About this course
This course provides you with the skills to build a predictive model from the ground up, using Python.
You will learn the full lifecycle of building the model. First, you'll understand the data discovery process and discover how to make connections between the predicting and predicted variables. You will also learn about key data transformation and preparation issues, which form the backdrop to an introduction in Python for data analytics.
Through the analysis of real-life data, you will also develop an approach to implement simple linear and logistic regression models. These real-life examples include assessments on customer credit card behavior and case studies on sales volume forecasting.
This course is the first in the MicroMasters program and will prepare you for modeling classification and regression problems with statistical and machine learning methods.
What you'll learn
In this course you will:
- Understand the predictive analytics process
- Gather and prepare data for predictive modelling
- Clean datasets to prevent data quality issues in your models
- Implement linear and logistic refression models using real-life data
Syllabus
Week 1: Introduction to Predictive Modelling
Week 2: Python and Predictive Modelling
Week 3: Variables and the Modelling Process
Week 4: Transformation and Preparation of Data
Week 5: Data Quality Problems and Other Anomalies
Week 6: Regression and Case Study
