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.

Dr. Joerg Storm

Your success, my mission | CIO | CTO | Mercedes-Benz, (ex) Siemens | The only newsletter you need for Tech/AI🔔drstorm.substack.com🔔 Follow for posts about business & Tech/AI

I am 𝗗𝗿. 𝗝ö𝗿𝗴 𝗦𝘁𝗼𝗿𝗺, 𝗮𝘂𝘁𝗵𝗼𝗿 and 𝗳𝗼𝘂𝗻𝗱𝗲𝗿 𝗼𝗳 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗹𝗱’𝘀 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿𝘀, read by 𝟱70,𝟬𝟬𝟬+ 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝟭𝟵𝟳 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀, with a combined 𝟭.4𝗠+ 𝗳𝗼𝗹𝗹𝗼𝘄𝗲𝗿𝘀 𝗼𝗻 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻.

I run a 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗺𝗲𝗱𝗶𝗮 𝗮𝗻𝗱 𝗮𝗱𝘃𝗶𝘀𝗼𝗿𝘆 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺 spanning LinkedIn, a leading AI newsletter, and a C level podcast, reaching 𝗖𝗘𝗢𝘀, 𝗖𝗜𝗢𝘀, 𝗖𝗧𝗢𝘀, 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀, 𝗮𝗻𝗱 𝘀𝗲𝗻𝗶𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗺𝗮𝗸𝗲𝗿𝘀 worldwide.

My focus is helping leaders move from 𝗔𝗜 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻, while building 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘁𝘆, 𝘃𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗶𝗺𝗽𝗮𝗰𝘁.

What I do

𝟭. 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗚𝗲𝗻𝗔𝗜 𝗮𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗔𝗱𝘃𝗶𝘀𝗼𝗿𝘆
I advise enterprises and scale ups on 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗲. The focus is execution, not theory.

𝟮. 𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗮𝗻𝗱 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝗚𝗿𝗼𝘄𝘁𝗵
As Founder and CEO of 𝗗𝗿. 𝗦𝘁𝗼𝗿𝗺 𝗔𝗱𝘃𝗶𝘀𝗼𝗿𝘆 𝗚𝗺𝗯𝗛, my team runs global 𝗟𝗶𝗻𝗸𝗲𝗱𝗜𝗻 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆, 𝗰𝗼𝗻𝘁𝗲𝗻𝘁, 𝗮𝗱𝘀 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 end to end. We deliver 𝗴𝗿𝗼𝘄𝘁𝗵 from startups to 𝗙𝗼𝗿𝘁𝘂𝗻𝗲 𝟱𝟬𝟬 companies.

𝟯. 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗕𝗿𝗮𝗻𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽
We position leaders as 𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝗲 𝘃𝗼𝗶𝗰𝗲𝘀 and convert visibility into 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝗰𝗲 𝗮𝗻𝗱 𝗶𝗻𝗯𝗼𝘂𝗻𝗱 𝗱𝗲𝗺𝗮𝗻𝗱.

𝟰. 𝗣𝗼𝗱𝗰𝗮𝘀𝘁, 𝗞𝗲𝘆𝗻𝗼𝘁𝗲𝘀, 𝗮𝗻𝗱 𝗧𝗲𝗮𝗰𝗵𝗶𝗻𝗴
I host an 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗔𝗜 𝗽𝗼𝗱𝗰𝗮𝘀𝘁 with CEOs, CIOs, CTOs, and founders, and work globally as a 𝗸𝗲𝘆𝗻𝗼𝘁𝗲 𝘀𝗽𝗲𝗮𝗸𝗲𝗿, 𝗺𝗼𝗱𝗲𝗿𝗮𝘁𝗼𝗿, 𝗮𝗻𝗱 𝗱𝗼𝗰𝗲𝗻𝘁 for International Management.

Who we work with

Our clients include 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗔𝗪𝗦, 𝗔𝗹𝗶𝗯𝗮𝗯𝗮, 𝗛𝘂𝗮𝘄𝗲𝗶, 𝗘𝗬, 𝗪ü𝗿𝘁𝗵, 𝗟𝘂𝗳𝘁𝗵𝗮𝗻𝘀𝗮, 𝗥𝗲𝗽𝗹𝗶𝘁, 𝗚𝗮𝗺𝗺𝗮, 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝗮, and leading AI driven enterprises worldwide.

If you want to 𝗳𝘂𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗼𝗳 𝘆𝗼𝘂𝗿 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻, 𝘀𝗰𝗮𝗹𝗲 𝗔𝗜 𝗯𝗲𝘆𝗼𝗻𝗱 𝗽𝗶𝗹𝗼𝘁𝘀, or 𝗯𝘂𝗶𝗹𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲, feel free to connect.

For collaborations and inquiries
digitalstormweekly@gmail.com

To book a conversation
https://topmate.io/dr_joerg_storm

Founder, Publisher

The "Digital STORM" weekly is one of the fastest-growing AI newsletter in the world, with thousand of readers working at companies like Apple, Meta, Google, Microsoft. 

Its all about AI, Future Trends, Digital Transformation for executives, decision makers, industry leaders and all others which want to grow

>> Robots doing Kung Fu on live TV is not a gimmick.

