This is the age of Big Data. Market forecasts indicate that worldwide revenues for big data and business analytics solutions are expected to reach $189.1 billion this year, an increase of 12% since 2018. IDC expects worldwide BDA revenue will be $274.3 billion by 2022 (Source: IDC).
Organisations hold more information about their business environments than ever before. Increasingly, these organisations are recognising the role of data in gaining insights and out-thinking competitors. As a result, there is a growing demand for employees and managers who have experience of Python and machine learning as well as having analytical skills and can make informed decisions that can drive organisational success.
Predictive analytics is being adopted universally to predict trends and build models that support proactive business decisions and identify both risks and opportunities.
Through this MicroMasters you will be introduced to the major concepts used in a predictive model, learn how to prepare data for modelling, and build predictive models using a range of statistical and machine learning methodologies on a variety of real-life datasets.
This MicroMasters has been designed to equip you with the skills to successfully set up and deploy your own predictive analysis. The course breaks the process down into four modules that will help you develop strong capabilities in this field. You can take just one module, some of the modules, or all of them. To gain a MicroMasters qualification and academic credit, you need to complete and pass all modules and the final project assessment.
Module 1: Introduction to Predictive Analytics using Python
Take a deep-dive into the predictive analytics process and its first steps.
Module 2: Successfully Evaluating Predictive Models
Gain an in-depth overview of evaluation and sampling approaches for predictive modelling.
Module 3: Statistical Predictive Modelling and Applications
Learn how to use regression and Bayesian inference to build statistical predictive models.
Module 4: Predictive Analytics using Machine Learning
Build predictive models using tree-based and other machine learning approaches.
Module 5: Predictive Analytics Final Project
Apply your predictive modelling acumen in a business case setting.
- Demand for Business/Management Analyst roles in the USA is predicted to grow by 14.3% over the next 10 years, attracting a median annual salary of $81k. (Source: Bureau of Labor Statistics (projected growth) and Burning Glass analysis.
- Skills such as Python and machine learning are in very high demand in virtually every business sector
- A deep understanding of predictive analysis has become pivotal to business success
Learners who successfully complete the programme can continue to progress their current career or move into a broad range of roles such as:
- Analytics associates
- Business analysts
- Business consultants
- Business intelligence and analytics consultants
- Business process analysts
- Data analysts
- Management consulting associates
- Metrics and analytics specialists
You will need:
- Access to a computer
- Access to the internet
- An up-to-date web browser
This MicroMasters programme is intended for those who have undergraduate level or equivalent professional experience/background in mathematics, statistics, or similar subject area (such as linear algebra, calculus). Previous experience with a procedural programming language is beneficial (such as Java, C, Python, Visual Basic).
The course is taught in English, so you will require a competent level of English in order to cope with the rigours of the course.
There is an opportunity to gain the pre-requisites in (for example) linear algebra and experience of programming in Python from existing courses on edX:
Our modules are delivered on the online learning platform edX and are charged in dollars.
|1: Introduction to Predictive Analytics using Python||US$150|
|2: Successfully Evaluating Predictive Models||US$300|
|3: Statistical Predictive Modelling and Applications||US$300|
|4: Predictive Analytics using Machine Learning||US$300|
|5: Predictive Analytics Final Project||US$450|
- Learn from leading international academics at the forefront of research in this field
- Gain knowledge and learning that could take you onto our MSc in Business Analytics, ranked 9th in the world (QS Business Analytics Rankings 2019)
- Achieve a qualification through edX that is taught by a world-renowned institution (ranked 19th in QS World University Rankings 2019)
- Assurance of quality as the University of Edinburgh Business School holds triple accreditation (AACSB, AMBA, EQUIS)
Learners who have completed all 5 modules successfully and earn this MicroMasters certificate can apply to the Master’s degree offered on campus at the University of Edinburgh in Business Analytics or the online programme in Data Science, Technology and Innovation. Completing the MicroMasters will not guarantee acceptance and the standard University admissions process and criteria will apply. When applying, students must upload their MicroMasters certificate to their application.
MSc Business Analytics
Applicants who meet all the entry requirements and are successfully admitted onto the Master’s in Business Analytics on-campus programme can expect that their MicroMasters coursework, comprising 30 credits of the total 180 credits of the master’s degree, will be recognised as prior learning. Please note that applicants must apply within 2 years of successfully completing the Predictive Analytics using Python.
MSc Data Science, Technology and Innovation
Applicants who meet all the entry requirements and are successfully admitted onto the Master’s in Data Science, Technology and Innovation can expect that their MicroMasters coursework, comprising 30 credits of the total 180 credits of the master's degree, will be recognised as prior learning. Those applying for the Postgraduate Certificate or Postgraduate Diploma will only be able to have 20 credits recognised as prior learning. Please note that applicants must apply within 2 years of successfully completing the Predictive Analytics using Python.