LSE DS105M

Data for Data Science

⚠️ Important Update 29/11/2022

• The syllabus was updated to reflect the impact of the UCU Industrial Strike Action.

Older Updates

(25/10/2022)

We re-ordered the topics from Weeks 05 onwards. See a list of what has changed:

• Topic of Week 05 lecture is now “Notebooks + data frames”
• The lecture “(Re)shaping data.” was moved from Week 05 to Week 08 (before transforming data, we need to see what data looks like!)
• Week 05 lab is now “APIs & Data Frames” to allow for a smoother transition into programming
• The lab “Github & Markdown” was moved from Week 05 to Week 07
• The lecture “Data viz with the grammar of graphics” was moved from Week 10 to Week 07 (to help you include plots in your projects sooner)
• The lecture “Unstructured data (text, audio & image)” was moved to Week 10
• Weeks 09 & 11 were left untouched

📓 Syllabus

A list of what will happen every week.

Click on each Week’s link for more information (slides, lab instructions, recommended resources, etc.).

Note about formative assessment: besides the in-person lab exercises, we might give take-home assignments on certain weeks. Even though we do not grade these problem sets, you will get written feedback on these formative assignments.

This course will help you become familiarised with the most fundamental practical tools needed to gather (Weeks 02-04) and pre-process data (Weeks 05-09) and will give you some inspiration for some fundamental analysis (Weeks 10-11) to perform in your selected datasets.

These skills are cumulative, so practice what you learned each week and make the most of lectures, labs and our Slack group. We believe these points of contact and integration will create a fertile environment of ideas for your project.

💡 Remember: collaboration is key to the success of a data science project!

Intro
	🗓️ Week 01	Lecture	Introduction and the Data Science Toolbox 🧰
		Lab	No class this week. (Use this time to revisit basic R or python programming)
Theme: Behind the scenes
	🗓️ Week 02	Lecture	Operating Systems, Files & The Terminal
		Lab	Navigating the command line in your own computer
	🗓️ Week 03	Lecture	The Cloud: accessing and getting data in and out.
		Lab	Connecting to the cloud via the command line
		Formative	Worth: knowledge! Problem set involving the terminal. Release date: 7 October 2022 Deadline: 13 October 2022
			🗓️ Week 04	Lecture	The Internet: protocols + scraping + APIs.
Lab	Web scraping exercise
Summative	Worth: 10% of final marks Problem set involving the upload of data to the cloud. Release date: 18 October 2022 Deadline: 24 October 2022
Group project	after class, students pitch their ideas of preferred APIs/datasets there will be a designated channel on our Slack for this
Theme: Working with data
	🗓️ Week 05	Lecture	Computational notebooks + data frames
		Lab	APIs & Data Frames
		Summative	Worth: 15% of final marks Problem set involving web scraping. Release date: 26 October 2022 Deadline: 9 November 2022
		Group project	students form groups of 3 students must submit a team contract to Moodle (not graded) Release date: 25 October 2022 Deadline: 9 November 2022
	🗓️ Week 06	Reading Week
	🗓️ Week 07	Lecture	Data viz with the grammar of graphics
		Lab	Github & Markdown
	🗓️ Week 08	Lecture	(Re-)shaping data, data normalisation & databases
		Lab	We will have group presentations instead of a structured class this week.
		Summative	Worth: 15% of final marks each group will present about their selected data. see instructions for marking criteria
	🗓️ Week 09	Lecture	Managing your data science workflow.
		Lab	CANCELLED (We didn’t have lab sessions on W09 due to UCU Industrial Strike Action)
Theme: Applications
	🗓️ Week 10	Lecture	Unstructured data (text, audio & image)
		Lab	🦸 Super tech-support (get help with your project) Setting up Github for your group project & GitFlow
			🗓️ Week 11	Lecture	Sentiment analysis, topic modelling and social networks
Lab	We will have group presentations instead of a structured class this week
Summative	Worth: 20% of final marks each group will present about their selected data see instructions for marking criteria