ℹ️ Course Information
All you need to know about DS105A (2025/26)
Are you ready to transform messy data into meaningful insights? Welcome to LSE DS105A - Data for Data Science, a hands-on course designed to teach you practical data analysis skills through real-world projects.
What is DS105A about?
DS105A 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 - Winter Term]
- 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
#helpchannel - 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.
🆘 Weekly support sessions
Starting Week 02, we offer drop-in sessions where you can get help with exercises, ask questions about lectures, or chat with peers and instructors. No booking required!
Weekly Support Sessions
- When: Wednesdays, 2:00-4:00pm
- Where: COL.1.06 (DSI Visualisation Studio)
- Who: Pedro
Always check the 📓 Syllabus for the most up-to-date information. Some sessions might be rescheduled.
We’ll offer additional sessions during key weeks, especially before assessment deadlines. These will be announced on Slack and in the syllabus.
🗨️ 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.
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.
👥 People and 📍 Places
Our Team
| Name: | Dr Jon Cardoso-Silva |
| Links: |
 LSE,  GitHub,  LinkedIn, 📧
|
| Role at LSE: |
Assistant Professor (Education) LSE Data Science Institute At LSE since 2021 |
| Office Hours: | 🗓️ Book via StudentHub |
| Background: |
|
| Research interests: | How Generative AI influences learning 1. |
COURSE LEADER | LECTURER
| Name: | Riya Chhikara |
| Links: | 📧 |
| Role at LSE: |
Class Teacher LSE Data Science Institute |
| Office Hours: | 🗓️ Book via StudentHub |
CLASS TEACHER
| Name: | Tabtim Duenger |
| Links: | 📧 |
| Role at LSE: |
Class Teacher LSE Data Science Institute |
| Office Hours: | 🗓️ Book via StudentHub |
CLASS TEACHER
| Name: | Pedro Henrique |
| Links: | 📧 |
| Role at LSE: |
Class Teacher LSE Data Science Institute |
| Office Hours: | 🗓️ Book via StudentHub |
CLASS TEACHER
✋ Contact Hours
We crafted a schedule such that someone is available to help you every day during the week. Refer to the table below for the exact times and staff who are available to help you and how to get in touch.
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.
| Day | Activity | Time | Staff | Type |
|---|---|---|---|---|
| Monday |  Slack Monitoring |
Morning | Dr Jon Cardoso-Silva | 💬 Online |
| Slack Support |
18:00 - 20:00 | Riya | 💬 Online | |
| Tuesday | Office Hours | 09:00 - 10:00 | Tabby | 🌐 Online (Booking Required) |
| Slack Monitoring |
Afternoon | Dr Jon Cardoso-Silva | 💬 Online | |
| Slack Support |
17:00 - 19:00 | Tabby | 💬 Online | |
| Wednesday | Office Hours | 09:00 - 10:00 | Tabby | 🌐 Online (Booking Required) |
| Slack Support |
13:00 - 14:00 | Pedro | 💬 Online | |
| Drop-in Session | 14:00 - 16:00 | Pedro | 🛟 In-person (COL.1.06) | |
| Office Hours | 14:00 - 17:00 | Dr Jon Cardoso-Silva | 👥 In-person (COL.1.03) | |
| Slack Support |
16:00 - 17:00 | Pedro | 💬 Online | |
| Thursday | Slack Support |
09:00 - 10:00 | Tabby | 💬 Online |
| Slack Monitoring |
Morning | Dr Jon Cardoso-Silva | 💬 Online | |
| Lecture | 16:00 - 18:00 | Dr Jon Cardoso-Silva | 🖥️ In-person (CLM.5.02) | |
| Friday | Office Hours | 09:00 - 10:00 | Riya | 👥 In-person (COL.1.06) |
| Slack Monitoring |
Afternoon | Dr Jon Cardoso-Silva | 💬 Online | |
| Labs | Throughout the day | All Class Teachers | 💻 In-person (FAW.4.01, FAW.4.03) |
Key to Icons:
- 👥 In-person: Face-to-face interaction, typically in a designated office or meeting space.
- 💬 Online: Support provided via Slack. Staff will attend to unanswered questions or follow-ups on the public channels (
#helpor#social) - 🛟 Drop-in: Flexible support sessions to get help with anything you need. No prior booking required but it helps if you accept the calendar invites.
- 🖥️ In-person Lecture: / 💻 In-person Labs: Formal teaching sessions.
This schedule ensures that there is a point of contact available to students every day, offering support in various forms to suit your needs.
🕰️ Class Details
📅 Lecture
- ⏰ Thursdays: 16:00 - 18:00
- 📍 CLM.5.02
- 👤 Dr Jon Cardoso-Silva
📅 Class Group 1
- ⏰ Friday: 09:00 - 10:30
- 📍 FAW.4.01
- 👤 Tabby
📅 Class Group 2
- ⏰ Friday: 10:30 - 12:00
- 📍 FAW.4.01
- 👤 Tabby
📅 Class Group 3
- ⏰ Friday: 12:00 - 13:30
- 📍 FAW.4.01
- 👤 Riya
📅 Class Group 4
- ⏰ Friday: 15:00 - 16:30
- 📍 FAW.4.01
- 👤 Riya
📅 Class Group 5
- ⏰ Friday: 16:30 - 18:00
- 📍 FAW.4.01
- 👤 Pedro
📅 Class Group 6
- ⏰ Friday: 09:00 - 10:30
- 📍 FAW.4.03
- 👤 Pedro
📅 Class Group 7
- ⏰ Friday: 10:30 - 12:00
- 📍 FAW.4.03
- 👤 Pedro
📅 Class Group 8
- ⏰ Friday: 13:00 - 14:30
- 📍 FAW.4.03
- 👤 Tabby
✍️ Assessment & Feedback
How will I be assessed in this course?
Your grade consists of two main components:
- INDIVIDUAL WORK (60%): Two coding problem sets focusing on data analysis workflows and meaningful contribution to the group project.
- 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 Weather Data Analysis |
Week 04 after the lecture |
Week 06 Thursday 8pm |
| 30% | Individual | Mini-Project 2 London Travel Analysis |
Week 07 | Week 09 Wednesday 8pm |
| 10% | Individual | Evidence of Contribution to final project | Week 10 | Winter Term Week 03 |
| 10% | Group | Pitch Presentation |
Week 10 | Winter Term Week 03 |
| 30% | Group | Final Project | Week 10 | Winter 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.
Footnotes
Read about the
GENIAL project↩︎