Intro to Predictive Data Analytics
Learn how to use Python and its powerful free libraries to read, clean, analyze, and visualize data
This course will introduce you to predictive data analytics with Python. It is designed for complete beginners. The estimated time to complete the course is 15 hours. The course focuses on using Python libraries to solve practical applications rather than on the underlying math concepts. You will learn how to import data from various types of data files, clean the data, and work with the so-called DataFrames. We will review basic statistics concepts ncluding the median, mode, mean, variation, standard deviation and boxplots. You will learn linear, polynomial, multiple and logistic regression, and use these techniques to analyze real-world data sets.
You do not need to know college-level calculus or statistics to take this course. You will work with some middle to high school math which we will review as we go: the equation of the line, how to calculate the average of a set of values, etc. Intro to Predictive Data Analytics uses the Python programming language, and therefore basic knowledge of Python commands, structures and syntax is required. We recommend that you take NCLab’s Intro to Python for Data Science in preparation for this course. We will not be writing complex Python programs, as we will mostly be using Python libraries. However, we will be working with lists, dictionaries, data files and other Python fundamentals.