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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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

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

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/

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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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

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

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

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 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.

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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💬 Comment

So more data analysts can prepare for interviews!

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Good luck with the interviews! 💼

You’ve been missing out! 

See What Every Data Expert Knows But Isn't Telling You!

You need to get a team of specialists and make sure the entire data life cycle is covered

- Data Engineers: Are you just storing data, or are you transforming it?
- Analytics Engineers: Is your data modelling just busy work, or are you gaining breakthrough insights?
- Data Analysts: Are your reports ending up in a drawer, or are they driving real action?

Each role is a critical piece of the puzzle in the data-to-decision process. 

Understanding this can dramatically boost your career and business strategy!

🌟 Don’t let your competitors get ahead! 

Is your team aligned with these best practices?

cc: https://rpst.in/lBuOU

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.

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

- You prefer Excel over Pandas for quick data checks.
- Statistics formulas feel like a foreign language sometimes.
- Your machine learning model didn’t perform as expected.
- You rely on Google for the syntax of a common Python function.
- You forgot how to interpret a p-value during a meeting.
- Your Jupyter notebook is a mess of commented-out code.
- You still find deep learning architectures intimidating.
- You’re NOT a data scientist when...

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

💡 Repost to inspire others on their data science journey!

I build distribution and revenue engines for AI companies.

Over the past years, we’ve built one of the largest AI-focused ecosystems globally:

1.4M+ LinkedIn audience
530K+ AI newsletter subscribers

Our audience consists of founders, operators, investors and enterprise decision-makers across AI, SaaS, Cloud and enterprise technology.

This isn’t about impressions.
It’s about category positioning, authority and enterprise pipeline.

I work selectively with AI companies that want to:

• Establish category leadership
• Accelerate enterprise visibility
• Scale revenue through strategic distribution
• Build long-term ecosystem presence

Partnerships are structured around long-term strategic distribution not one-off sponsored placements.

Past collaborations include AWS, NVIDIA, Alibaba, Huawei, Red Hat, EY, BlackBerry and Synthesia.

For strategic partnership inquiries:
paul_akira_storm@icloud.com


Paul Storm

>> Harvard just removed the velvet rope.

Let that sink in.

The same AI education once reserved for a 200000 dollar degree
- is now online
- free
- complete
- and unfiltered.

This is not marketing content.
Not a teaser.
Not highlights.

It is the real curriculum.
Full lectures.
Full frameworks.

The exact mental models shaping the next generation of leaders.
Here is the uncomfortable truth most people miss.

𝗧𝗵𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝗶𝘀 𝗻𝗼 𝗹𝗼𝗻𝗴𝗲𝗿 𝗮𝗰𝗰𝗲𝘀𝘀.
𝗧𝗵𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝗶𝘀 𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗲.

Harvard did not lower the bar.
They removed the gatekeeper.

Six open tracks now cover what the market quietly expects leaders to understand
1️⃣ Generative AI strategy
2️⃣ Machine learning logic
3️⃣ Data science foundations
4️⃣ Computational thinking for executives
5️⃣ Edge AI
6️⃣ High dimensional analysis

This is not about becoming an engineer.
It is about not becoming irrelevant.

Knowledge is no longer scarce.
Execution is.

Harvard opened the door.
𝗪𝗵𝗼 𝗶𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗮𝗹𝗸𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵

Find the links below:

1️⃣ Generative AI for Leaders
Strategy and policy from the Harvard Kennedy School
🔗 https://lnkd.in/drpQDwMk
2️⃣ CS50 AI with Python
How intelligent systems actually think
🔗 https://lnkd.in/dRWVXa6m
3️⃣ Data Science Machine Learning
Build a real recommendation engine
🔗 https://lnkd.in/d9STFdNm
4️⃣ CS50 for Business
Computational thinking for executives
🔗 https://lnkd.in/dpKPt-sV
5️⃣ Fundamentals of TinyML
AI on tiny edge devices
🔗 https://lnkd.in/dgfyBhkC
6️⃣ High Dimensional Data Analysis
When Excel breaks
🔗 https://lnkd.in/dXSt3JKj

𝗡𝗼𝘁𝗲
Courses 3, 5, and 6 are free via Audit This Course


----
👉 Love my content?

☑ Follow me Paul Storm  

👉 Found this helpful? Share it! ♻️

AI Distribution & Strategic Partnerships | 1.4M LinkedIn Audience | 570K+ AI Newsletter | Enterprise Revenue Growth

Google is offering FREE AI courses nwith certificates.

And almost nobody is talking about it.

If you’re not learning AI right now, you’re falling behind.

Here are 7 free AI courses from Google you can start today:

⸻

1️⃣ AI Fundamentals

Build a strong foundation in AI.
Learn core concepts + how to write powerful prompts using a structured framework.

🔗 https://lnkd.in/ddvC9pXm

⸻

2️⃣ AI for Brainstorming & Planning

Use AI as your creative co-pilot.
Brainstorm ideas, evaluate them, and prioritize smarter.

🔗 https://lnkd.in/dDHdinbj

⸻

3️⃣ AI for Research & Insights

Research in minutes, not hours.
Use Gemini and NotebookLM to summarize multiple sources instantly.

🔗 https://lnkd.in/di_mpJBh

⸻

4️⃣ AI for Writing & Communication

Turn messy meeting notes into clean summaries + action items with Gemini.

