Upskilling a group of students, researchers, faculty or staff in data science can be daunting and time consuming. Finding quality courses with relevant, applicable content that suit groups with varying skill sets and schedules is no easy feat.
Fundamentals of Data Science for Social Scientists is a full suite of online courses designed to solve your institution’s pedagogical needs and get your team or group mobilized and practicing the latest computational methods. The learning path covers everything from the big picture of data science, getting started with programming skills in R and Python, through to more advanced ‘bytesize’ data management and analysis topics. There’s something to offer everyone.
SAGE Campus created the courses in partnership with the Social Science Data Lab (D-Lab) at the University of Berkeley, so you can get courses by leading expert instructors, with the trusted SAGE quality, and an unparalleled online learning experience.
Enrollment is easy
Provide us with a list of learners and we’ll sort the rest! We provide learners with their own log-in details.
Courses available 24/7
Learners with busy and different schedules or varying abilities can learn at their
Clear learning pathway
Learners just starting out can take their time and those who are more advanced can skip to intermediate courses.
Choice of programming language and topics
The choice of programming language and bytesize topics allows learners to focus on what applies to their research.
Time to complete: This section consists of one introductory course on the theory of data science and it’s applicability to social science and takes 5-9 hours to complete.
Prerequisites: Suitable for all. A basic foundation in statistics would be helpful but is not essential.
Time to complete: This section consists of two courses, where learners can choose to learn a programming language of their choice; Python (which takes 33 hours), or R (which takes 35 hours).
Prerequisites: No prior programming experience is required but completing the introductory course is helpful.
Time to complete: This section consists of 7 standalone “bytesize” courses so learners can choose topics most relevant to them. The courses take a total of 30 hours combined.
Prerequisites: Require knowledge of R or Python. Learners are taught the required knowledge in the beginner section.
This introductory course gives an understanding of data science methods and tools, all from a social science perspective. By the end of the course learners will:
This course will teach learners to program in the R programming language, master the fundamentals of R and learn practical skills that are directly applicable to social science research.
It covers Jupyter Notebooks, variables, data types, data structures, plotting, statistical testing and more.
This course will teach learners to program in the Python programming language, master the fundamentals of Python and learn practical skills that are directly applicable to social science research.
It covers Jupyter notebooks, variable assignment, functions and variables, programming style and more.
Teaches learners how to extract data from web resources appropriate to their research questions. Special attention will be given to how to obtain permission from hosts, and proper etiquette when using APIs and scraping. By the end of this bytesize course, learners will be able to:
Teaches learners how to prepare data so that it is in a format that can be recognized by the coding function in R or Python. By the end of this bytesize course, learners will be able to:
Teaches learners what formats data comes in, and how they should structure their own data if they collect it themselves. By the end of this bytesize course, learners will be able to:
Teachers learners how to model explicit relationships, how to examine the statistical properties of relationships in co-mention networks, and how contextualize the statistical properties of a network. By the end of this bytesize course, learners will be able to:
Teachers learners effective presentation methods for various data types and variables, and how to create their own visualizations in Jupyter notebooks in Python or R. By the end of this bytesize course, learners will be able to:
Teaches learners the basics of machine learning and core organizational concepts of classification and regression, data preprocessing and fitting a model to a training dataset. By the end of this bytesize course, learners will be able to:
Teavhers learners the building blocks that serve as the foundation for computational text analysis. By the end of this bytesize course, learners will be able to:
Your institution can get a 6 month subscription to Fundamentals of Data Science for Social Scientists. Learners will have full access to all of the courses for the duration of the subscription.
Your institution’s subscription will be tailored depending on the number of learners you have. We can accommodate anything from 10-100 learners.
We will invoice you for your subscription.
We can arrange to send regular reminders to your learners about the courses and are able to tailor these emails to suit your institution’s needs. When you set up your subscription with us, we can discuss your communication needs and our recommendations for keeping learners engaged.
We can provide overall reports of how your group is doing at interim periods throughout your subscription. Alternatively, we can provide access to the learner platform where you can see learner reports and can pull the data you need.
Yes! If you would like to see the courses before purchasing your institution’s subscription you can trial free demos of the courses. Simply ask for this when you make your enquiry.
Only if you want to! All that’s needed to complete the courses is a web browser and an internet connection. We do all our programming using JupyterHub, which means that you can code in your browser window.
Jupyter notebooks offer a seamless integration of code with explanatory markdown text. It will allow you to read the narrative of the programming task, and write code of your own to fit into the larger narrative. If you’d like to find out more about Jupyter notebooks, see our blog post 'What is a Jupyter notebook'.
However, if you wish to install R or Python, please do!
SAGE is passionate about social science and is dedicated to finding new ways to support social science researchers. We understand that the rise of big data and new technology is set to revolutionize social science. Researchers of all kinds need an array of additional skills and experience to take advantage of this opportunity, while maintaining integrity of the research process.
SAGE Campus courses are designed to support the development of these new skills. We’ve delivered courses to nearly 600 academics and practitioners in the field of social science, so we’re well equipped to help your colleagues or trainees achieve important milestones within your institution.
This course was developed by the Social Science Data Lab (D-Lab) at the University of California, Berkeley. The Jupyter notebooks and videos were developed and produced by the D-Lab. The JupyterHub was configured with support from the Jupyter Project, SAGE, and the D-Lab.
The D-Lab gives researchers access to the cutting edge of the data revolution. Operating as a hub for broad-ranging and multidisciplinary data-intensive social sciences, it advances research excellence by helping the community integrate latest software, technology, and methods into their research practices.