The Data Scientist Skills

In this section, you will find some skills that I have

image description
DeepRacer competition runner. Global League for Autonomous Vehicle Racing Powered by Machine Learning (ML)
Awarded for being a top 10 performer in the DeepRacer competition.

Skill

Machine Learning 🧠

  • ✅ Unsupervised algorithms
  • ✅ Supervised algorithms

Machine Learning and Data Mining

  • Undersampling
  • SMOTE
  • ADASYN
  • PCA & SVD
  • Gain of information and entropy
  • Forward selection
  • Backward elimination
  • Stepwise selection
  • Stability selection
  • Feature engineering
  • Feature bucketing
  • Feature hashing
  • Feature crossing
  • Distance: Euclides, Gauss, Mahalanobis
  • Unsupervised algorithms
  • Simple linkage, Complete linkage, Average linkage, Centroid linkage
  • Agglomerative type algorithms, Agglomerative Algorithms, K-Means, Canopy clustering
  • DBSCAN, OPTICS & Affinity propagation algorithms
  • Evaluation of binary problems. Cross validation
  • Evaluating Unbalanced Binary Problems
  • Evaluation of multiclass problems
  • Confusion matrix
  • ROC curves
  • SSW, SSB, Davies Bouldin, Silhouette coefficient
  • External validation
  • K-NN, Decision trees (ID3, C5.0, autopruning, boosting, regression models)

Skill

Mathematics and statistics 📊

  • ✅ R
  • ✅ Python
  • ✅ Matlab
  • ✅ SPSS
For review-specific knowledge, click on read more

Mathematics and statistics

  • Gram-Schmidt orthogonalization process
  • Linear application
  • Applications between sets
  • Linear maps between vector spaces
  • Matrix associated with a linear application
  • Kernel and image of a linear map
  • Monomorphisms and epimorphisms
  • Changes of basis in a linear map
  • Diagonalization of endomorphisms
  • Application to the calculation of powers of a matrix
  • Application to the study of dynamic systems
  • The curse of the dimension in data science
  • Principal Component Analysis (PCA): dimension reduction and feature extraction
  • Application of PCA and Singular Value Decomposition to data science
  • Matrix models in discrete time
  • Markov chains in discrete time. Diagram of states and transition probabilities
  • Time evolution of a Markov chain
  • Positive matrices and dominant eigenvalue. Stationary state distributions. Application to data science
  • Matrix
  • Eigenvector and eigenvalues
  • Norms and semi-norms of a matrix
  • Direct methods of solving systems of linear equations
  • Gaussian Elimination
  • Gauss-Jordan method
  • LU decomposition
  • Iterative methods of solving systems of equations linear
  • Iterative Jacobi method
  • Gauss-Seidel iterative method
  • Convergence and error estimation
  • Iterative method for the approximation of eigenvalues and eigenvectors: power and inverse power method
  • Power method
  • Polynomial interpolation
  • Calculation methods of the interpolating polynomial
  • Lagrange's method
  • Newton's divided difference method
  • Piecewise interpolation
  • Cubic splines
  • Derivation and numerical integration
  • Numerical derivation
  • Numerical integration
  • Interpolatory Integration Formulas
  • Monte Carlo method
  • Approximation of functions by the method of least squares
  • Scalar product, Orthogonal systems, Solution to the approximation problem, Approximation by orthogonal polynomials
  • Triangular family of polynomials, Orthogonal polynomials, Chebyshev polynomials, Legendre polynomials
  • Fourier methods, Fourier Analysis, Fourier series, Fourier integral theorem,
  • Linear regression, Quantification of the error of the linear regression, Linearization of nonlinear relationships
  • Polynomial Linear Regression, Multiple Linear Regression

Skill

Web technologies 🛡️

  • ✅ JavaScript
  • ✅ HTML
  • ✅ CSS

Skill

Back-end 🗄️

  • ✅ MySQL
  • ✅ PostgreSQL (data warehousing)
  • ✅ PHP: framework Laravel
  • ✅ Hosting

Back-end

  • Data structure, operations of the relational model (ER)
  • Relational algebra & integrity rules
  • Database design: conceptual and logical design
  • PostgreSQL database administration
  • Triggers, surrogate keys, common table expression, analytic functions, null value handling, OLTP and OLAP transactions
  • B+ trees, hash tables, bitmaps, indexes in database management systems, query optimization
  • Application level
  • Obtaining data from external networks
  • DataWarehouse: data storage, design and structure.
  • Departmental, corporate, operational data warehouse
  • Management of metadata and components of the Corporate Information Factory
  • Multidimensional structures
  • Strategy in the construction of the FIC
  • Development of the integration and transformation component
  • Data warehouse construction: departmental, corporate and operational
  • OLAP: conceptual, logical and physical design
  • Decision support systems DSS
  • Maps, data mining, self-service BI, business search systems in natural language, Big Data, Webhousing and mobile BI
  • Importance, integrity and quality of the data

Skill

Application Development 📂

  • ✅ Python
  • ✅ C

Skill

Hacking ☠️

  • ✅ TheHarvester
  • ✅ Nmap/ Zenmap
  • ✅ Acunetix
  • ✅ Nessus
  • ✅ Nikto
  • ✅ Cmsmap
  • ✅ Wpscan
  • ✅ Joomscan
  • ✅ Zap
  • ✅ Burpsuite-pro
  • ✅ Metasploit
  • ✅ Craking (online and offline with hashcat, hydram ophcrack, metasploit, etc.)
  • ✅ Wifi (aircrack, airgeddong, Dos, deauthentication, evil twin atack, fake points access and MitM)

Skill

Electronics & network architecture 🔌

  • ✅ Arduino
  • ✅ Network architecture

Electronics & network architecture

  • Protocol architecture
  • OSI and TCP/IP reference models
  • The physical layer, access to the medium, network
  • Wireless technologies and data generation
  • Data network architecture
  • Transport level
  • Application level
  • Obtaining data from external networks

Skill

Languages 🌐

  • ✅ English B2 (UOC certified)

    Key: 34805440871748ACAF10FC650D16796F

  • ✅ Spanish Native language
  • ✅ Catalan Native language