This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Tech Community.
This week, is the annual National Data Science Conference hosted by UC Berkeley which we are proud to be supporting.
As part of the event Microsoft will be announcing the release of Berkeley Data 8 Foundations of Data Science on Microsoft Learn, which has been complimented by a interactive learning content and auto graded mini labs using Otter grader. These mini labs allow students to get hands on with code and test their skills. Students will have have the opportunity to complete a number of hands on interactive learning exercises and interactive notebooks using Python and Jupyter with Visual Studio Codespaces.
So we welcome all the attendees to the UC Berkeley’s National Data Science Conference to learn more about Data Science.
The UC Bekerely Computing, Data Science, and Society (DSEP) division is proud to announce the new Learning Path Foundations of Data Science on Microsoft Learn based on the Computational and Inferential Thinking textbook authored by Berkeley faculty members Ani Adhikari, John DeNero, and David Wagner. The Foundations of Data Science course serves as an introductory data science course that teaches data science from the ground up, without any prerequisite knowledge in programming or statistics.
This new Microsoft Learn offering will allow learners worldwide to engage the content of one of UC Berkeley’s most popular undergraduate courses. Below is a brief overview of the course from Berkeley’s course website:
Professor John Denero
'The UC Berkeley Foundations of Data Science course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design. You’ll learn to program when studying data science — but not for the purpose of building apps or games. Instead, we use programming to understand the world around us.'
The Foundations of Data Science Learning Path is split into twelve modules, each of which covers a different fundamental topic in data science.
- Learn the data science method
- Introduction to Python using the datascience library
- Create and manipulate tables using the datascience library
- Design and plot graphs in Python
- Introduction to probability
- Simulate and generate empirical distributions in Python
- Test hypothesis by simulating statistics
- Compare two samples by bootstrapping
- Understand the normal curve
- Make predictions with linear regression
- Simulate the distribution of regression coefficients
- Predict classes with a K-NN classifier
The modules are accompanied by knowledge checks and Jupyter notebook assignments hosted on Visual Studio Codespaces, allowing students to apply their knowledge in an interactive setting. In the Jupyter assignments, students will have a chance to use Python and relevant data science libraries to manipulate data, perform exploratory analyses, visualize data, conduct statistical hypothesis tests, and build machine learning models.
Berkeley National Data Science Conference Resources
Please take a look at our one page handout(link is external) summary guide to open source resources. This short document will explain the curriculum and set of technologies developed at Berkeley.
The Berkeley team are also building an external facing resources portal website with key links for instructors.