Master in Data Science & Big Data

Subjects

200h

Data Science Fundamentals

Introduction to fundamental concepts of Data Science.
Presentation of the general frame of reference.

Statistics for Data Science

Fundamentals of statistics necessary to master data science. Descriptive statistics, hypothesis contrasting, etc.

Data Science with Python

Python as a framework for data science specialist. Notebook development, use of pandas, numpy, matplotlib. Data processing from structured (CSV, REST, HSQL, Logs) and unstructured (Web, Spark, Cassandra) sources.

Data Pre-processing

Proper data pre-processing. Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.

Data Visualization

How to visualize different types of data, which techniques to use? Use of Matplotlib, Bokeh and Seaborn among others.

Advanced Data Processing

Data source processing. Batch and streaming processing architectures. Databases (structured and unstructured).

Predictive Analytics

Time series analysis, review of the best algorithms. Development of use cases for anomaly detection and series prediction.

Machine Learning I

Introduction to classification and clustering problems. How to evaluate the results? How to build the datasets? Review of the main algorithms and their application.

Big Data Fundamentals

Analysis of architectures and adoption models with current technologies, including data ingestion, analysis and visualization processes.

Entrepreneurship I

Global perspective of the process of creation, financing and possible success of a startup. Tools for entrepreneurship projects.

Subjects

Master in Data Science & Big Data

200h

Data Science Fundamentals

Introduction to fundamental concepts of Data Science.
Presentation of the general frame of reference.

Statistics for Data Science

Fundamentals of statistics necessary to master data science. Descriptive statistics, hypothesis contrasting, etc.

Data Science with Python

Data Pre-processing

Proper data pre-processing. Application of filters, data anonymization, attribute selection, sampling and dimensionality reduction.

Data Visualization

How to visualize different types of data, which techniques to use? Use of Matplotlib, Bokeh and Seaborn among others.

Advanced Data Processing

Data source processing. Batch and streaming processing architectures. Databases (structured and unstructured).

Predictive Analytics

Time series analysis, review of the best algorithms. Development of use cases for anomaly detection and series prediction.

Machine Learning I

Introduction to classification and clustering problems. How to evaluate the results? How to build the datasets? Review of the main algorithms and their application.

Big Data Fundamentals

Analysis of architectures and adoption models with current technologies, including data ingestion, analysis and visualization processes.

Entrepreneurship I

Global perspective of the process of creation, financing and possible success of a startup. Tools for entrepreneurship projects.