Applied Data Science with Python

"Applied Data Science with Python" is an online course designed to teach students how to use Python programming language to analyze and manipulate data, build machine learning models, and communicate insights effectively. The course consists of several modules, each covering a different aspect of data science. The first module focuses on the fundamentals of Python programming, including data types, variables, functions, and control flow. Students will learn how to use popular Python libraries such as NumPy, Pandas, and Matplotlib to load, clean, and visualize data. The second module dives deeper into data manipulation, covering topics such as data cleaning, data wrangling, and feature engineering. Students will learn how to use Pandas to handle missing data, aggregate data, and transform data into the desired format. They will also learn how to apply statistical methods to identify patterns and relationships in the data. The third module focuses on machine learning, covering the basics of supervised and unsupervised learning. Students will learn how to build and evaluate machine learning models, including decision trees, logistic regression, and k-means clustering. They will also learn how to optimize their models for better performance and interpret their results. The fourth and final module covers data visualization and communication. Students will learn how to use Matplotlib and Seaborn to create clear and compelling visualizations that effectively communicate their findings. They will also learn how to present their results in a way that is understandable and actionable to stakeholders. Overall, "Applied Data Science with Python" provides a comprehensive and practical introduction to the field of data science. By the end of the course, students will have gained the skills and knowledge necessary to tackle real-world data science problems and communicate their insights effectively. The course is ideal for anyone interested in pursuing a career in data science or looking to enhance their existing skills in the field. Author: CognitiveClass.ai