- 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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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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Python Basics

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!

Some Power BI interview questions for an analyst interview :

1. What is Power BI, and how do you use it?

2. How do you connect to data sources in Power BI?

3. What is a measure in Power BI, and how do you create one?

4. What are the differences between calculated columns and measures in Power BI?

5. Can you explain the concept of drill-through in Power BI and give an example?

6. How do you create a dashboard in Power BI, and what are the best practices?

7. How do you use Power Query in Power BI to clean and transform data?

8. What is DAX, and how do you use it in Power BI?

9. Can you explain the difference between a table and a matrix visual in Power BI?

10. How do you use Power BI to create a report that updates automatically based on new dataset? 

11. How do you share your reports and dashboards with others in Power BI?

12. How do you handle large datasets in Power BI?

13. Can you give an example of a complex data model you have built in Power BI?

14. How do you handle data refreshes in Power BI, and what options are available?

15. How do you handle security and permissions in Power BI?

16. What are some limitations of Power BI, and how would you work around them?

17. Can you explain the difference between calculated columns and measures in Power BI?

18. How do you create a drill-through report in Power BI?

19. How do you create a custom visualization in Power BI?

20. How do you optimize the performance of a Power BI report?

21. Can you explain the difference between DirectQuery and Import modes in Power BI?

🚀 Marketing Strategist | 📈 Social Media Marketing | 🌟 Product Hunt Expert | 🤝 LinkedIn Influencer | 💬 Helping Students | 📩 DM for Collaboration

Hi, I Am Suraj Kumar Mehta Social Media Influencer.

Suraj Kumar Mehta

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Did you hear about Airpost?

They didn’t win because it’s AI.

It won because it survived real budgets.

When you’re managing millions in spend,
you don’t care about features.
You care about what holds under pressure.

Here’s the test most tools fail:

Can performance stay stable
when volume increases?

In one case, Airpost-powered ads:

→ Supported $1M+ in live spend
→ Produced multiple top-10 spenders
→ Delivered ~40% better CAC vs internal benchmarks
→ Held performance as scale increased

That’s not optimization.
That’s infrastructure.

The reason is simple.

Airpost doesn’t treat ads as content.
It treats them as systems.

Every ad is broken down by:

→ Strategic intent
→ Narrative mechanics
→ Execution variables

Those patterns are tracked,
refined,
and reused with intention.

That’s why teams don’t churn.

→ $1M+ ARR in under six months
→ 100% long-term renewals
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Hype doesn’t get renewals.
Performance does.

This is what happens
when AI handles scale
and humans handle judgment.

Airpost doesn’t replace teams.
It gives them a system
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At enterprise scale,
that’s the difference between growth
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The Battle

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

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