Course Description

CS250: Python for Data Science is a course that focuses on using Python programming language for data analysis, visualization, and machine learning. The course covers the fundamentals of Python programming, as well as the essential libraries and tools used in data science. The course starts with an introduction to Python programming language, covering the basic syntax, data types, control structures, functions, and modules. Students will learn how to write Python scripts to read, process, and output data in different formats, including CSV, JSON, and XML. The course then dives into the core libraries used in data science, including NumPy, Pandas, Matplotlib, and Seaborn. Students will learn how to use these libraries to perform numerical computations, manipulate and analyze data, create visualizations, and build predictive models. Next, the course covers machine learning, one of the most important and rapidly growing areas of data science. Students will learn the basics of supervised and unsupervised learning, including regression, classification, clustering, and dimensionality reduction. They will also learn how to use scikit-learn, a powerful machine learning library in Python, to build and evaluate machine learning models. The course also covers advanced topics in data science, such as natural language processing (NLP), deep learning, and big data processing. Students will learn how to use libraries such as NLTK, TensorFlow, and Spark to analyze text data, build deep neural networks, and process large datasets. Throughout the course, students will work on hands-on projects and assignments that reinforce the concepts covered in the lectures. They will also have access to a range of resources, including textbooks, online tutorials, and a community of fellow learners. By the end of the course "CS250: Python for Data Science", students will have a solid understanding of Python programming for data science and the essential libraries and tools used in this field. They will be able to apply these skills to real-world data science problems, and they will be well-prepared for further studies in data science or related fields. Author: Saylor Academy