Dr Emmanuel Ogungbemi

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

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

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

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

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

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

Now, I'd like to talk about you.

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

Here's a roadmap to guide you:

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

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

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

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

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

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

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

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

Head of Data Engineering

Excited to share an essential resource: Linear Regression!

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

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

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


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

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Dr Emmanuel O.

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

Linear Regression

Excited to share an essential resource: Python Machine Learning!

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

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

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


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

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Building Machine Learning Systems with Python

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

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

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

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


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

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Querying Data with Transact-SQL

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

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

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

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

Master these terms and enhance your data analysis skills!

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Data Analysis Vocabulary

Excited to share an essential resource: SQL: Comprehensive Guide and Examples!

This detailed guide covers a wide range of SQL topics, from getting started with basic queries to advanced techniques. 

Perfect for students, professionals, and anyone looking to deepen their SQL knowledge, this document provides valuable insights and practical examples.

Key topics include:
- Getting Started with SQL: Overview, Syntax, and Examples.
- ALTER TABLE: Modify columns, add/drop constraints, and alter table structure.
- AND & OR Operators: Combining conditions in SQL queries.
- Cascading Delete: Maintaining data integrity with cascading deletes.
- CASE Statements: Implementing conditional logic in queries.
- Clean Code in SQL: Best Practices for Writing Readable and Maintainable SQL Code.
- Common Table Expressions (CTEs): Simplifying complex queries with CTEs.
- CREATE DATABASE and CREATE TABLE: Setting up databases and tables.
- Data Types: Understanding different data types in SQL.
- DELETE Statements: Removing data from tables.
- JOINs: Combining data from multiple tables with various types of joins.
- Subqueries: Using subqueries in SELECT, FROM, and WHERE clauses.
- Transactions: Managing data consistency with transactions.
- Indexes: Improving query performance with indexing.
- Stored Procedures: Creating and using stored procedures.


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

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

Certificate of Participation

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

Certificate

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

Certificate of National Service

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

Letter of Appreciation

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

Certificate of Fellowship

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

Certificate of Service

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

Certificate of Attendance

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

Certificate of Merit

Marketing: Copywriting for Social Media

Developing CI/CD Solutions with Azure DevOps

Python Projects

Microsoft Certified: Azure Data Engineer Associate

Microsoft Certified: Azure AI Fundamentals

Microsoft Certified: Azure Data Fundamentals

Microsoft Certified: Azure Fundamentals

Data Engineering Foundations

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

Agile at Work: Building Your Agile Team

Scrum: The Basics

Stay Lean with Kanban

Machine Learning Essentials for Seismic Interpretation

Starting a Memorable Conversation

Artificial Intelligence Foundations: Neural Networks

Diversity, Inclusion, and Belonging

Machine Learning and AI Foundations: Classification Modeling

Body Language for Leaders

Industrial/Oilfield Health, Safety & Environmental Management Appreciation

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

International Association of Engineers

Institution of Chemical Engineers (IChemE)

The Institution of Engineering and Technology

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

Emmanuel Ogungbemi | Substack

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

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

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

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

I coordinate Real Projects Talk for AFBE-UK Scotland

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

Learning SQL

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

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

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

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


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

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Comprehensive Python Cheatsheet

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

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

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

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


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

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Basic SQL

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

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

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

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


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

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Docker Tutorial

I am excited to share an essential resource: Scalable Machine Learning with Apache Spark!

This comprehensive guide is perfect for anyone looking to build and scale machine-learning models using Apache Spark. 

Covering everything from data processing pipelines to distributed hyperparameter tuning, this document provides valuable insights and practical knowledge for data scientists and engineers.

Key topics include:
- Data Processing Pipelines: Creating and managing efficient data workflows with Spark.
- Machine Learning Models: Building, Tuning, and Deploying ML Models with Spark ML.
- Model Tracking and Deployment: Using MLflow to track, version, and deploy machine learning models.
- Hyperparameter Tuning: Performing distributed hyperparameter tuning with Hyperopt.
- Scaling Inference: Scaling the inference of single-node models with Spark.


Enhance your machine learning capabilities and streamline your data engineering processes with this detailed guide!

