Boost Data Science Productivity with PyCharm [2019, ENG] torrent

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Boost Data Science Productivity with PyCharm

Год выпуска: 2019
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/library/courses/pycharm-boost-data-science-productivity
Автор: Dan Tofan
Продолжительность: 2h 33m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание: PyCharm is an incredible Python integrated development environment. This course shows tips, tricks, and techniques to boost your Python productivity with PyCharm, with step-by-step demos targeted at Data Science projects.

Being productive with the tools at your disposal is key to the success of any data scientist. Pycharm brings many coding, debugging, and scientific tools to the table.

In this course, Boost Data Science Productivity with PyCharm, you will gain the ability to use PyCharm’s most relevant features for Data Science projects. Features such as highlighting typos and visual debugging reduce development friction and empower you to focus on finishing your Data Science projects faster.

First, you will learn to understand code faster, by finding usages, creating classes diagrams, viewing hierarchies, and accessing documentation.

Next, you will discover how to write better code faster by using PyCharm features, such as code completion, refactoring, and inspections, as well as how to debug code by using breakpoints, stepping, and remote debugging.

Finally, you will learn how to explore data by using the scientific mode in PyCharm, Jupyter notebooks, running R script, and SQL queries.

When you’re finished with this course, you will have a great set of tips, tricks, and techniques to boost your Python productivity in your Data Science projects.

Prerequisites: Data Science Literacy | Path
Python for Data Analysts | Path
Core Python | Path

Related Topics: TensorFlow
PyTorch
scikit-learn
Pandas
Matplotlib
NumPy
Deep Learning Literacy — Practical Application | Path
Deep Learning Literacy | Path
Feature Engineering | Path
Interpreting Data with Python | Path
Machine Learning Literacy — Practical Application | Path
Machine Learning Literacy | Path
[spoiler="Содержание"]
1. Course Overview 1. Course Overview 2. Getting Started 1. Version Check
2. Setting Expectations
3. PyCharm Editions
4. Installation
5. Customizing PyCharm
6. Interpreters 3. Understanding Code 1. Projects
2. Searching
3. Find Usages
4. Diagrams
5. Hierarchies
6. Documentation
7. Running Code
8. Running Tests 4. Writing Better Code 1. Code Completion
2. Refactoring
3. Demo
4. Inspections
5. Testing
6. Version Control with Git 5. Debugging Code 1. Breakpoints
2. Debug Tool Window
3. Stepping
4. Attaching to a Process
5. Remote Debugging 6. Exploring Data 1. Scientific Mode
2. Exploring a Dataset
3. Jupyter Notebooks
4. R Scripts
5. SQL Queries
6. Summary
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Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 199 kb/s
Аудио: AAC, 44.1 kHz, 96.0 kb/s, 2 channels
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