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
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.
Week 1: Introduction to Predictive Modelling
Week 2: Python andPredictive 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
You should be familiar with an undergraduate level, or have a background, in mathematics and statistics. Previous experience with a procedural programming language is beneficial (e.g. Python, C, Java, Visual Basic).
What type of activities will I complete on the course?
This course foregrounds self-directed and active ways of learning: reading, coding in Python, knowledge check quizzes, and peer discussion. In addition, the course features videos that demonstrate relevant predictive analysis techniques and concepts.
What software will I be required to use?
All coding activities on this course will be hosted on Vocareum. You will be able to access this free software directly within the edX platform. There is no requirement to purchase further software in order to complete this course.
What do I need to complete the course?
For successful completion of this course, you will need access to a computer or mobile device and a reliable internet connection.
What is the University of Edinburgh Accessibility Guidance?
The University of Edinburgh is committed to providing online information and services accessible to all. Edx provide an accessibility statement which is available via the footer of all edx.org pages and includes an 'Accessibility Feedback' form which allows Learners to register feedback directly with the edx. Courses created by the University of Edinburgh contain an Accessibility Statement which addresses equality of access to information and servicesandis available via the 'Support' page.