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Scalable Machine Learning with Apache Spark

Excited to share an essential resource: SQL Fundamentals!

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

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

Key topics include:

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

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Writing Basic SQL Statements

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

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

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

Key topics include:

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


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

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Improving Python and Spark Performance and interoperabi

Pre-Interview Checklist for Big Data Engineer Roles.

1. SQL Essentials:
- SELECT statements, including WHERE, ORDER BY, GROUP BY, HAVING
- Basic Joins: INNER, LEFT, RIGHT, and FULL
- Aggregate functions: COUNT, SUM, AVG, MAX, MIN
- Subqueries, Common Table Expressions (WITH clause)
- CASE statements, advanced JOIN techniques, and window functions (OVER, PARTITION BY, ROW_NUMBER, RANK)

2. Python Programming:
- Basic syntax, control structures, data structures (lists, dictionaries)
- Pandas & NumPy for data manipulation: DataFrames, Series, groupby

3. Hadoop Ecosystem Proficiency:
- Understanding HDFS architecture, replication, and block management.
- Mastery of MapReduce for distributed data processing.
- Familiarity with YARN for resource management and job scheduling.

4. Hive Skills:
- Writing efficient HiveQL queries for data retrieval and manipulation.
- Optimizing table performance with partitioning and bucketing.
- Working with ORC, Parquet, and Avro file formats.

5. Apache Spark:
- Spark architecture
- RDD, DataFrame, Datasets, and Spark SQL
- Spark optimization techniques
- Spark Streaming

6. Apache HBase:
- Designing effective row keys and understanding HBase’s data model.
- Performing CRUD operations and integrating HBase with other big data tools.

7. Apache Kafka:
- Deep understanding of Kafka architecture, including producers, consumers, and brokers.
- Implementing reliable message queuing systems and managing data streams.
- Integrating Kafka with ETL pipelines.

8.Apache Airflow:
- Designing and managing DAGs for workflow scheduling.
- Handling task dependencies and monitoring workflow execution.

9. Data Warehousing and Data Modeling:
- Concepts of OLAP vs. OLTP
- Star and Snowflake schema designs
- ETL processes: Extract, Transform, and load
- Data lake vs. data warehouse
- Balancing normalization and denormalization in data models.

10. Cloud Computing for Data Engineering:
- Benefits of cloud services (AWS, Azure, and Google Cloud)
- Data storage solutions: S3, Azure Blob Storage, and Google Cloud Storage
- Cloud-based data analytics tools: BigQuery, Redshift, and Snowflake
- Cost management and optimization strategies

If I had to start learning #dataengineering all over again

(I will follow this plan, mostly in order)

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

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

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

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

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

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

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

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

Let's break it down:

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

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

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

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

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

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

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

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Querying data with SQL

Understanding Git: Your Ultimate Version Control System 🚀

Ever wondered what makes Git so essential?

Let’s break it down for you:

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

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

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

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

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

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

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

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

Struggling with Data Management? Discover SQL!

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

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

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

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

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

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

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

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

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

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25 Most Common Interview Questions for Data Analyst Positions 🚀

I will hire you if you can answer these questions.

Here are 25 key questions to help you prepare:

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

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

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

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

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

________________________________________

Found these questions helpful?
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So more data analysts can prepare for interviews!

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Good luck with the interviews! 💼

Pyspark can be HARD

But learning these concepts made it easier for you.

𝟭. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗣𝘆𝘀𝗽𝗮𝗿𝗸:
- Spark Architecture & Apache Spark Overview
- Setting up the PySpark Environment
- SparkContext and SparkSession

𝟮. 𝗥𝗗𝗗𝘀 (𝗥𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁𝘀):
- Creating RDDs
- Transformations and Actions (Narrow vs Wide)
- RDD Operations (map, filter, reduce, etc.)

