The example content included here was used by Data Science students at Lambda School. The full-time program was six months, including four months of data science content. Each week, students would prepare for the daily lecture (a guided project with an instructor) by reading through the intorductor tutorials and sample code exercises.

After the guided project, they would work through assignments (module projects), some of which were autograded (Python notebook using nbgrader to check the student answers). Each day, students also took a short quick to check their understanding of the material for that day.

At the end of the week, students individually complete a project (sprint challenge) that was also autograded and received a score. Aftering finishing the challenge, they take one additional assessment that covers all topics for the week.

Data Science Program Information

Unit 1: Exploratory data analysis, data cleaning and preparation, statistics, linear regression Unit 2: Introduction to modeling, regression, decision trees, Kaggle competition Unit 3: Data engineering, databases, software engineering, object oriented programming, SQL Unit 4: Natural language processing, machine learning, deep learning, artificial intelligence

Note about ipynb file rendering: Sometimes there will be an error when GitHub tries to render a Jupyter notebook. Usually reloading the file solves the problem but it can take a few tries.

In the tutorials folder, there is example written content for a single objective. Each day, a student would read and work through 2-5 objective tutorials before the guided project lecture. These tutorials are designed to be introductory and to provide example code, resources, and links to documentation that will help prepare them both the guided project and to complete the daily afternoon assignment.

In the assessments folder, two example weekly assessments are included which cover material for Units 1 and 2. The assessment was designed to provide information both for the instructor as to how well the students did over the week and for the student to self-assess where they might need additional study.

In the autograded_assignments folder, there are examples of the daily assignments that student were required to complete. The solution version is shown here but student would not receive the answers. They instead needed to fill in the code and then submit the notebook for automatic grading. They would receive simple automated feeback and a score on their notebook.