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.
As a software engineer and lifelong learner, Dan wrote a PhD thesis and many highly-cited publications on decision making and knowledge acquisition in software architecture. Dan used Microsoft technologies for many years, but moved gradually to Python, Linux and AWS to gain different perspectives of the computing world.
Course Overview Hi everyone. My name is Dan Tofan, and welcome to my course, Boost Data Science Productivity with PyCharm. I'm a senior software engineer with a PhD in software architecture, and I really like getting things done. PyCharm is an integrated development environment for Python that helps getting things done. This course helps you increase your productivity by using PyCharm in your Python data science projects. Some of the major topics that we will cover include installing and customizing PyCharm, easy refactoring to write better code, debugging code to fix bugs and really understand code, exploring a dataset with Pandas, matplotlib, Seaborn, and Jupyter Notebooks, and even running R scripts and SQL queries from PyCharm. By the end of this course, you will know tips, tricks, and techniques to leverage PyCharm and increase your productivity in your data science projects. Before beginning this course, you should be familiar with Python and the basics of data science. Stop wasting your time on busywork. Start learning how to work faster and smarter in your data science projects by using PyCharm and watching this course here and now at Pluralsight.