𝟯. 𝗗𝗮𝘁𝗮𝗙𝗿𝗮𝗺𝗲 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗔𝗣𝗜𝘀:
- DataFrame operations (select, filter, groupBy, etc.)
- Reading, writing data & inferring schemas
- Spark File Layout
- How to refer columns in DF and DS

𝟰. 𝗦𝗽𝗮𝗿𝗸 𝗦𝗤𝗟 𝗮𝗻𝗱 𝗗𝗮𝘁𝗮 𝗦𝗼𝘂𝗿𝗰𝗲𝘀 :
- Running SQL Queries on DataFrames
- Registering DataFrames as Temporary Tables or Views
- SQL Functions and UDFs
- Working with various formats (CSV, JSON, Parquet, Avro)
- Hive Tables and External Databases (JDBC)

𝟱. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀:
- Broadcast and Accumulator Variables
- Caching, Persistence, and Memory Management
- Data Skew Handling
- Partition Pruning and Shuffle Optimization

𝟲. 𝗣𝘆𝘀𝗽𝗮𝗿𝗸 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴:
- Structured Streaming Basics
- Windowing Operations
- Integrating with Kafka for Real-time Data Processing
- Fault Tolerance and Checkpointing

Data skills will always be relevant.

So learning skills focused on data will last a lifetime.

Here are 3 career options to consider in data.

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁:
- SQL
- Python
- Excel
- Power BI / Tableau
- Statistical Analysis
- Data Warehousing

𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴:
- SQL
- Python
- Hadoop
- Hive
- HBase
- Kafka
- Airflow
- PySpark
- CI/CD
- Data Warehousing
- Data modeling
- AWS/Azure/GCP

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁:
- SQL
- Python/R
- Artificial intelligence
- Statistics & Probability
- Machine Learning
- Deep Learning
- Data Wrangling
- Mathematics (Linear Algebra, Calculus)

If you've read so far, do SHARE the post.

An aspiring Data Engineer aspirant asked me a question.

Why is PySpark so important to learn?

I showed them this list:

- PySpark + DataFrame API = Data Manipulation
- PySpark + RDD = Distributed Datasets
- PySpark + filter() = Data Filtering
- PySpark + join() = Data Integration
- PySpark + groupBy() = Data Aggregation
- PySpark + orderBy() = Data Sorting
- PySpark + union() = Combining Datasets
- PySpark + withColumn() = Data Transformation
- PySpark + select() = Column Selection
- PySpark + SQL Queries = SQL Integration
- PySpark + createOrReplaceTempView() = Virtual Tables
- PySpark + map() = Data Mapping
- PySpark + reduceByKey() = Data Reduction
- PySpark + partitionBy() = Data Partitioning
- PySpark + broadcast() = Data Broadcasting
- PySpark + accumulators = Shared Variables
- PySpark + Spark SQL = Structured Data
- PySpark + DataFrame Caching = Performance Optimization
- PySpark + Window Functions = Advanced Analytics
- PySpark + UDFs = Custom Functions
- PySpark + Machine Learning = Scalable Models
- PySpark + GraphX = Graph Processing
- PySpark + Streaming = Real-Time Processing
- PySpark + DataFrame Joins = Efficient Merging
- PySpark + MLlib = Machine Learning
- PySpark + Structured Streaming = Continuous Processing
- PySpark + Pipeline API = Workflow Automation
- PySpark + Delta Lake = Reliable Lakes
- PySpark + Databricks = Cloud Platform
- PySpark + ETL Pipelines = Data Extraction
- PySpark + Performance Tuning = Query Efficiency
- PySpark + Cluster Management = Distributed Computing

With consistency and effort, you will get there.

LIKE and SHARE, if you can relate to this post.

Ferenc Fekete

Helping non-tech Founders transform Ideas into SaaS products @ VeryCreatives | Product Agency, SaaS & MVP Development | Startups

I've watched many startup founders fail at the same exact point: turning their validated idea into actual software.

They waste months (or even years):
→ Trying to hire the right developers
→ Burning cash on poor planning and execution
→ Second-guessing every technical decision along the way

Meanwhile, competitors move faster, investors demand traction, and the pressure to deliver keeps building more and more.

After watching this pattern destroy countless promising startups, we built VeryCreatives to fix it.

But here's what makes us different:

We don't just build your MVP and disappear. We're product people who think like founders, not just developers who code to spec.

👉 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻-𝗳𝗶𝗿𝘀𝘁 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵: We start by pressure-testing your assumptions before writing a single line of code. If the market doesn't want it, we'll tell you straight.

