How can we extract value from the data generated by our clients? Can we identify market trends through data? What are the patterns in consumer behaviour? Learn how to identify consumer trends and behaviour change and generate strategic value for the business through the use of data.
DATA SCIENCE EXPERT
LEARN WITH THE BEST:
Become a data science professional with the ability to contribute to decision-making in an efficient way by applying analytical techniques to specific business problems, obtaining useful information from data and offering innovative solutions in making decisions. You will learn to extract value from every set of data; supplying creative solutions and scientific results based on data and developing predictive models.
ESPECIALLY DESIGNED FOR:
- Professionals and Engineering, Maths or Statistics graduates.
- Graduates with an interest in the world of ICT.
EXECUTIVE SCHEDULE
Practical methodology. Programme adapted to working hours and with a practical methodology.
TECHNOLOGY
Classrooms are equipped with state-of-the-art technology.
FACULTY
Expert faculty made up of active professionals with extensive experience.
PRACTICAL TRAINING
Internship programme at leading companies. Profile with significant professional opportunities.
Data Science Expert
STUDY PLAN:
Introduction
SUBJECT
Data science
Comparison with traditional statistical analysis
Business models and applications of data analysis
Legal and regulatory aspects in data analysis
Distributed Computing Platforms
SUBJECT
Introduction to Apache Hadoop and Map-Reduce. Advantages
Other distributed computing models (Storm and Spark)
Introduction to R and Relevant Packages
SUBJECT
R and RStudio command interface
Configuration and installation of packages
Basic statistics commands in R
R relevant packages
Caret; e1071; Data.table; Rpart; RandomForest
Introduction to Python
SUBJECT
IPython development environment
Relevant libraries: NumPy; Scikit-learn; Pandas
Data Storage Techniques
SUBJECT
HDFS storage
NoSQL database storage
Advantages. When they are useful
MongoDB; CouchDB; Apache Cassandra
Redis
Tokyo Cabinet
Examples of NoSQL database storage
(MongoDB; Cassandra; Tokyo; Redis)
Preparation of Data
SUBJECT
Cleaning up and transforming data
Scale changes; NaN; nonexistent observations
Combination of diverse data sources
Pandas. Examples with the Python Pandas library
R: Examples of data preparation with R
Pig/Hive: Examples of data transformation with Pig/Hive
Data Analysis
SUBJECT
Difference between traditional statistical analysis and automatic learning
Descriptive statistical analysis. Probability and inference.
Examples with R
Simple Linear Regression and Logistics
SUBJECT
Examples with R
Design Methodologies for Data Analysis Applications
SUBJECT
Distribution of data
Test; Train and Validation Sets
Evaluation metrics
Confusion matrices (FP; TP; FN; TN)
Precision metrics; Recall; F-Measure. Cross-validation; Overfitting; ROC and AUC curves
Common problems
Unbalanced data sets and Skew distributions
Under-sampling and Over-sampling
A/B testing
Unsupervised learning and A/B Testing
ACTIVE learning
Supervised Learning Techniques
SUBJECT
Naive Bayes and SVM. Neural networks
Decision trees (ID3 and Cart)
Random forests
Unsupervised Learning Techniques: Clustering
SUBJECT
Collaborative Filtering and Content-based recommenders
Recommendation Systems
SUBJECT
Collaborative Filtering and Content-based recommenders
Approximation Algorithms and Stream Mining
SUBJECT
Hashing techniques; Min-Hash and LSH. Bloom filters
Data Visualisation and Reporting
SUBJECT
Dimension reduction techniques (PCA; ICA; SVD)
Visualising and communicating data (D3.js. Graphviz; Jung and ggplot2 in R)
Skills and Careers
SKILLS
- Be able to use the new management tools for large data sets, such as the Hadoop ecosystem and other distributed computing models such as Storm and Spark.
- Carry out advanced data analysis with R, RStudio and relevant packages such as Caret, e1071, RandomForest, data.table, spart, etc.
- Develop data analysis libraries in Python and use the libraries available in the IPython environment.
- Understand how to apply the legal and regulatory aspects that affect the analysis of massive data sets originating from automated files.
- Apply new methodologies for distribution, metrics, evaluation and data testing.
- Process data sets using new automatic learning techniques.
- Design and implement validation and testing strategies in production environments.
- Exploit data in NoSQL environments such as MongoDB, CouchDB, Cassandra, Redis, Tokyo Cabinet and Neo4J.
- Prepare and clean up data to allow for effective processing and knowledge discovery.
- Apply advanced descriptive and inferential statistical analysis techniques.
- Visualise data and create automatic reports with D3.js, Graphviz, Jung, ggplot2.
CAMPUS AND FACILITIES
Our classrooms are equipped with the same hardware and software used by the world’s major studios. This high-tech educational environment will allow you to work with the same tools and techniques used at all levels of digital production and to share the same work processes.
PROFESSIONAL DEVELOPMENT
Maintaining a direct connection with the industry from the first day is a key and fundamental part of our philosophy. We prepare our students so that they can immediately and spontaneously find work.
We have created the Career Development Service as part of our practical study methodology. This strategic unit acts as a link between students and companies, allowing them to work on real projects during their studies.
Our Career Development Service provides constant relations with leading companies in each sector throughout the students’ academic career, through internships, job vacancies, classroom projects and support for entrepreneurs.
ADMISSIONS PROCESS
Want to study with us? Here is what you need to do
GUIDANCE
We will guide you about your future and clarify all your questions
DOCUMENTATION
We will inform you about what documents you have to submit
RESERVE YOUR PLACE
Once we have informed you that your application has been accepted, you need to complete the admissions process
ENROLMENT
After complying with all of the requirements, you can complete your enrolment
REQUEST MORE INFORMATION
Calle Playa de Liencres, 2 bis(Entrance street Rozabella)
Parque Europa Empresarial
Edificio Madrid
28290 Las Rozas, Madrid
900 373 379 
info@u-tad.com
Center Affiliated to:
Project financed by:
Partners
