Python Data Science with the TCLab

Python Data Science with the TCLab

Scientists and engineers will learn about data science in this class.

What you’ll learn

Python Data Science with the TCLab

Visualize data to see how relationships work and to check the quality of the data.
Understand how classification, regression, and clustering work and when each can be used.
Detect overfitting and find ways to make predictions better.
Engineering and business goals must be taken into account to plan applications.
To finish a project, use data science techniques correctly.

Requirements

Beginner Python skills are required.
Consider taking the free course on GitHub: APMonitor/begin python to learn about variables, loops, functions, lists, and other Python basics.

Description

These modules are meant to help you learn more about data science and machine learning in Python. These videos show you how to do all 12 exercises, and they show you how to get the correct answers. One of the cool things about these modules is that you start with simple things and then do real-world exercises with real data to see how well you know them. You work on a heat transfer design project. See how your Python code works when you design the materials for a new thing.

Start or review a programming language the best way is to start or work on a project. This project is to figure out how well different materials move heat. Thermal conductivity is how well a material moves or blocks heat.

The goal is to get and look at data from the TCLab to figure out the thermal conductivity of three materials (metal, plastic, and cardboard) that are between two temperature sensors. Create a digital twin that can predict how the heat will move and how hot it will be.

Suppose that you are designing a cell phone for the next generation. This will make the problem more real-world. The battery and the processor on the cell phone make a lot of heat, which makes the phone hot. It is important to make sure that the material between them will keep the battery from getting too hot because of the processor. This study will help you answer questions about material properties so you can figure out how hot the battery and processor will get.

Topics

There are 12 lessons that will help you learn data science in Python, so you can reach your goal. People who use IPython notebook files in Jupyter will need to install Python first. This will allow them to open and run the notebook files. There are more instructions on how to set up Python and manage modules. It doesn’t matter what kind of Python distribution you have or what kind of Integrated Development Environment (IDE) you use. Don’t forget to unzip the folder (extract the archive) and move it to a good place before you start.

Giving skills to work on the project is what they do. Finally, in the finished project, there is a path for heat to move through between the two heaters. Because the material is able to move heat from heater 1 and the temperature sensor T1 to the other one and back again, there is a difference in temperature.

You may not always know how to solve the problems of how to make the algorithms when you first start. You might not know the name of the function you need or the name of the property that comes with a certain thing. I made this for a reason. You should use help resources, online resources, textbooks, and more to find the information that you might need, like how to write a paper.

Keep in mind that comments, indentation, and modular programming can make it easier for you and other people to review your code.

Controlling the temperature in a lab

The projects are a review of everything we learned in class with real data from temperature sensors in the Temperature Control Lab (TCLab). There are heaters that can be changed with TCLab. In the digital twin simulator, you can use TCLab() instead of TCLabModel() ().

Who this course is for:

Beginner Python programmers who want to learn more about Data Science.
There are people who want to be scientists or engineers.
Students and professionals who want to use Data Science in the real world.

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