ℹ️ Course Information

All you need to know about DS105W (2025/26)

Are you ready to transform messy data into meaningful insights? Welcome to LSE DS105W - Data for Data Science, a hands-on course designed to teach you practical data analysis skills through real-world projects.

What is DS105W about?

DS105W teaches you to transform, manipulate and analyse real data through a fully hands-on, practical approach from the very beginning. You’ll be introduced to Python programming in a way that welcomes coding beginners, with weekly coding tasks that build foundational skills progressively.

You’ll start by experiencing what data analysis can accomplish before learning how to build it yourself. Working with familiar spreadsheet-like data first, you’ll see the analytical possibilities, then learn the technical skills to achieve these goals. This course follows a data journalism approach with technical depth, emphasising reproducibility, transparency, and clear communication.

This course does NOT teach machine learning or algorithms. Instead, you’ll develop practical skills for collecting, cleaning, and communicating insights from real-world data.

🥅 Intended Learning Outcomes

By the end of this course, you should be able to:

• Master the fundamentals of data types, structures, and common data formats [W01-W03]
• Apply Python and pandas to clean, reshape and transform raw data [W02-W04]
• Design and implement practical data analysis workflows [W04-W05]
• Use Git and GitHub for version control and collaborative workflows [W03, W11]
• Identify and resolve common data quality issues [W04]
• Integrate data from multiple sources [W08]
• Understand database normalisation concepts and basic SQL queries [W07, W09-W10]
• Create visualisations using seaborn to apply grammar-of-graphics principles [W05]
• Critically evaluate visualisations and distinguish between correlation and causation [W05, W09]
• Understand markup languages fundamentals, including HTML and Markdown [W03, W11]
• Create and maintain simple websites using HTML and CSS [Group Project]
• Craft clear, accurate and responsible data reports [Throughout - applied in Mini-Projects since W01]

📓 Syllabus

Check the syllabus every week for the most up-to-date information on the course structure, schedule, and assessment details.

📟 Communication

At DS105, we believe learning happens everywhere. Frequent communication between students, peers, and teaching staff creates the best learning experience. We’ve set up multiple channels to support you throughout this course.

Slack

Slack serves as our primary communication hub. We use it for sharing resources, announcements, polls, and answering questions. For formal requests (like extensions), please use email.

Most informal communication happens through Slack. Use it to ask questions, share discoveries, and engage with your peers and instructors.

Things we love to see on Slack:

• Questions posted on the #help channel
• Students helping each other by sharing solutions and explanations
• Discussions about real-world data science applications

🗒️ IMPORTANT: You can contact instructors directly via Slack DMs, but we prioritise public channels like #help and #social. We typically don’t respond in the evenings or weekends. Each week, teaching staff dedicate specific hours to Slack questions.

🗨️ Office Hours

Book one-on-one sessions with teaching staff to discuss course questions, practice exercises, or data science careers. Add a note when booking so we can prepare and make the most of our time together.

📅 Book your slot

Search for the name of one of the instructors. Add a note about what you’d like to discuss.

📧 E-mails

Use email for administrative queries like class changes or extension requests. Contact our Teaching and Assessment Administrator for these matters.

📧

For administrative matters and formal requests.

👥 Our Team

Dr Jon Cardoso-Silva

Course Leader

Tabby

Class Teacher (TA)

Sara Luxmoore

Class Teacher (TA)

David

Class Teacher (TA)

Kevin Kittoe

Admin

✋ Contact Hours

We crafted a schedule such that someone is available to help you every day during the week. See the full schedule on the Contact Hours page.

Note that this schedule is subject to change, so please always be aware of announcements via e-mail/Slack. I also always try to keep the 📔 Syllabus page current.

✍️ Assessment & Feedback

How will I be assessed in this course?

Your grade consists of two main components:

1. INDIVIDUAL WORK (60%): Two coding problem sets focusing on data analysis workflows and meaningful contribution to the group project.
2. GROUP PROJECT (40%): Collaborative data analysis project with presentation and repository submission

Assessment Timeline

Weight	Type	Assessment	Release of Instructions	Due Date
20%	Individual	Mini-Project 1	Week 04 after the lecture	Week 06 Thursday 8pm
30%	Individual	Mini-Project 2	Week 07	Week 10 Wednesday 8pm
10%	Individual	Evidence of Contribution to final project	Week 10	Summer Term Week 03
10%	Group	Pitch Presentation	Week 10	Summer Term Week 03
30%	Group	Final Project	Week 10	Summer Term Week 03

📝 Practice Exercises

Throughout the course, you’ll complete weekly practice exercises that prepare you for assessments. While ungraded, these exercises are essential for building the skills you need to succeed.

The Week 04 formative exercise receives individual feedback to help you prepare for Mini-Project 1.

💡 Success Tip: Regular practice with these exercises is the best way to master the material. Data science skills develop through consistent application, not just reading about concepts.

🤖 Generative AI Policy

Position 3: Full authorised use of generative AI in assessment

You can use generative AI tools (ChatGPT, Claude, GitHub Copilot, etc.) during lectures, labs, and assessments.

This course participates in the LSE GENIAL project. Hence you will be asked to turn in your chat history. This is voluntary but highly encouraged and will help the lecturer to better understand how you use these tools. On the flip side, you might also get some useful tips how to engage with AI tools in more effective ways.

Good Practices

DO:

• Use AI to personalise your learning with relevant analogies
• Use AI for code generation when you understand what you want to achieve
• Give AI lots of context about course materials and learning objectives

AVOID:

• Using AI for exercises designed to check your understanding
• Generating first drafts when you’re unsure where to start
• Accepting AI answers without verification, especially for complex technical concepts

Remember: AI tools can make you feel like you understand something when you don’t. The students who benefit most from AI stay in control of their learning, provide detailed context, and always verify AI suggestions against course materials.