It is a manufacturing readiness signal.

In this CCTV+ (Video News Agency) Gala clip you can see the jump from 2025 to 2026.

2025 was novelty.
2026 is 𝗯𝗮𝗹𝗮𝗻𝗰𝗲, 𝗳𝗮𝘂𝗹𝘁 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝘆, 𝗽𝗿𝗲𝗰𝗶𝘀𝗲 𝗮𝗰𝘁𝘂𝗮𝘁𝗶𝗼𝗻, and 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 with kids.

Drunken boxing and nunchaku are not “cute”.
They are stress tests for control under chaos.

The next frontier is not dancing.
It is 𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗶𝗮𝗹 𝗿𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 in messy environments.

By 2028, the winners will not be the best demos.
They will be the teams that nail:
• safe work next to humans
• rapid recovery from slips and collisions
• cheap serviceability and uptime

China is already pushing this at scale.

Reuters reports four major startups featured on the 2026 Gala, including Unitree, as part of a broader push into AI driven manufacturing. 

The IFR reports 4.28 million industrial robots operating in factories worldwide. 
McKinsey & Company’s framing is blunt: work becomes a partnership of people, agents, and robots. 

So the real jobs question is not “Will robots replace us”.
 It is “Which workflows get redesigned first, and who captures the value”.

𝗔𝗴𝗿𝗲𝗲 𝗼𝗿 𝗱𝗶𝘀𝗮𝗴𝗿𝗲𝗲: 𝗯𝘆 𝟮𝟬𝟮𝟴 𝗵𝘂𝗺𝗮𝗻𝗼𝗶𝗱𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮 𝗻𝗼𝗿𝗺𝗮𝗹 𝗰𝗼𝘀𝘁 𝗹𝗶𝗻𝗲 𝗶𝗻 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀.

----

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Founder of one of the world’s largest AI newsletters (570K+ readers) | 1.4M LinkedIn Followers | Social Media & LinkedIn Growth Agency | Enterprise GenAI & Strategy Advisor | Docent & Keynote Speaker

>> 𝗜 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 𝗜 𝘄𝗮𝘀 𝗔𝗜 𝘀𝗮𝘃𝘃𝘆.

𝗜 𝘄𝗮𝘀𝗻’𝘁 𝗲𝘃𝗲𝗻 𝗰𝗹𝗼𝘀𝗲.

Then I saw a single team using over 100 tools, and realized I was basically running a tricycle in a Formula 1 race.

AI isn’t just chatbots or content generators anymore.
It’s entire ecosystem for ideas, websites, writing, meetings, automation, design, audio, video, SEO, marketing, and even Twitter growth.

The people winning quietly aren’t smarter, they just know which tools to plug in at the right moment.

Those who ignore this wave risk being left behind faster than they imagine.

Here’s the list of 135 mind-blowing AI tools:
1. Ideas: Claude, ChatGPT, Bing Chat, Perplexity, Copilot
2. Website: Dora, 10Web, Framer, Unicorn, Style AI
3. Writing: Jasper, HIX AI, Longshot, Textblaze, Jenny AI
4. Meeting: Tldv, Krisp, Otter, Fathom, Sembly AI
5. Chatbot: Droxy, Chatbase, Chatsimple, CustomGPT, Mutual info
6. Automation: Make, Zapier, Xembly, Bardeen, Levity
7. UI/UX: Figma, UiMagic, Uizard, InstantAI, Penpot
8. Image: Dreamina AI, Leap AI, Midjourney, Stability AI, Fotor
9. Video: Dreamina AI, HeyGen, InVideo, Eightify, Morphstudio xyz
12. Audio: Lovo ai, Eleven labs, Songburst AI, Adobe Podcast, Resemble AI
13. Presentation: Decktopus, Slides AI, Pitch, Designs AI, Beautiful AI
14. SEO: VidIQ, Seona AI, BlogSEO, Keywrds ai, Semrush
15. Design: Canva, Flair AI, Designify, Clipdrop, Magician design
16. Logo Generator: Looka, Designs AI, Brandmark, Stocking AI, Namecheap
17. Prompts:FlowGPT, Alicent AI, PromptBox, Promptbase, Snack Prompt
18. Productivity: Merlin, Tinywow, Notion AI, Adobe Sensei, Personal AI
19. Marketing: Pencil, Ai-Ads, AdCopy, Simplified, AdCreative
20. Twitter: Typefully, Tweetlify, Tapilo, Hypefury, TweetHunter

Here’s a gentle reminder for anyone looking to level up:
● Awareness beats effort alone
● Hidden tools create visible results
● Early adoption compounds faster than skill
● Productivity now lives in ecosystems, not apps

Which of these 135 AI tools would you try first to level up your workflow?

----

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🎧 𝗟𝗶𝘀𝘁𝗲𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗳𝘂𝗹𝗹 𝗲𝗽𝗶𝘀𝗼𝗱𝗲 𝗵𝗲𝗿𝗲 

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#ai #productivity #automation #growth #innovation

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