Data Science Programming
Class Materials
101 - Beginner
Learn the basics of Data Science through computer programming. With no previous experience required, students learn how to instruct the computer to perform the tasks and calculations needed to analyze and summarize data. Learn concepts like variables, input/output, and loops/flow control. Read data in from files, perform calculations, and display simple charts.
| Session | Presentation | Files | |
|---|---|---|---|
| 1. | Python Introduction and Environment Setup | 1 - intro.pptx | |
| 2. | Python Language Basics | 2 - python language basics.pptx | |
| 3. | Python Language Basics Continued | 3 - python language basics continued.pptx | |
| 4. | Complex Types | 4 - complex types.pptx | |
| 5. | Flow Control | 5 - flow control.pptx | |
| 6. | Modules | 6 - modules.pptx | |
| 7. | Pandas - read, display, write tabular data | 7 - pandas.pptx | |
| 8. | Pandas - manipulate and analyze tabular data | 8 - pandas.pptx | |
| 9. | Plotly - draw charts/graphs of tabular data | 9 - plotly.pptx |
201 - Intermediate
Expand upon the beginner course to learn higher level programming concepts like functions, classes, and multi-file programs. Perform advanced processing and analysis over DataFrames, and design more complex visualizations. Layout multiple visualizations into dashboards and turn interactive notebooks into standalone programs that can run continuously or on schedule.
| Session | Presentation | Files | |
|---|---|---|---|
| 1. | Review of beginner concepts, Intro to intermediate topics, Environment setup | 1 - intro.pptx | |
| 2. | Writing Functions and Classes | 2 - functions and classes.pptx | |
| 3. | Multiple Files: Modules and Packages | 3 - modules and packages.pptx | |
| 4. | Pandas – advanced processing | 4 - advanced pandas.pptx | |
| 5. | Plotly – advanced plots and subplots | 5 - advanced plotly.pptx | |
| 6. | Dash - Intro and simple layouts | 6 - dash intro.pptx | |
| 7. | Dash - Callbacks and Interactivity | 7 - dash callbacks.pptx | |
| 8. | Converting notebooks to standalone programs | 8 - convert standalone.pptx | -- |
| 9. | Running on schedule or as a continuous service | 9 - schedule service.pptx |
301 - Advanced
Explore Machine Learning through this hands-on workshop. Learn about the many categories of Machine Learning and experience the complete end-to-end application of one of those categories - Supervised Regression. Assemble a small machine with electric motor, belt drive, sensors, and control unit. Adjust the belt tension to simulate performance tuning. Program the control unit to read the sensors and report the data. Write another program to receive and record the data. Finally, use this data to train and test machine learning algorithms to predict the output speed based on the belt tension.
| Session | Presentation | Files | |
|---|---|---|---|
| 1. | Welcome and Intro to Machine Learning - Overview of all categories, and deep dive into Supervised Regression |
1 - intro.pptx | -- |
| 2. | Build machine. In-depth description of machine and its components. | 2 - build machine.pptx | -- |
| 3. | Introduce Arduino and CircuitPython, ensure needed software is installed. | 3 - arduino and circuitpython.pptx | |
| 4. | Program the control unit | 4 - program control unit.pptx | |
| 5. | Program the data recorder | 5 - program data recorder.pptx | |
| 6. | Gather and examine test data. Make improvements to programs. | 6 - test and improve.pptx | |
| 7. | Gather “final” data. Clean up and prepare data for ML. Set up and train the candidate ML algorithms. Select one to use. |
7 - training and selection.pptx | |
| 8. | Run the selected algorithm over remaining data and compare results | 8 - final ml run.pptx |