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Python Data Science with the TCLab

Data science introduction for scientists and engineers

4.58 / 5.0
1053 students4 hours 23 minutes

Created by John Hedengren, offered on Udemy

bestcourses score™

Student feedback

5/10

To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 1053 students which left 6 reviews at an average rating of 4.58, which is average.

Course length

9/10

We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 4 hours 23 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.

Overall score

5.9/10

This course currently has a bestcourses score of 5.9/10, which makes it an average course. Overall, there are probably better courses available for this topic on our platform.

Description

These modules are intended to help you develop data science and machine learning skills in Python. The 12 modules have video tutorials for each exercise with solutions for each exercise. One of the unique things about these modules is that you work on basic elements and then test your knowledge with real data exercises with a heat transfer design project. You will see your Python code have a real impact by designing the materials for a new product.

One of the best ways to start or review a programming language is to work on a project. These exercises are designed to teach data science Python programming skills. Data science applications are found across almost all industries where raw data is transformed into actionable information that drives scientific discovery, business innovations, and development. This project is to determine the thermal conductivity of several materials. Thermal conductivity is how well a material conducts or insulates against heat transfer. The specific heat transfer project shows how to apply data science to solve an important problems with methods that are applicable to many different applications.

Objective: Collect and analyze data from the TCLab to determine the thermal conductivity of three materials (metal, plastic, and cardboard) that are placed between two temperature sensors. Create a digital twin that predicts heat transfer and temperature.

To make the problem more applicable to a real situation, suppose that you are designing a next-generation cell phone. The battery and processor on the cell phone generate a lot of heat. You want to make sure that the material between them will prevent over-heating of the battery by the processor. This study will help you answer questions about material properties for predicting the temperature of the battery and processor.

Topics

There are 12 lessons to help you with the objective of learning data science in Python. The first thing that you will need is to install Python to open and run the IPython notebook files in Jupyter. There are additional instructions on how to install Python and manage modules. Any Python distribution or Integrated Development Environment (IDE) can be used (IDLE, Spyder, PyCharm, and others) but Jupyter notebook or VSCode is required to open and run the IPython notebook (.ipynb) files. All of the IPython notebook (.ipynb) files can be downloaded. Don't forget to unzip the folder (extract the archive) and copy it to a convenient location before starting.

  1. Overview

  2. Data Import and Export

  3. Data Analysis

  4. Visualize Data

  5. Prepare (Cleanse, Scale, Divide) Data

  6. Regression

  7. Features

  8. Classification

  9. Interpolation

  10. Solve Equations

  11. Differential Equations

  12. Time Series

They give the skills needed to work on the final project. In the final project, metal coins, plastic, and cardboard are inserted in between the two heaters so that there is a conduction path for heat between the two sensors. The temperature difference and temperature levels are affected by the ability of the material to conduct heat from heater 1 and temperature sensor T1 to the other temperature sensor T2.

You may not always know how to solve the problems initially or how to construct the algorithms. You may not know the function that you need or the name of the property associated with an object. This is by design. You are to search out the information that you might need using help resources, online resources, textbooks, etc.

You will be assessed not only on the ability of the program to give the correct output, but also on good programming practices such as ease of use, code readability and simplicity, modular programming, and adequate, useful comments. Just remember that comments, indentation, and modular programming can really help you and others when reviewing your code.

Temperature Control Lab

The projects are a review of all course material with real data from temperature sensors in the Temperature Control Lab (TCLab). The temperatures are adjusted with heaters that are adjusted with the TCLab. If you do not have a TCLab module, use the digital twin simulator by replacing TCLab() with TCLabModel().

What you will learn

  • Visualize data to understand relationships and assess data quality
  • Understand the differences between classification, regression, and clustering and when each can be applied
  • Detect overfitting and implement strategies to improve prediction
  • Understand engineering and business objectives to plan applications
  • Implement data science techniques successfully to complete a project

Requirements

  • Beginner Python experience is needed.
  • Consider the freely available course found on GitHub: APMonitor/begin_python to gain foundational experience with variables, loops, functions, lists, and other Python introductory topics.
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Frequently asked questions

  • Price: $19.99
  • Platform: Udemy
  • Language: English
  • 4 hours 23 minutes
Python Data Science with the TCLab thumbnail

bestcourses score: 5.9/10

There might be better courses available for this topic.