Executive Master's Degree in Big Data Science

UIC - Universitat Internacional de Catalunya. Màsters Oficials
A Barcelona (Spagna)

8.280 

Informazione importanti

  • Master
  • Barcelona (Spagna)
  • Durata:
    8 Months
  • Quando:
    Ottobre 2017
Descrizione

We are offering the opportunity to take a course focussed on the field of data analysis that will teach you the most advanced analytical techniques, the application of machine learning algorithms, the basics of applied statistics and their implementation through the modern technologies of mass and parallel processing which are reshaping the horizons for businesses and organisations through their exposure in Big Data projects.

The programme is taught in the Faculty of Economic and Social Sciences in collaboration with the MBIT School, the only professional school dedicated exclusively to business intelligence and big data training.

The Learning by Doing Method
This innovative classroom method is used to ensure students acquire the programme skills, mainly through hands-on activities. The case method is also used to analyse practices based on real cases.

Informazione importanti
Quali sono gli obiettivi della formazione?

To provide the knowledge to analyse large volumes of data from a statistical basis. To understand the new ICT architectures and technologies of mass and parallel processing used in Big Data analysis.

È la formazione giusta per me?

The Executive Master's Programme in Business Intelligence and Big Data is perfect for you if you're an IT professional (or work in another industry and have excellent technological skills) with at least 3 to 5 years' professional experience.

Professionals who deal with data analysis; business analysts from different areas such as marketing, finance, management control, human resources, risk analysis, actuaries, etc., or professionals with comparable knowledge who wish to focus on data analysis, particularly through techniques provided by Big Data.

Requisiti: To get the most out of classes, students should have some experience with the following skills: - Data analysis and traditional statistical methods. - Basic knowledge of programming and algorithms, preferably Java. - Knowledge of databases: Relational databases (SQL), administration of databases and operating systems.

Sedi

Dove e quando

Inizio Luogo Orario
Ott-2017
Barcelona
Barcelona, Spagna
Visualizza mappa
Fridays from 4pm to 9pm and Saturdays from 9am to 2 pm

Cosa impari in questo corso?

Continuous Improvement
Big data in organizations
Business intelligence in organizations
BI solutions
Tools and suppliers
Legal regulations
Architectures of information systems
Informational architectures for BI
Big data architectures
Launching BI/big data projects
Executing BI/big data projects
Knowledge extraction
Analysis of structured information
Analysis of unstructured information
Advanced reporting

Programma

Course programme

Module 1. The Big Data Environment and Business Intelligence
  1. The requirements for data analysis as a business tool
  2. New paradigms of data insights
  3. Unstructured sources of information. Big data and data analysis
  4. Introduction to Data Science
  5. Differences with respect to traditional statistical analysis
  6. Business models and data analysis applications
  7. Data Management: DM BoK
  8. Laboratory
  9. Introduction to R and Python
  10. Presentation of the datasets for the course
  11. Workshop
Module 2. Descriptive statistics
  1. Differences between traditional statistical analysis  and machine learning
  2. Descriptive statistics
  3. Statistical Analysis
  4. Descriptive statistical analysis
  5. Probability and inference
  6. Statistical tools
  7. R, Knime, SAS
  8. Regression models
  9. Simple linear
  10. Logistic
  11. Polynomial
  12. Statistical Inference
  13. Estimation
  14. Contrast of Hypotheses
  15. R programming language
  16. Visualisation with R
  17. Statistical inference with R
  18. Multivariate statistics
  19. Principal Components Analysis (PCA)
  20. Correspondence Analysis (CA)
  21. Multidimensional Scaling (MDS)
  22. Workshop
Module 3. Machine Learning
  1. Data distribution
  2. Evaluation metrics
  3. Contrast of hypothesis and statistical validation models
  4. Supervised learning techniques
  5. Decision trees, Random forests
  6. Bayesian models
  7. Support vector machines
  8. Neural networks
  9. Unsupervised learning techniques
  10. Clustering
  11. Recommendation systems
  12. Genetic algorithms
  13. Association Rules
  14. Semantic analysis and natural language processing
  15. Approximation and data stream mining algorithms
  16. Techniques for hashing, min-hash, LSH and bloom filters
  17. Workshop
Module 4. Integrated environment with KNIME
  1. Statistics and visualisation with KNIME
  2. Machine Learning with KNIME
  3. Workshop
Module 5. Open Data
  1. Bases for Open Data
  2. Infographics workshop
  3. Regulatory environment
  4. Master Class: Digital legal case
Module 6.  SQL and NoSQL databases
  1. SQL
  2. Workshop with SQL DB Manager
  3. Document databases: MongoDB
  4. Graph databases: Neo4J
  5. Column-oriented databases: HBase, Cassandra
Module 7. Business Intelligence projects
  1. What is Business Intelligence?
  2. BI tools
  3. ETL tools
  4. Data Warehouse
  5. Data Marts
  6. Reporting
  7. Reporting in BI, dashboards for prediction or support
  8. Dimensionality reduction techniques (PCA, ICA; SVD)
Module 8. Big Data Environment and Architectures
  1. Introduction to Big Data technological architectures
  2. Hadoop
  3. HDFS storage
  4. Introduction to MapReduce
  5. Hadoop Ecosystem:  Flume, Sqoop, Pig, Hive
  6. Spark
  7. Spark SQL: Processing structured data
  8. Spark Streaming: Processing in real-time
  9. Spark Mllib: Machine Learning