Web Scraping Made Simple: Unlocking Data with Beautiful Soup

This 50-minute session introduces attendees to the fundamentals of web scraping using Python’s Beautiful Soup library. Attendees will learn how to navigate the complexities of HTML structures to extract valuable data efficiently. Key skills and concepts covered include:

Understanding HTML and CSS: Learn how web pages are structured to identify the data you need.

Setting up Beautiful Soup: Install and initialize the library to parse web content.

Scraping Techniques: Use tags, attributes, and classes to locate and extract specific elements from web pages.

Handling Dynamic Content: Work with tools like requests to scrape static pages and integrate with libraries like Selenium for dynamic content.

Saving Scraped Data: Export extracted data into structured formats like CSV or JSON for further analysis.

The session also emphasizes ethical considerations and best practices for web scraping, including handling website terms of service and respecting rate limits. Through hands-on examples, attendees will scrape a sample website and transform raw HTML into actionable insights. By the end of the session, participants will have the confidence to build their own web scraping workflows and apply them to real-world projects.

Transforming Direct Debit Mandates with Python in Microsoft Fabric

This 50-minute session will guide participants through automating direct debit mandate processing using Python in Microsoft Fabric. Starting with raw, unformatted text files, attendees will learn to transform data into HSBC-compliant structured files by:

Parsing raw text into tabular data using PySpark and pandas.

Creating dynamic headers and footers that include record counts and aggregated financial sums.

Formatting fields with precise lengths and padding to meet specific requirements.

Key skills include chaining methods for efficient data processing, aggregating data for insights, and leveraging Python’s scalability for large datasets.

Attendees will also explore practical examples of manipulating text fields, validating data, and generating outputs ready for real-world delivery.

Code snippets will highlight list operations, text transformations, and scalable solutions using PySpark’s capabilities, empowering participants to automate complex workflows with confidence.

Time-Series Forecasting Made Easy with Prophet in Microsoft Fabric

In this 100-minute session, attendees will dive into the practical use of the Prophet library for time-series forecasting within Python, leveraging Microsoft Fabric. The session begins with an introduction to Prophet, explaining its capabilities for handling trends, seasonality, and holidays. Participants will:

Learn how to prepare and clean time-series data for analysis.

Explore how to build forecasting models using Prophet’s intuitive API.

Understand how to visualize forecast results, including confidence intervals and key trends.

Attendees will also discover techniques for customizing forecasts, such as adding holiday effects or adjusting parameters for seasonality. Real-world case studies will demonstrate how to apply Prophet to solve challenges like predicting sales, website traffic, or operational metrics. One example will include addressing a real-world issue where a couple of months of data were missing due to supplier changes. Prophet was used to forecast the missing data, enabling a complete trend analysis and ensuring accurate insights despite the gap.

Python Fundamentals: Mastering Data Analytics with Key Libraries

This 50-minute session introduces attendees to the fundamentals of Python for data analytics, focusing on using the ETL (Extract, Transform, Load) process to manage and analyze data efficiently. Participants will learn how to:

Ingest data: Import datasets in various formats (CSV, JSON, Excel) using pandas and numpy.

Clean data: Handle missing values, detect outliers, and apply transformations for data quality.

Shape data: Manipulate data with pandas to filter, sort, aggregate, and pivot tables for analysis.

Visualize data: Create compelling plots and graphs with matplotlib and seaborn, including 15 minutes dedicated to exploring their powerful visualization capabilities.

Save data: Export processed data into desired formats for further use or integration.

Through practical examples and a hands-on approach, attendees will see how each library supports the ETL process and gain a clear understanding of how to apply these tools to real-world data analytics challenges. By the end of the session, participants will have built a complete pipeline, from raw data ingestion to generating meaningful insights.

Mastering C# Scripting in Tabular Editor 3: From Beginner to Pro

This session takes you from knowing nothing about C# to mastering scripting in Tabular Editor 3. We'll start with the basics, showing you how to create scripts that can automate repetitive tasks, improve model consistency, and enhance your Power BI workflows.

You’ll learn how to build reusable scripts to save time and effort in managing complex models.

By the end of this session, you'll not only understand the fundamentals of C# but also have the practical skills to build reusable scripts that reduce errors, save time, and enable consistency and scalability in your data models.

Whether you’re a beginner or looking to expand your scripting toolkit, this session will help you unlock the full potential of Tabular Editor 3.

Getting Started with Python in Microsoft Fabric

In this introductory session, you'll gain practical experience with Python in Microsoft Fabric. We'll start with connecting to a Lakehouse, exploring how to access and navigate data within Fabric. Attendees will learn various methods to ingest data, including uploading files directly. You’ll also discover how to use Python commands to make modifications to your data, such as cleaning, transforming, and preparing it for analysis. Finally, we’ll cover efficient ways to save and export your data, ensuring that your work is ready for further use.

