With a background in Physics and as a Data Scientist I provide support in these areas: • Data Science: Importing and Cleaning Data. SQL and MySQL. Machine Learning in R and Python: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression, Logistic Regression, K-NN, SVMs, Naive Bayes, Decision Tree Classification, Random Forest Classification, K-Means, Hierarchical Clustering, Reinforcement Learning with Q-Learning and Transfer Learning, LSTM, NLP, Deep Learning with ANNs and CNNs, and Deep Belief Network, Dimensionality Reduction, RBMs and Autoencoders. Big Data Analysis with Apache Spark. • CRAN, Numpy, Pandas, Scikit-Learn, NeuroLab, PyBrain, Tesseract, Caffe, Theano, Keras and TensorFlow. • Computational Physics: Finite Difference Method, Numerical Differentiation and Integration, Monte Carlo Techniques (Ising Model, Critical Exponents, Binder cumulants, Finite Size Scaling and Lennard-Jones fluids), Matrix Computation, Searching and Fitting. ODEs and PDEs Applications, High-Performance Computing, Fourier Transforms, Molecular Dynamics (Verlet Algorithms, Periodic Boundary Conditions, Phase Transitions, Brownian Dynamics, Eigenvalues and Eigenstates in Quantum Mechanics. • Astrophysics, Optics, Electromagnetism, CFD, Classical and Quantum Mechanics. • Phyton, R, C++, Fortran, Matlab, Processing, SQL and MySQL. • Finite Element Analysis packages: Abaqus, CST Studio, Solidworks, Comsol • 3D modelling, animation and rendering. Motion Graphics in Cinema 4D, After Effects, OpenGL, GLSL, OpenFrameworks, Max/Msp and Pure Data. Here are some of my certifications: - Deep Learning with TensorFlow by IBM Cognitive Class: - Analyzing Big Data in R using Apache Spark by IBM Cognitive Class: - Cleaning Data in R by DataCamp: Best regards!