Data Mining in Python
By External Provider
About this course
In “Data Mining in Python,” you will learn how to extract useful knowledge from large-scale datasets. This course introduces basic concepts and general tasks for data mining. You will explore a wide range of real-world data sets, including grocery store, restaurant reviews, business operations, social media posts, and more. You will learn how to formally describe real-world information with general data representations (e.g., itemsets, vectors, matrices, sequences, and more). You will then learn how to formulate data in the wild with one or more of these representations. This course will teach you how to characterize and explain your data by looking for patterns and similarities, which are basic building blocks for advanced analysis and machine learning models. This is the first course in “More Applied Data Science with Python,” a four-course series focused on helping you apply advanced data science techniques using Python. It is recommended that all learners complete the Applied Data Science with Python specialization prior to beginning this course.
What you will learn
- Understand basic concepts, tasks, and procedures of data mining.
- Formulate real-world information using basic data representations: itemsets, vectors, matrices, sequences, time series, and networks.
- Use data mining algorithms to extract patterns and similarities from real-world datasets.
- Calculate the importance of patterns and prepare for downstream machine-learning tasks.