👉 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝗳𝗿𝗼𝗺 𝗱𝗮𝘆 𝗼𝗻𝗲: We build like it's our own startup, obsessing over user experience, scalability, and investor readiness from the first sprint.

👉 𝗙𝗿𝗼𝗺 𝗶𝗱𝗲𝗮 𝘁𝗼 𝗲𝘅𝗶𝘁 𝗮𝗻𝗱 𝗯𝗲𝘆𝗼𝗻𝗱: Whether you're at napkin sketch or Series B, we've been there. We've helped founders go from zero to acquisition, and we stick around for the entire journey.

For the past 14 years, we've built products for venture-backed startups, fast-growing scale-ups, and globally recognized brands like Red Bull, Nestlé, and Philips.

The startups we work with have:
✅ Launched investor-ready MVPs in under 90 days
✅ Secured funding from top-tier investors and VCs
✅ Scaled from first users to thousands of paying customers
✅ Successfully exited to strategic acquirers

Got a SaaS idea that's been sitting in your head for months? 
Let's map out your 90-day path to launch → https://verycreatives.com/book-a-call

Co-Founder

At VeryCreatives, we are a team of expert developers who create powerful SaaS apps that solve problems and drive growth for founders with initial funding. We believe in empowering our clients with innovative software that helps them achieve their goals and stand out in their industry.

Our audience consists of driven and ambitious founders who have just received initial funding for their startup. They are looking for a software development partner who understands their vision, can help bring their ideas to life, and deliver results that will help them achieve their business goals.

Founders with initial funding have a pressing need to build their product quickly and efficiently to maximize their chances of success. At VeryCreatives, we understand this need and are dedicated to creating high-quality SaaS apps that not only meet their requirements but also exceed their expectations. We are committed to delivering value and results that help our clients succeed.

VeryCreatives stands out in the market for our ability to deliver high-quality, innovative solutions that meet the needs of our clients. Our unique approach to software development, coupled with our expertise and commitment to excellence, is what drives us to provide outstanding results to our clients.

Industries our clients operate in, using the SaaS products we've built: 
- Digital marketing, Event marketing & Influencer marketing SaaS
- Foodtech & Delivery 
- Fintech, Banking & Investments
- 2-sided marketplace platforms
- Agriculture & Livestock management
- Packaging compliance & Waste

Markets we cover: UAE, EU, UK, US.

The UK is shipping world-class SaaS 🇬🇧

Here's 20 UK startups and the core feature that makes each one worth watching:

Attio (Nicolas Sharp) → AI-native CRM that auto-enriches contacts in real time. ARR quadrupling YoY.

Synthesia (Victor Riparbelli) → Text-to-video with 230+ AI avatars in 140+ languages. $180M Series D.

PolyAI (Nikola Mrkšić) → Voice AI that handles live customer calls in 45 languages. Backed by Nvidia.

Xelix (Paul Roiter) → AI agents for Accounts Payable. Processes 115M+ invoices per year.

Granola (Christopher Pedregal) → AI meeting notepad. Auto-generates structured notes and action items from calls.

Vertice (Eldar Tuvey) → SaaS spend platform that auto-negotiates your renewals. 437% growth rate.

Signal AI (David Benigson) → Real-time monitoring across millions of sources for reputation and regulatory risk.

Plain (Simon Rohrbach) → API-first support where AI agents resolve tickets autonomously. Used by Vercel and Cursor.

Griffin (David Jarvis) → UK's first full-stack Banking-as-a-Service with a full banking license.

Primer (@Paul Anthony) → Single API connecting 100+ payment processors with no-code routing.

hyperexponential (​Amrit Santhirasenan) → No-code pricing models for commercial insurers. Backed by a16z.

Raycast (Thomas Paul Mann) → Keyboard-first productivity launcher. 800+ app integrations with built-in AI.

StackOne (Romain Sestier) → One API to connect 200+ enterprise SaaS integrations. Built for AI agents.

Lindus Health (Meri Beckwith) → Runs full clinical trials 3x faster than traditional CROs.

Metaview (Siadhal Magos) → Auto-generates structured interview notes from live conversations. Used by Sony and Brex.

