Tech workshops with Stelios

Parallel Processing with Python

Modern software often needs to do many things at the same time to run faster and scale better. This includes data processing, web services, and machine learning workloads. Understanding parallel and concurrent execution is now an important skill for Python developers.
This session gives a clear and practical introduction to parallel processing in Python. It focuses on the main ideas and shows when and how to use different approaches correctly.

Who is this for?

Students, developers, and anyone who wants to understand how Python programs can run faster by doing work in parallel. This session is useful if you want to speed up Python programs, understand the difference between threads and processes, and build more efficient and scalable applications.

Who is leading the session?

The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor and MSc Programme Director at Birkbeck, University of London.

He works in distributed systems, cloud computing, operating systems, and Python-based data processing. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked with Huawei, IBM, Autodesk, and several startups. Since 2018, he has been teaching at Birkbeck and founded Warestack in 2021.
What we will cover

Requirements

A laptop with Python installed (Windows, macOS, or Linux), Visual Studio Code, and Python pip. Lab computers can be used if needed.

Format

This is a hands-on introduction with examples and short exercises. Topics include what concurrency and parallelism mean, threads vs processes in Python, the Global Interpreter Lock explained simply, using threading for I/O-heavy tasks, using multiprocessing for CPU-heavy tasks, basic use of concurrent.futures, common problems like race conditions, and when parallelism is not the right choice.

A 1.5-hour live session with short theory explanations, live coding, and guided exercises. The session runs in person, with streaming available for remote participants.

Prerequisites

Basic to intermediate Python knowledge, including functions, loops, and basic data structures.

Do you work with tabular data? Learn how to clean, prepare, and organise datasets properly in Python.

Data Cleaning with Python Pandas

Working with real data means dealing with missing values, errors, duplicates, and inconsistent formats. Before any analysis or machine learning, data must be cleaned and prepared properly. Data cleaning is one of the most important and time-consuming tasks in data work.
This session gives a clear and practical introduction to data cleaning using Python and Pandas. It focuses on common real-world problems and shows simple, correct ways to fix them.

Who is this for?

Students, developers, and anyone who works with data and needs to clean and prepare datasets using Python. This session is useful if you work with messy files such as CSV or Excel, want to understand how Pandas handles missing or incorrect data, and want to build reliable data analysis pipelines.

Who is leading the session?

The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor and MSc Programme Director at Birkbeck, University of London.

He works in data processing, distributed systems, cloud computing, and Python-based analytics. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked with Huawei, IBM, Autodesk, and several startups. Since 2018, he has been teaching at Birkbeck and founded Warestack in 2021.

What we will cover

This is a hands-on introduction with real examples and short exercises. Topics include loading data with Pandas, inspecting datasets, handling missing values, fixing data types, removing duplicates, cleaning text data, filtering and transforming columns, combining datasets, and common data cleaning mistakes to avoid.

Requirements

A laptop with Python installed (Windows, macOS, or Linux), Visual Studio Code, and Python pip. Lab computers can be used if needed.

Format

A 1.5-hour live session with short explanations, live coding, and guided exercises. The session runs in person, with streaming available for remote participants.

Prerequisites

Basic to intermediate Python knowledge, including functions, loops, and basic data structures. Some familiarity with Pandas is helpful but not required.

Modern data analytics often involves large datasets that do not fit into memory or are too slow to process with traditional tools. Apache Spark is one of the most widely used frameworks for scalable data processing and analytics.

This session gives a clear and practical introduction to data analytics using Apache Spark and Python. It focuses on understanding how Spark works and how to use it to analyse large datasets efficiently.

Who is this for?

Students, developers, and anyone who works with data and wants to analyse large datasets using Python. This session is useful if you have outgrown Pandas, work with big CSV or log files, or want to learn how modern data analytics systems handle scale.

Who is leading the session?

The session is led by Dr. Stelios Sotiriadis, CEO of Warestack and Associate Professor and MSc Programme Director at Birkbeck, University of London. He works in big data systems, distributed computing, cloud platforms, and Python-based data analytics. He holds a PhD from the University of Derby, completed a postdoctoral fellowship at the University of Toronto, and has worked with Huawei, IBM, Autodesk, and several startups. Since 2018, he has been teaching at Birkbeck and founded Warestack in 2021.

What we will cover

This is a hands-on introduction with practical examples and short exercises. Topics include loading data into Spark, understanding DataFrames, basic transformations and actions, filtering and aggregations, grouping and joins, using Spark SQL, and understanding when and why Spark is better than Pandas.

Requirements

A laptop with Python installed (Windows, macOS, or Linux), Visual Studio Code, and Python pip. Spark will be provided via local setup or lab environment. Lab computers can be used if needed.

Format

A 1.5-hour live session with short explanations, live coding, and guided exercises. The session runs in person, with streaming available for remote participants.

Prerequisites

Basic to intermediate Python knowledge, including functions, loops, and basic data structures. Prior experience with Pandas is helpful but not required.