This MicroMasters program is designed for data analysts and data scientists and will teach you how to prepare data for predictive modelling, data mining, and advanced analytics using a range of statistical and machine learning methodologies on real-life datasets. Upon completion, this program will equip you with the skills to drive better decision making, identify risks, and deliver value to your organisation.
In the era of Data Science and Big Data, organisations hold more information about their business environments than ever before. Increasingly, these organisations are recognising the role of data science in leveraging current and historical data and out-thinking competitors. As a result, there is a growing demand for employees and managers who have advanced data analytics skills and the ability to make informed decisions that drive organisational success.
This MicroMasters program is designed for those who have an undergraduate level, or equivalent professional experience/background in mathematics, statistics, or programming (Java, C, Python, Visual Basic).
Courses under this program: Course 1: Introduction to Predictive Analytics using Python
Learn the predictive modelling process in Python. Create the insights needed to compete in business.
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.
A predictive exercise is not finished when a model is built. This course will equip you with essential skills for understanding performance evaluation metrics, using Python, to determine whether a model is performing adequately.
Specifically, you will learn:
Appropriate measures that are used to evaluate predictive models
Procedures that are used to ensure that models do not cheat through, for example, overfitting or predicting incorrect distributions
The ways that different model evaluation criteria illustrate how one model excels over another and how to identify when to use certain criteria
This is the foundation of optimising successful predictive models. The concepts will be brought together in a comprehensive case study that deals with customer churn. You will be tasked with selecting suitable variables to predict whether a customer will leave a telecommunications provider by looking into their behaviour, creating various models, and benchmarking them by using the appropriate evaluation criteria.
In this course, you will learn three predictive modelling techniques - linear and logistic regression, and naive Bayes - and their applications in real-world scenarios.
The first half of the course focuses on linear regression. This technique allows you to model a continuous outcome variable using both continuous and categorical predictors. This technique enables you to predict product sales based on several customer variables.
In the second half of the course, you will learn about logistic regression, which is the counterpart of linear regression, when the response variable is categorical. You will also be introduced to naive Bayes; a very intuitive, probabilistic modeling technique.
This course will give you an overview of machine learning-based approaches for predictive modelling, including tree-based techniques, support vector machines, and neural networks using Python. These models form the basis of cutting-edge analytics tools that are used for image classification, text and sentiment analysis, and more.
The course contains two case studies: forecasting customer behaviour after a marketing campaign, and flight delay and cancellation predictions.
You will also learn:
Sampling techniques such as bagging and boosting, which improve robustness and overall predictive power, as well as random forests
Support vector machines by introducing you to the concept of optimising the separation between classes, before diving into support vector regression
Neural networks; their topology, the concepts of weights, biases, and kernels, and optimisation techniques
The final project brings together the skills and knowledge acquired throughout the MicroMasters programme. You will draw on your knowledge of data analysis techniques to demonstrate your capacity to deal effectively with current job market needs.
You will have the opportunity to demonstrate that you can crunch vast amounts of information to gain valuable insight, as well as use a range of approaches for extracting hidden information and building intelligence to assist with decision making.
You will also have to independently apply the methods and tools used to address common practical issues faced by data analysts today, and consolidate your understanding of the most effective methodologies used through hands-on experience. This final project will prepare you for a step change in career or set you up to pursue further study.
Please note, this course is only available to learners who have successfully completed all 4 MicroMasters courses on the verified track prior to undertaking this course:
PA1.1x Introduction to Predictive Analytics using Python
PA1.3x Statistical Predictive Modelling and Applications
PA1.4x Predictive Analytics using Machine Learning
Learners who successfully complete this final course as part of the MicroMasters programme can apply to the on-campus Masters in Business Analytics at the University of Edinburgh. Successful completion of the MicroMasters programme does not guarantee acceptance to the Master's but, if accepted, the 30 credits awarded from the MicroMasters program will be recognised as credit obtained towards the 180 credits required for the full MSc. Visit the University of Edinburgh Business Analytics Entry Requirements page for more information.
Dr Galina Andreeva, Dr Johannes De Smedt, Dr Matthias Bogaert and Sofia Varypati
Start your review of Predictive Analytics using Python
The course content is good (4/5). Average delivery: sometimes it feels like watching Sheldon Cooper's fun with flags (from Big Bang Theory). Also you might need to reach out to other sources as Wiki as the topics are not always clearly explained. One...
The course content is good (4/5). Average delivery: sometimes it feels like watching Sheldon Cooper's fun with flags (from Big Bang Theory). Also you might need to reach out to other sources as Wiki as the topics are not always clearly explained. One star down as reinforcement learning is not covered at all.
The biggest issue is with the test and evaluation. The tasks are not well described and answers in quiz questions not always align with what you find in the course. It is often a guessing game.
The worst are coding assessments. Creators of the course have not taken into consideration large variety of possible inputs due to characteristics of the problems. It leads to the situations where you code fails automatic grading despite of being bug free and fulfilling all stated assessment goals.
Overall there are better offerings on the edX. I do not recommend it.