Sylvera (Allister Furey) → Satellite imagery and ML to rate the actual quality of carbon credits.

Robin AI (Richard Robinson) → LLM trained on 2M+ contracts. Reviews and negotiates legal docs for UBS and Pfizer.

BVNK (Jesse Hemson-Struthers) → Stablecoin payment infrastructure for cross-border business transactions.

Shop Circle (Luca Cartechini) → AI-powered suite for Shopify merchants. Inventory, SEO, analytics in one dashboard.

Tessl (Guy Podjarny) → Forces AI coding agents to write specs before generating code. Prevents hallucinations.

Every one of these started with a sharp product decision: solve one problem exceptionally well, then expand.

What UK startup has caught your eye recently?

Launch products customers love, then exit | 5x exits, $100M ARR created for founders

I’ve developed products for 5x exits

Shock: Don't hire a developer in 2026

Every week, a non-technical founder asks me: "Where do I find good developers?"

My answer: "Why do you need one?"

Usually they don't. Not yet.

Before you hire a developer, you need:

1. Validated problem

→ Real people willing to pay. Not just "interested."


2. Clear scope

→ What are you building? What are you NOT building?


3. User journey mapped

→ Every screen. Every interaction. Every edge case.


4. Prioritized features

→ What's V1 vs V2 vs never?

Without these, you'll waste $15-20k per month while you "figure it out."

If you DO need a developer:

Red flags:

→ Promises fast/cheap (you'll pay later)
→ Doesn't ask about your business model
→ Jumps straight to coding
→ Can't explain trade-offs in plain English

Green flags:

→ Asks more questions than you expect
→ Pushes back on features
→ Talks about users, not just tech
→ Shows similar work (not just "we can do that")

The best developers will tell you what NOT to build.

"The project will cost $50k." - Developer
 
3 months later: $120k. Sh*t. What happened?

Every non-technical founder has heard this: "It will take longer than expected."

But you can prevent it (with some high-school math)

Here's how to build an accurate cost forecast:

Step 1: Break it down absurdly small

Not "user authentication: $5k"

But:

→ Login page design: $500
→ Login backend: $1k
→ Password reset flow: $1.5k
→ OAuth integration: $2k
→ Security testing: $500


Step 2: Add complexity multipliers

→ Simple feature: 1x
→ Medium complexity: 1.5x
→ Complex/uncertain: 2x
→ Integration with third parties: 2.5x


Step 3: Add the "unknown unknowns" buffer

→ New team: +30%
→ Existing team, new domain: +20%
→ Experienced team, known domain: +10%


Step 4: Phase the budget

→ Phase 1: Core features (40% of budget)
→ Phase 2: Secondary features (30% of budget)
→ Phase 3: Polish and optimization (20% of budget)
→ Buffer: (10% of budget)


Step 5: Define cut points

If Phase 1 goes over, what do you cut from Phase 2?

Decide BEFORE you're in crisis mode.


The rule:

If you haven't scoped it in detail, you haven't scoped it.

And if you haven't scoped it, any number is a guess.

So…

Maybe pause that developer project.

I've been part of 5 SaaS acquisitions.

Here's what buyers discuss privately:

When a company gets acquired, founders focus on revenue, growth metrics, and the pitch.

But after the presentation, when the acquirer's technical team goes back to their office, the conversation shifts completely.

I've seen this happen five times now. The questions they ask each other are almost always the same:

→ "Can we maintain this code without them?"

If the answer is no, the price drops or the deal dies. One of our clients got acquired by Adesso ($1.3B IT company) and one of the deciding factors was code quality. Their team reviewed the codebase and actually kept us as the development partner post-acquisition.


→ "How much technical debt are we inheriting?"

Every shortcut the original team took becomes a line item on a spreadsheet. They calculate what it costs to fix and deduct it from the offer. This can knock six figures off valuations.


→ "Can this handle 10x the current load right now?"

Not after a rebuild. Right now, as-is. If the answer requires re-architecting, that's another cost they subtract.


→ "Is the data clean enough to migrate?"

Messy data means months of integration work on their side. They price that in aggressively.


→ "What happens when the founders walk away?"

