2/29/2024 0 Comments Basic data science for pythonThe material on this site is written in Jupyter notebooks and rendered using Jupyter Book to make it easily accessible. Wrangle different types of data in Pandas including numeric data, strings, and datetimes.Use Pandas to create and manipulate data structures like Series and DataFrames.Use NumPy perform common data wrangling and computational tasks in Python.Produce human-readable code that incorporates best practices of programming, documentation, and coding style.Know when and how to abstract code (e.g., into functions, or classes) to make it more modular and robust.Understand how to write functions in Python and assess if they are correct via unit testing.Understand the key data structures in Python.Translate fundamental programming concepts such as loops, conditionals, etc into Python code.These are the key learning outcomes for this material: That material has built upon previous course material developed by Patrick Walls and Mike Gelbart. The content of this site is adapted from material I used to teach the 2020/2021 offering of the course "DSCI 511 Python Programming for Data Science" for the University of British Columbia's Master of Data Science Program. Or, if you'd like to learn more about using Python and PyTorch for deep learning, you can check out my other online material Deep Learning with PyToch. If you're interested in learning more about Python packages, check out my and Tiffany Timber's book Python Packages. We'll cover topics such as data structures, basic programming, code testing and documentation, and using libraries like NumPy and Pandas for data exploration and analysis. This course is designed for experts, managers, and researchers from other fields, who are willing to apply programming skills and feature to projects in their own domain. This course is not intended for computer science students.Welcome to Python Programming for Data Science! With this website I aim to provide an introduction to everything you need to know to start using Python for data science. Create a project in their own practical domain to demonstrate their ability in programming skills and applied data science techniques and tools in Python.Apply various skills and programming tools of data analysis and process, including import, clean, manipulate, and visualization of data.Understand, develop, and apply the fundamental knowledge of programming in Python environment, including algorithms, data types and variables, expressions, program flow and decision statements (such as branching and loop), more complex data structures (such as list, tuple, set, and dictionary), functions, file, etc.Leerdoelenīy completing this course, students are able to: Practice through lab exercises, and you will be ready to create your first python program in your own domain. Python is a versatile language for data science, from basic data handling to advanced machine learning and deep learning. This course provides a beginner-friendly introduction to Python with a special focus on data analysis. Python is a programming language used by many data scientists to clean data, make visualizations and build decision or prediction models or perform classification and clustering. In order to be an outstanding data analyst, they need to also have programming skills in addition to their own domain. They can extract valuable information from chaotic data. This course will introduce the learner to the basics of the python programming ecosystem and how skills of programming and other features in python can be used in analysis of various data.Ī data analyst can use programming tools to explore and process large amounts of complex data and find relevant information or pattern from this data. It is a great language for beginners because it is concise and easy to read. Python is a multi-purpose, flexible, and powerful programming language, with a broad diversity of relevant jobs in many fields, especially data science. Lees de ingangseisen goed door voordat je je inschrijft. Let op: deze minor heeft een selectieprocedure.
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