Whether you’re a complete beginner or looking for a structured introduction, this session provides the nuts and bolts of getting started with Python in Fabric—empowering you to confidently take the next steps in your data journey.

Deep Dive into Time-Series Forecasting with Prophet in Microsoft Fabric

This follow-up 50-minute session builds on the fundamentals of time-series forecasting with Prophet, providing a deep dive into advanced capabilities and practical applications. Attendees will learn how to fine-tune and customize Prophet models to handle complex forecasting challenges, such as:

Incorporating external factors like holidays or special events to refine predictions.

Adjusting seasonality and trend parameters for improved accuracy.

Visualizing forecast components (trend, seasonality, and residuals) to uncover actionable insights.

Using real-world examples, this session will demonstrate how to:

Handle missing data scenarios with interpolation techniques.

Save forecasted results into the Lakehouse for integration into dashboards or further analysis.

Participants will gain hands-on experience with the Prophet library’s features, including detailed explanations of its underlying statistical models. The session will also cover best practices for interpreting forecasts and presenting results to stakeholders. With a focus on real-world applications, attendees will leave equipped to apply these techniques confidently in their own projects.

Bring Your Data to Life: Interactive Visualization with Python, Plotly, and Dash

This 50-minute session delves into the power of Plotly and Dash for creating interactive and dynamic visualizations that go beyond static graphs. While Plotly focuses on generating individual interactive charts, Dash extends this functionality to build full-fledged data dashboards. Here’s why they stand out:

Plotly:
Interactivity: Features like zoom, pan, hover tooltips, and filtering enable deep exploration of data.
Chart Variety: Supports a wide range of visualizations, including 3D charts, heatmaps, and maps.
Ease of Use: Create stunning visualizations with minimal code.

Dash:
Dynamic Dashboards: Combine multiple Plotly visualizations into a single, interactive application that updates live based on user inputs.
Custom User Controls: Add dropdowns, sliders, and buttons for enhanced user interaction.
Live Data: Seamlessly integrate real-time data streams into dashboards.

Why choose Plotly and Dash? They provide a seamless bridge between backend data processing and front-end visualization, making them ideal for projects where interactivity and user-driven exploration are key. Unlike tools like Matplotlib, which are static, Plotly and Dash enable dynamic interactions, offering a richer user experience.

During this session, attendees will:

Learn how to create interactive plots using Plotly to visualize trends and uncover insights.
Build a real-world dashboard using Dash, integrating charts, sliders, and dropdowns for interactive exploration.
Explore how to connect live datasets and create real-time updates for decision-making.

By the end of this session, participants will understand how Plotly and Dash stand out from other visualization tools and how to leverage their unique capabilities for projects requiring interactivity, exploration, and real-time insights.

AI Meets Analytics: Unlocking Real-World Insights with OpenAI's GPT in Python

In this 50-minute session, attendees will learn how to harness the power of OpenAI’s GPT library in Python to transform raw data into actionable insights. Through practical examples, participants will:

Automate text analysis: Use GPT to classify, summarize, and extract key information from text data.

Generate actionable insights: Apply GPT to real-life scenarios, such as analyzing customer feedback or summarizing business reports.

Streamline workflows: Learn how GPT integrates seamlessly with existing Python workflows, making it an invaluable tool for automation.

A real-world use case will demonstrate how to analyze a dataset of customer reviews, extract sentiment trends, and auto-generate a summary of actionable recommendations for business improvement. Attendees will also explore best practices for working with AI models, including ethical considerations and avoiding pitfalls in text generation.

By the end of the session, participants will have hands-on experience using OpenAI’s GPT library and practical knowledge to integrate AI-powered text analysis into their analytics workflows.

Certified to Succeed: How Technical Certifications Propel Your Career

Explore how certifications can drive long-term success for Data professionals. This session discusses why you should consider certification and offers practical strategies to navigate the certification landscape. We will focus not only on popular certifications like PL-300 and DP-600 but will provide practical advice on preparing for exams, including tips and tricks for time management and the exam process.
Whether you're starting out or advancing your career, learn how to leverage certifications to achieve your professional goals.

Taking the Fear out of Tabular Editor

There is a lot of talk about how as a professional Power BI developer, you should use Tabular Editor. Yet when you open it, it’s daunting. Where do you start, what do these buttons do, how long is it going to take for me to learn this, is it really worth it?

