Boost Data Science Productivity with PyCharm from Pluralsight

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.

What's inside

Syllabus

Course Overview

Getting Started

Understanding Code

Writing Better Code

Debugging Code

Exploring Data

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

If you work in data science and use Python, this course can make you more productive in your projects

Teaches a set of tips, tricks, and techniques and demonstrates them with step-by-step demonstrations

Taught by a recognized expert in the field

Helps students improve their coding skills by learning about code completion, refactoring, and inspections

Designed for data scientists, this course is relevant to your professional practice

This course covers the basics of understanding code, writing better code, and debugging code

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Boost Data Science Productivity with PyCharm with these activities:

Python review before PyCharm

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In order to get the most out of PyCharm, refresh your Python coding knowledge to ensure you know the basics of the language.

Browse courses on Python

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Review Python tutorials.
Complete online Python exercises.

Practice Python

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Improve coding ability by practicing Python skills in an interactive environment.

Browse courses on Python

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Install Python and set up a development environment
Complete coding exercises and tutorials to reinforce basic syntax and data structures

Read 'Head First Python' for PyCharm

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To build a strong foundation in Python and PyCharm, read this book and complete chapter review exercises.

View Head First Python: A Learner's Guide to the... on Amazon

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Locate the book online.
Read and review chapters.
Complete coding exercises.

Nine other activities

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Attend a PyCharm workshop or webinar

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Find in-person workshops or online webinars on PyCharm to enhance your existing knowledge.

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Search for workshops or webinars.
Register for attendance.
Attend the event.

Coding exercises for PyCharm

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Coding exercises will help you reinforce your comprehension and develop muscle memory for Python coding within the PyCharm IDE.

Browse courses on Python

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Locate chapter exercises.
Code solutions
Review solutions

PyCharm quick tips and tricks

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Find and review tutorials and guides online to learn the most effective shortcuts and features within your PyCharm IDE.

Browse courses on PyCharm

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Search for online resources.
Choose 2-3 videos or blog posts.
Review the content.

Learn PyCharm Features

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Enhance code understanding and debugging skills by exploring PyCharm's features through interactive tutorials.

Browse courses on PyCharm

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Follow online tutorials on PyCharm's code navigation, refactoring, and debugging tools
Apply the techniques learned in real-world Python projects

Practice Data Analysis with PyCharm

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Reinforce your knowledge of data analysis by completing exercises and examples in PyCharm.

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Solve coding puzzles and challenges using PyCharm's features.
Debug errors and exceptions to improve code quality.
Analyze datasets and visualize results to gain insights from data.

Join a PyCharm study group

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Joining a study group can help you learn from others and solidify your understanding of PyCharm.

Browse courses on Discussion

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Find a study group.
Attend meetings.
Participate in discussions.

Study Group Discussions

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Enhance understanding and retention by discussing course concepts and PyCharm techniques with peers.

Browse courses on PyCharm

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Form study groups with classmates
Meet regularly to discuss course material, share insights, and work on projects together

Data Science Project with PyCharm

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Consolidate learning by applying PyCharm skills to a practical data science project.

Browse courses on PyCharm

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Choose a data science problem and gather the necessary data
Use PyCharm to analyze, visualize, and model the data
Create a report or presentation showcasing the project

Document your data science project using PyCharm

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Create a well-documented data science project in your PyCharm IDE, focusing on clear explanations, organization, and thoroughness of your work.

Browse courses on Data Science

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Plan your documentation strategy.
Write clear and concise documentation.
Organize your documentation into sections.

Career center

Learners who complete Boost Data Science Productivity with PyCharm will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

**Machine Learning Engineers** design, develop, and deploy machine learning models. They use programming, mathematics, and statistics to create models that can learn from data and make predictions. PyCharm is a great tool for Machine Learning Engineers because it provides features that can help them write better code, debug code more effectively, and explore data more efficiently.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

**Data Scientists** use scientific methods to analyze and interpret data. They use statistics, machine learning, and programming to uncover patterns and trends in data. PyCharm is a powerful Python IDE that can help Data Scientists write better code faster, debug code more effectively, and explore data more efficiently. With PyCharm's help, Data Scientists can become more productive and efficient in their work.

See salaries and explore the career path for Data Scientist

Data Analyst

**Data Analysts** collect, clean, and analyze data to identify trends and patterns. They use their findings to make recommendations and inform decision-making. PyCharm can help Data Analysts become more productive and efficient in their work by providing features that can help them write better code faster, debug code more effectively, and explore data more efficiently.

See salaries and explore the career path for Data Analyst

Software Engineer

**Software Engineers** design, develop, and maintain software applications. PyCharm is a great tool for Software Engineers because it provides features that can help them write better code, debug code more effectively, and explore data more efficiently.

See salaries and explore the career path for Software Engineer

Data Visualization Analyst

**Data Visualization Analysts** translate data into visual representations, such as charts, graphs, and maps. They use their findings to make data more accessible and easier to understand. PyCharm may be useful for Data Visualization Analysts because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Data Visualization Analyst

Web Developer

**Web Developers** design, develop, and maintain websites. PyCharm may be useful for Web Developers because it can help them write Python code more quickly and efficiently.

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Operations Research Analyst

**Operations Research Analysts** use mathematical and analytical methods to help businesses improve their operations. PyCharm may be useful for Operations Research Analysts because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

**Business Analysts** help businesses improve their performance by analyzing data and identifying opportunities for improvement. PyCharm may be useful for Business Analysts because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Business Analyst

Financial Analyst

**Financial Analysts** help businesses make sound financial decisions by analyzing financial data. PyCharm may be useful for Financial Analysts because it can help them write Python code more quickly and efficiently.

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Market Research Analyst

**Market Research Analysts** help businesses understand their customers and their needs. PyCharm may be useful for Market Research Analysts because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Market Research Analyst

Statistician

**Statisticians** collect, analyze, and interpret data to draw conclusions about the world. PyCharm may be useful for Statisticians because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Statistician

Computer Scientist

**Computer Scientists** design, develop, and analyze software and hardware systems. PyCharm may be useful for Computer Scientists because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Computer Scientist

Information Security Analyst

**Information Security Analysts** protect computer systems and networks from unauthorized access and cyberattacks. PyCharm may be useful for Information Security Analysts because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Information Security Analyst

Software Tester

**Software Testers** test software to identify bugs and ensure that it works as expected. PyCharm may be useful for Software Testers because it can help them write Python code more quickly and efficiently.

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Data Engineer

**Data Engineers** build and maintain the infrastructure that stores and processes data. PyCharm may be useful for Data Engineers because it can help them write Python code more quickly and efficiently.

See salaries and explore the career path for Data Engineer