Duration: 3 Days
Technologies: R, Python
Overview: So, you’ve learned about the power of analytics and advanced analytics techniques, but are you getting the most out of your data? Are you struggling to match your data to the business problems you are trying to solve? Are you using the best data structures to drive your analytics models? Are you focusing on the most appropriate target metrics? Using examples from customer segmentation to sales forecasting, this course demystifies the techniques required to transform raw data into insight-filled data for driving your analytics solutions.
Outcomes: After completing the course delegates will be capable of creating the data structures required to drive predictive analytics models. Delegates will have a full understanding of the sources of data used for analytics, how to perform exploratory data analysis, how to design and develop the business concepts and data metrics required for a business problem, and how to implement data metrics.
Pre-requisites: Before taking the course delegates should be:
- Familiar with basic predictive analytics techniques (e.g. clustering, market basket analysis, regression models, decision tree models).
- Comfortable with basic statistical concepts and techniques.
- Comfortable with basic data manipulation tools such as spreadsheets and databases.
Who: This course is targeted at delegates who are familiar with analytics techniques and are involved in deploying analytics solutions in their organisations.
Outline: The course will explore the following topics through a series of interactive workshop sessions:
- What is predictive analytics?
- The CRISP-DM analytics project methodology
- Converting business questions into analytics questions
- Data structures for predictive analytics
- Understanding where data comes from in an organisation
- Exploratory data analysis techniques
- Developing business concepts
- Designing data metrics
- Building the analytics base table