In this session, we will run through the core components of Tabular Editor in normal non programming language. We look at how you can utilise this software to save you time in working with Power BI.

Session Objectives:

- Understand the Tabular Editor 3 interface and its various elements
- Learn how to use Tabular Editor 3 to create and manage Tabular models
- Learn how to use key features of Tabular Editor 3, including the formula bar, expression editor, and DAX functions
- Understand how Tabular Editor 3 can be integrated with Microsoft Analysis Services and Power BI.

Session Plan:

Introduction:

- Brief overview of the session
- Introduce Tabular Editor 3 and its key features and functionalities

Tabular Editor 3 Interface:

- Demonstrate the Tabular Editor 3 interface and its various elements, including the tree view, formula bar, property grid, and expression editor
- Explain how to use the interface to create and manage Tabular models

Creating and Managing Tabular Models:

- Demonstrate how to create a new Tabular model using Tabular Editor 3
- Explain how to add tables, columns, and relationships to the model
- Discuss best practices for organizing and managing models

Using Key Features of Tabular Editor 3:

- Demonstrate how to use the formula bar and expression editor to create and edit DAX expressions
- Explain how to use key DAX functions, such as CALCULATE, SUMX, and AVERAGE
- Discuss other key features, such as the script editor, perspectives, and translations

Integration with Analysis Services and Power BI:

- Explain how Tabular Editor 3 can be integrated with Microsoft Analysis Services and Power BI
- Discuss the benefits of using Tabular Editor 3 with Analysis Services and Power BI
- Demonstrate how to use Tabular Editor 3 to deploy and manage Tabular models in Analysis Services and Power BI

Conclusion:

- Recap key points covered in the session
- Provide resources for further learning and exploration, such as documentation and online communities.

(50 mins)

Key Data Structures for Optimal Model Performance in Power BI

Recognise the key basic concepts behind different types of data
Understand the importance of data types
Learn how to effectively model data within Power BI
Understand what is meant by normalised data
The importance of a Star Schema
How the engine works

Take off with Tabular Editor

A fast paced session showing several quick wins on how to use Tabular Editor to speed up delivery.
We will cover creating measures, batch editing and renaming, copying and pasting objects, formatting, folder management, Best Practise Analyzer and an example of C# scripting and automation.
This session will show you how to use Tabular Editor to work with your model and enable you to take off and fly smoothly away.

20 minute session. All features will be suitable for Tabular Editor 2 and Tabular Editor 3. This session is a condensed version of a series of presentations delivered at Data Relay, at various User Groups, at Data Weekender and validated in front of Daniel Otykier.

Soaring Through Data: An Intro to Python for Data Analysis

Take off on a data-driven journey with 'Soaring Through Data: An Intro to Python for Data Analysis.'

This 100-minute session is your pre-flight checklist for mastering Python's role in data analytics.
We'll start at the runway with the basics of Python syntax and programming constructs.
Ascend into the clouds as we explore essential data structures like lists and NumPy arrays.
Reach cruising altitude with the Pandas library, your cockpit for data manipulation. Navigate through data cleaning techniques and set your coordinates for basic yet powerful data analysis methods.
Finally, prepare for landing with a Q&A session, ensuring you're ready to pilot your own data projects.
Buckle up and prepare for takeoff into the world of Python for data analysis.

Target Audience - those attendees that are interested in understanding what Python is and how to use it.
The data set will be aviation themed.
This session hasn't been delivered previously, but in my previous life I've taught Python within the GCSE and A Level syllabus in the UK.

The session will be broken down into these areas:
Part 1: Introduction (10 mins)
• Brief on Python's importance in data analysis (3 mins)
• Agenda overview (2 mins)
• Required tools: Python, Jupyter Notebook (5 mins)
Part 2: Basics of Python (15 mins)
• Syntax, variables, and data types (5 mins)
• Control structures: loops, if-else (5 mins)
• Functions and libraries (5 mins)
Part 3: Data Structures (10 mins)
• Lists and dictionaries (5 mins)
• NumPy arrays (5 mins)
Part 4: Pandas Library (20 mins)
• DataFrames and Series (5 mins)
• Data import/export (5 mins)
• Basic operations: filter, sort, groupby (10 mins)
Part 5: Data Cleaning (15 mins)
• Handling missing data (5 mins)
• Data transformation (5 mins)
• Data visualization: Matplotlib/Seaborn basics (5 mins)
Part 6: Basic Analysis (15 mins)
• Descriptive statistics (5 mins)
• Correlation and basic inference (5 mins)
• Simple linear regression (5 mins)
Part 7: Q&A and Wrap-up (15 mins)
• Questions (10 mins)
• Summary and next steps (5 mins)