She graduated in Mathematics from the Complutense University of Madrid (UCM) in 1996 and earned her PhD in Mathematics from the Carlos III University of Madrid (UC3M) in 2004. She is a tenured professor in the Department of Statistics and Operations Research. She participates in research projects at UC3M and URJC. She has completed the PhD program in Mathematical Engineering at UC3M. Her research interests focus on non-stationary time series, non-parametric inference, resampling techniques, and the analysis and development of prediction techniques for time series
Data Analysis and Big Data Techniques
About this course
Itinerary
Description
In a globalized and increasingly dynamic world, making the right decisions quickly and efficiently is essential in many areas of our daily lives. For any business sector, it is crucial to have professionals who can combine large amounts of data and information to make decisions based on objective evidence.
This course is designed for anyone seeking an introductory and practical overview of data analysis and big data. Specifically, the MOOC focuses on fundamental concepts, methods, and tools for processing, analyzing, and building statistical models with diverse data types. This knowledge is of particular interest to students, professionals, and managers interested in understanding the core principles of data analysis and applying big data methods and techniques using leading technologies and tools in this field.
What you will learn
- Techniques and methods for analyzing and visualizing data in one dimension and in multiple dimensions, using statistical tools, software and models.
- More advanced methodologies for data modeling and analysis applied to the field of econometrics.
- Most important methods, technologies and tools for the analysis of large volumes of data (big data).
- Key trends and cutting-edge aspects that will influence the development of the methods, techniques, and tools covered in the course over the next few years.
Requirements
- There are no formal requirements to take the MOOC, although it is advisable to start from a university diploma, degree, engineering degree or postgraduate training in technical areas or related to statistics, economic sciences or management systems.
Faculty
Clara Simón de Blas
King Juan Carlos University
Lecturer and coordinator of the Mathematics degree program at URJC. She previously worked as a project manager at Saint Louis University, Avon Cosmetics, ICA, and Bayes Forecast. She participates in research projects at UCM and URJC. She has collaborated with the University of Graz (Austria) and Berkeley (USA) as a postdoctoral researcher. Her current research interests include time series, management and efficiency of public organizations, statistical applications, social networks, and humanitarian logistics.
José Felipe Ortega Soto
King Juan Carlos University
Researcher and academic director of the Master's Program in Data Science at URJC. He has worked as a coordinator and researcher on more than 35 national and international projects. He has lectured at prestigious institutions such as Xerox PARC and the Cervantes Institute. His research focuses on massive online collaboration and the application of data science techniques and methods.
Frequently Asked Questions
What type of audience might be interested in taking the MOOC?
This course is for graduates of any university, including those with diplomas, technical engineering degrees, bachelor's degrees, and advanced engineering degrees. It is especially suitable for individuals with degrees in Computer Engineering, Industrial Engineering, Technical Engineering in Computer Science and Management, Technical Engineering in Computer Systems, Telecommunications Engineering, Industrial Organization, Bachelor's degrees in Mathematics, Statistics, Economics, Business Administration and Management, diplomas in Statistics, and related fields.
What can it be used for?
Numerous recent reports maintain that one of the most in-demand profiles today is that of a professional in business analytics and Big Data with the ability to combine decision-making methods and advanced computing technologies.
What certification do I get if I complete it?
You will be able to obtain the MOOC completion certificate once you have completed all the required course activities. The certificate will confirm your successful completion of the MOOC and will include the total number of hours.
How can I register?
To enroll in this course, simply log in or create your account and then click on the Start.
Which browsers are compatible with a URJC MOOC?
Current versions of Chrome, Firefox, Safari, or Internet Explorer version 9 or higher.
What happens if I have to drop out of a MOOC? Will I be able to re-enroll in a future edition of the same MOOC and/or another MOOC in the future?
Enrollment and participation in a URJC MOOC is free. There are absolutely no academic penalties for dropping out. You can enroll in the same MOOC and/or others (as long as they are still being offered) at a later time.
When does my MOOC start and end?
This MOOC is designed to be self-paced. You don't need to start at a specific time, although a learning pace of one topic per week is recommended.
How do I pass the course?
At the end of each module you will be assessed with a test on the basic concepts learned.
RAC credit validation
If you are an undergraduate student at Rey Juan Carlos University, you must register for the course using your university account (@alumnos.urjc.es) to receive RAC credits upon successful completion. Credits will not be awarded to students who completed the course using an account other than their URJC account or who are not currently enrolled in an undergraduate degree program.
🙋 You won't need to request the recognition, as it will appear automatically.
- 5 Sections
- 51 Lessons
- 20 Hours
Expand all sectionsCollapse all sections
- 1. Univariate Data Analysis and Visualization10
- 1.1Sample design
- 1.2The scale: types and selection criteria
- 1.3Database development
- 1.4Measures of centrality and variability
- 1.5Calculating measurements using computer tools
- 1.6Univariate data visualization
- 1.7Variable distribution pattern
- 1.8Classification of variables with tools
- 1.9Exam
- 1.10Additional content
- 2. Multivariate Data Analysis and Visualization11
- 2.1Relationship between quantitative variables: types and selection criteria
- 2.2Calculation of relational measures using computer tools
- 2.3Visualization of multivariate relationships using computer tools
- 2.4Relationship between qualitative variables: types and selection criteria
- 2.5Creating contingency tables using computer tools
- 2.6Calculation of relational measures using computer tools
- 2.7Data classification and reduction methods
- 2.8Selection criteria for exploratory tools for multivariate relationships
- 2.9Verification of assumptions for data classification and reduction methods using computer tools
- 2.10Exam
- 2.11Additional content
- 3. Econometric Techniques (Modeling and Prediction)9
- 3.1Concept, data and its handling, introduction to Gretl
- 3.2Simple Linear Regression Model: Elements, hypotheses, estimation
- 3.3Multiple Linear Regression Model
- 3.4Contrasts, diagnosis, prediction
- 3.5Economic Forecasting Techniques
- 3.6Analysis of a series with a trend
- 3.7Guidelines for evaluation
- 3.8Exam
- 3.9Additional content
- 4. Big Data: Concepts, Methods and Technologies11
- 4.1Introduction to Big Data Processing and Analysis
- 4.2Data Type Classification
- 4.3Big Data Project Development Cycle
- 4.4Data Processing Strategies
- 4.5Hybrid Architectures for Big Data
- 4.6Laboratory Analysis vs. Production Analysis
- 4.7Laboratory Analysis vs. Production Analysis
- 4.8The Apache Ecosystem of Hadoop
- 4.9Introduction to NoSQL Databases
- 4.10Exam
- 4.11Additional content
- 5 Trends in Data Analysis and Big Data10
- 5.1Data Mining / Machine Learning
- 5.2Risk Analysis and Quality Management
- 5.3Data Analysis and Visualization for Decision Making
- 5.4Methodologies for advanced modeling and prediction
- 5.5Univariate and Multivariate Modeling
- 5.6Debate on Trends in Big Data Analytics
- 5.7Cloud Computing and Big Data
- 5.8Applications of Big Data Analytics
- 5.9Exam
- 5.10Additional content
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