Solve problems together with high performance students with diverse backgrounds in science, data analysis, engineering, mathematics and more. Create meaningful connections, meet potential employers and join a community of lifelong learners.
Concepts, platforms and techniques in the course.
- Programming: R, Python
- Data visualization: ggplot2, seaborn, matplotlib
- Inferential statistics,
- Probability distributions,
- Regression analysis
- Classification algorithms
- Grouping and recommendation.
- Communication skills: they are essential to adequately explain and visualize everything that was learned before.
- Data laboratories
- Final project
Fundamentals of Data Science: Python and statistics
Students are directly incorporated into a Python-based curriculum where we explore and learn best practices in statistical analysis, including frequentist and Bayesian methods. By using software engineering best practices and programming in pairs with peers from different backgrounds, students master the fundamental concepts of data science.
- Installing our work tool
- An introduction to predictive analysis and Machine Learning
- Data Cleaning
Machine Learning and real case studies
In the second block we began to immerse ourselves in machine learning, working on real problems of classification, regression and grouping using structured and unstructured datasets. We will discover libraries like scikit-learn, NumPy and SciPy, and use real case studies to integrate our understanding of these libraries into real-world applications.
- Data handling operations
- Basic concepts of statistics for predictive modeling
- Linear regression with Python
- Logistic regression with Python
- Clustering and classification
- Random trees and forests
Natural language processing and data visualization
In our third block, we add natural language processing and recommendation systems to our body of knowledge of data science. We learn the processing of open source big data, and finish the Block with perfecting the art of visualization and knowledge of data. At the end of this Block, students must be well versed in conceptual knowledge and ready to embark on independent projects.
- Vector Support Machines
- K Nearest Neighbors
- Recommendation systems
- Principal component analysis
- Introduction to neural networks and deep learning with TensorFlow
- Join R and Python code with the rpy2 library
Capstone project and preparation for the labor market
To complete our immersion program, students work independently on an applied data science project that is unique to their interests or career aspirations in a Capstone project. These projects reflect the set of technical skills that students have learned throughout the course and demonstrate their competence and aptitude as real data scientists.
By 2020, an estimated 1 million new digital and technological jobs in Europe.
The profile of data Science will be one of the most relevant for the productivity of the companies, giving the necessary information to these to be able to have advantage over the competitors.