If all the product knowledge lives in two people's heads with nothing documented, the acquirer just bought a risk.


Most non-technical founders don't think about any of this until they're already in due diligence. By then the damage is done.

Build like someone's reviewing your codebase tomorrow. After 13 years, I've watched acquisition offers show up when founders least expect them.

What would you want to clean up in your tech stack before a buyer looked at it?

I run a 28-person agency. I used to feel guilty about an ‘empty calendar’ on a Monday. Now I try cancel all of my meetings:

And it’s helped us grow revenue 30%+.

Previously I was in every delivery meeting, every project review, every standup at VeryCreatives.

I thought I had to be in the room for things to run.

Now, our fantastic Head of Delivery meets with the PMs directly. 

And an ‘empty calendar’ means brain space to strategise on growth.

Here's what my Monday looks like now:

1. Strategy session with my co-founder Mate
→ Previous week, pipeline, finances, what's planned
→ Keeps us both aligned so neither of us needs to check in again all week

2. Sales sync
→ New leads, who handles what, timing

3. Afternoon: wide open
→ This is the whole point
→ We use that time to plan new internal tools, shape future services, figure out where we're going next

By lunchtime, the operational part of my day is done.

Everything after that is thinking time.

What's one meeting you could delete from your Monday to create space?

My 6-year old son tells $100M private equity firms if product ideas are ‘good’ (I’m serious).

If you can't explain your product to a 6-year old, I’ve got $100,000 on the fact that no one else will get it either.

I sit through dozens of product pitches every year.

The pattern is always the same:

Founder: "So basically, we're building a platform that leverages AI to optimize cross-functional workflows while integrating with existing enterprise systems to enable seamless collaboration and data-driven decision making."

Me: "What does it actually do?"

Founder: "...It helps teams share files."

The 6-year old test:

Can someone who has never heard of your product understand:

1. WHO it's for
2. WHAT problem it solves
3. WHY they should care

In 5 seconds?

If not, here's the formula that works:

"We help [specific person] [do specific thing] so they can [achieve specific outcome]."

Examples:

→ "We help freelancers send invoices so they get paid faster." 
→ "We help HR teams track applicants so they hire better candidates."

Trust me… my 6-year old is a great product idea validation filter.

Can you explain your product in one sentence?

Clay is one of the only companies I’ve seen do this 🔥

When you open the dashboard:

→ There's an AI chat text box.
→ It asks: "What do you want to build?"
→ You type your goal. It takes you to a chat screen. 
→ Then their AI (Sculptor) builds the workflow for you.

I think this solves three problems at once:


1. The blank canvas problem

Clay is powerful but notoriously hard to start with (lot’s of features).

Beginner users will open it, see an empty table, and don't know where to begin.

This prompt box removes that moment. 


2. Slow learning curve

A lot of tools teach you their features via an onboarding. 

But then over a few years they launch 10+ other features you have no clue how to use.

This Clay feature speeds up the learning curve:

→ You describe the outcome
→ The product figures out which features to use
→ You learn by watching it build


3. HUGE time and cost savings. 

For a lot of large products such as Clay, there are off shoots of service based agencies that build with the product for you.

Now you literally do not need to hire an agency.

The product builds what you need for you.

Making it more fun to use.

-

Have you seen other tools do this?

Why US innovation labs pay $500K for what Hungarian teams build for $100K (and get better results):

I just watched an enterprise innovation lab spend $500K on a product that never saw a single real user.

Then in same quarter, we shipped a similar concept for a German client. 

It took six weeks, it cost much less…. and in the end was live and taking payments vs the US one (zero payments).

The problem is US labs have been trained to solve every problem with a 20-person team.

Think about what happens when you have a massive budget:

→ You hire a product manager.
→ You hire three designers.
→ You hire a scrum master.
→ You hire eight developers.
→ You hire two QA engineers
→ And a DevOps team.

Everyone needs to justify their role. So they build. And build. And build.

👉 The PM adds features to show progress
👉 Designers polish screens nobody asked for
👉 Developers architect for scale you don't have
👉 QA tests edge cases that won't exist for years

Six months later, you have a beautiful product that solves nothing particularly well.