🔗 https://lnkd.in/d27xRvHq

⸻

5️⃣ AI for Content Creation

Generate high-quality images & videos with Gemini.
Turn boring slide decks into engaging visual presentations in Google Slides.

🔗 https://lnkd.in/dCzTWYrj

⸻

6️⃣ AI for Data Analysis

Track metrics.
Clean messy data.
Measure performance — faster.

🔗 https://lnkd.in/d7sAz78A

⸻

7️⃣ AI for App Building

Build a working web app using AI.
No code required.

🔗 https://lnkd.in/dUHQt78B

⸻

💡 The best part?
They’re free.
They’re practical.
And they come with certificates.

AI won’t replace you.
But someone using AI will.

Save this.
Share this.
Start today. 🚀

♻️ Repost so your network doesn’t miss this.
⚡ Follow Paul Storm for no-BS AI tools & insights that actually work.

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

♻️ Repost this to help others in your network.
And follow Paul Storm for more!

No alternative text description for this image

>> 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲𝘀 𝗮𝗿𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲!

So I compiled the best 𝗙𝗥𝗘𝗘 𝗮𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝘃𝗲𝘀!

Adobe Premier → Davinci Resolve
Adobe Lightroom → Darktable
Buzzsumo → Marketing Miner
Adobe After Effects → Natron
Adobe Animation → Pencil2D
Skill Share → Khan Academy
Adobe Audition → Audacity
Adobe Indesign → Scribus
ChatGPT4 → Huggingchat
Powerpoint → Libre Office
Adobe Photoshop → Krita
Matlab → GNU Octave
Salesforce → HubSpot
Illustrator → Inkscape
Keyhole → Analisa
Netflix → Freevee
Istock → Flickr

---

♻️ Repost this to help others in your network.
And follow Paul Storm for more!

>> 99% of professionals will ignore this.

And that’s exactly why they’ll be replaced.

Stanford University just made its AI education free to the world…

Learn from professors who shaped the field. The material is recorded, self-paced and free. This curriculum helps anyone getting started with, building or managing AI.

𝟭. 𝗖𝗦𝟮𝟮𝟭 - 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲
→ Foundations of AI: search, reasoning, planning, decision-making:
https://hubs.li/Q03KwyM-0

𝟮. 𝗖𝗦𝟮𝟮𝟵 - 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗔𝗻𝗱𝗿𝗲𝘄 𝗡𝗴)
→ Supervised learning, unsupervised learning, deep dive into algorithms.
https://hubs.li/Q03KwxSk0

𝟯. 𝗖𝗦𝟮𝟯𝟬 - 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗔𝗻𝗱𝗿𝗲𝘄 𝗡𝗴)
→ Neural networks, CNNs, RNNs, best practices for building deep models.
https://hubs.li/Q03KwxTf0

𝟰. 𝗖𝗦𝟮𝟮𝟵𝗠 - 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗧𝗵𝗲𝗼𝗿𝘆
→ Mathematical underpinnings and rigorous ML proofs.
https://hubs.li/Q03Kwy3r0

𝟱. 𝗖𝗦𝟮𝟯𝟰 - 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
→ Markov decision processes, Q-learning, policy gradients.
https://hubs.li/Q03KwxRt0

𝟲. 𝗖𝗦𝟮𝟮𝟰𝗡 - 𝗡𝗟𝗣 𝘄𝗶𝘁𝗵 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴
→ Word embeddings, transformers, modern NLP architectures.
https://hubs.li/Q03KwxwQ0
 
When Stanford makes AI courses free, is it democratizing knowledge or raising the bar for everyone else?
----
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Don't miss out! For exclusive AI and tech insights trusted by 557,000+ professionals at Microsoft, Google, Amazon, and more—join my free newsletter for cutting-edge strategies to keep you ahead in AI.

🔗 Subscribe now: https://lnkd.in/eFNvmcYa

Watch at YouTube: https://lnkd.in/dbSsN36G

Listen at

- Amazon Music: https://lnkd.in/daTPRypn
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- Spotify: https://lnkd.in/dQ9gjG2E
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🚀 Leverage AI, boost your career, and master the future with over 80+ AI
bestseller eBooks.

🔗 Get your eBooks here: https://lnkd.in/dY2bXE2e

Feeling stuck when prompting ChatGPT? 

You’re not alone. 

But what if you had a master key to unlock its full potential—on demand?

📌 Meet the Grok: All-in-One Cheatsheet—your ultimate prompt engineering sidekick. Whether you're a marketer, developer, entrepreneur, or just trying to get the most out of AI tools, this guide has something for everyone.

Here’s what makes it insanely valuable:

  🔹 Plug-and-play prompt formats for marketers, developers, and business owners

  🔹 A powerful C.R.E.A.T.E formula to refine and perfect your prompts

  🔹 Role-based guidance to help you ask questions like a pro (think Analyst, Copywriter, Coach…)

  🔹 Tailored outputs for any format—SEO keywords, essays, web copy, and more

  🔹 Pro tips for priming prompts like a human-AI whisperer

💡 Save this. Print it. Bookmark it.

This isn't just another cheatsheet—it’s a daily productivity accelerator for anyone serious about working smarter with AI.

👇 Drop a “🔥” if you want more practical resources like this.
  And tag a friend or teammate who NEEDS this on their desktop!

 Love my content?
 
Follow me Paul Storm   
 
Found this helpful? Share it!

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The Battle

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Paul Storm

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The Battle

Dr Emmanuel Ogungbemi

Paul Storm

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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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