FOUNDATIONS FOR DATA SCIENCE
Data has become one of the most valuable assets for companies today. Indeed, the extraction of knowledge from data is a highly valued and sought-after task by companies. In other words, the profile of the data scientist is booming.
The task of transforming big data into valuable information has become imperative in business because of its potential to answer complicated questions and offer predictions.
For this reason, there is an increasing demand for experts in advanced statistical analysis techniques and modern visualisation tools. In this sense, this course serves as a springboard for improving the qualities of these professional profiles.
Without a doubt, this education is an excellent way to expand or update knowledge in two rising disciplines: Data Science and Big Data. Thus, the student will be able to master data analysis and optimise their performance in the company.
Discover the key modules and concepts
INTRODUCTION TO DATA SCIENCE
- The Scientific Method.
- Data Science vs Business Analytics vs Business Intelligence.
- Different analyses, different technologies.
- CRISP-DM.
UNDERSTANDING THE BUSINESS
- The importance of understanding business processes.
- Business model:
- Value chain.
- Identification of relevant variables.
- Definition of indicators.
UNDERSTANDING THE DATA
- Identification of data sources.
- Inventory of variables.
- Diagnosis of data quality.
DATA PREPARATION
- Descriptive analysis.
- Loading strategies.
- Data modelling:
- Dimensions and metrics.
- Masters and facts.
MODELLING
- Descriptive models.
- Predictive models.
- Classification.
- Regression.
- Association.
EVALUATION OF THE MODEL
PUTTING INTO PRODUCTION
- Trust: tests with the Champion.
- Presentation: the importance of reaching all audiences.
- Loopback: training endlessly.
admission process
Description
In the world of Data Science and Big Data, where data is the protagonist, terms such as Data Quality, Data-Driven Decision Making, and data monetisation have emerged. These concepts cover various fields and have unquestionable value.
This course focuses on both disciplines with the aim of providing students with a comprehensive view of how they can transform their professional performance in the company. Ignoring data management is to renounce success.
In this context, education highlights the need to have both a broad knowledge of technology and a business vision. These skills, together with data management, will enable decisions to be directed towards a substantial improvement in the company's performance.
Objectives
After completing the course, participants will be able to:
1. Introduce students to the scientific method applied to data analysis.
2. Develop a methodology for carrying out data analysis.
3. Understand the usefulness of the next modules of the master's degree, where each of the technologies to be studied fits in and what its purpose is.
4. Know how to apply the methodology to business processes.
Methodology
ENAE uses an active and participative methodology, ‘learning by doing’, which alternates the presentation of concepts, techniques and methods of analysis with the development of practical cases reflecting real business situations.
By promoting teamwork, the aim is to achieve the integration of all members and to resolve the cases presented more effectively, through the exchange of different points of view, opinions and experiences. Students will learn from the trainers but also from the professional experiences of their classmates.