OR-

With that same $500k budget, you could run 10 separate validation sprints at $50k each. Even if 8 fail completely, you still walk away with 2 validated concepts to scale.

Compare that to putting the budget on one product that takes six months to discover nobody wants.

Agree or disagree?

Fireflies.ai hit a $1B valuation.

How to copy their MVP strategy:

Founders Sam Udotong and Krish Ramineni would dial into calls as "Fred from Fireflies.ai", sit silently, take notes by hand, and send them 10 minutes later.

They did 100+ meetings like this. Completely manual. 🤯

→ Zero development cost to validate
→ Learned exactly what users needed (by doing the job themselves)
→ Proved people would pay $100/month BEFORE investing in code
→ Only automated after hitting $750/month revenue

What I take from this:

Before you scope features, before you wireframe, before you hire anyone:

Can you deliver the core value manually? Without hiring any developers?

If yes, do that first. Talk to those customers. Understand the problem deeply.

Then build.

-

P.s.

We use Fireflies.ai at VeryCreatives.

We switched from Fathom because it felt more complete and could even log our internal calls in Hungarian. 🇭🇺

Could you imagine Fred joining your call to take notes?

What's stopping you from delivering your product's value manually this week?

The fastest way to make $100k MRR with SaaS 

Copy all of your competitors features:

No, seriously.

But only once you take this into account.

Your competitor has 50 features. 
They’re proven over 5+ years.
They’re clearly making revenue.

Just copy them all right?

Here's the problem:

Your competitor built those 50 features over 5 years.

They added most of them because:

→ A big customer asked for it
→ Someone on the team thought it was cool
→ They were trying to justify a price increase

They probably regret half of them.

What to do instead:

1. Identify their core value proposition (usually 2-3 features)
2. Find which features their customers actually use (check G2 reviews, Reddit, support forums)
3. Identify what they do poorly (read 1-star reviews)
4. Build that. Better.

Don't compete on feature count. 

You'll lose.

Compete on solving one problem exceptionally well.

What's the one thing your competitor does that you could do 10x better?

Hiring a developer is the most expensive mistake a non-technical founder can make. Do these 7 things to save $50k+ for your MVP (avoid Upwork/Fiverr):

1. Define your product strategy first

→ Spend 2-3 days defining exactly what you're building and why. 
→ Don’t scroll Upwork/Fiver for a developer.


2. Create detailed user journeys

→ Every screen. Every interaction. Every edge case.
→ If you can't draw it, you can't build it.
→ Otherwise you’ll end up trusting a developer with the entire thing.


3. Prioritize features ruthlessly

→ What's your V1? V2? Never?
→ Cut 50% of what you think you need.


4. Wireframe everything

→ Before design and before code. Do wireframes.
→ It’s cheap to change (trust me on that one). 


5. Weekly demos with the developer

→ See progress. Catch misunderstandings early.
→ Fast feedback loops save thousands.


6. Fixed scope, not fixed time

→ Tell the developer "Build these 10 features" not "work for 3 months."
→ Not "work for 3 months." or "work for 10 hours this week."


7. Kill features that aren't working

→ Sunk cost fallacy kills startups.


The math:

A few days of product strategy work upfront can prevent $50k-$100k in wrong features.

I've seen founders spend $80k building the wrong thing. Then start over.

Don't be that founder.

Wispr Flow is blowing up right now 🔥

Their onboarding is why:

→ It’s a Voice-to-text app valued at $700M.
→ They are currently growing 50%+ month-on-month.
→ In 2025, they 10x’d customers in 5 months.

During onboarding, you speak to configure your settings. 

By the time setup is done, you've already used the core feature without realizing it. 

Brilliant!

After 13 years of building products, the biggest onboarding mistake I see is separating setup from usage. 

Founders build welcome sequences with videos and walkthroughs. But then… The customer finishes onboarding having never done the one thing the product exists for.

Wispr Flow makes setup and the core action the same thing.

The whole point of onboarding is getting the customer from zero to value fast. 

If your product needs 10 steps before a user sees value, think about what you can cut. 

The best V1 products I've worked on put the core experience first and build everything else around it.

How fast does your product get a new user to the thing they signed up for?

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