Other projects

Data Analyst Nanodegree @Udacity:

• Projects:

  • Project 6: Data Visualization ^(NEW)

    _{Data visualization: D3.js graph showing the relationship between health expenditure and life expectancy at birth for countries in each continent.}

    _{Skills: D3.js, JavaScript, HTML, CSS, storytelling}

  • Project 5: Intro to Machine Learning

    _{Machine learning: detect people involved in the ENRON scandal from e-mail data.}

    _{Skills: python, sklearn, Gaussian Naive Bayes}

  • Project 4: Explore and Summarize Data

    _{Statistical analysis: Analysis of features influencing Red Wine quality}

    _{Skills: ggplot, Ggally, gridExtra, data.table, R, rmarkdown}

  • Project 3: OpenStreetMap Data Wrangling with SQL

    _{Relational Databases: Cleaning of OpenStreetMap data for the Málaga area using data munging techniques, such as assessing the quality of the data for validity, accuracy, completeness, consistency and uniformity using a SQL data schema.}

    _{Skills: SQL, xml, OpenStreetMap}

  • Project 2: Investigate a dataset

    _{Statistical analysis: Investigate a dataset using in Python and perform chi-squared test for independence to draw conclusions using the module stats from scipy.}

    _{Skills: chi-squared test, pandas, numpy, stats, scipy, Jupyter-notebook}

  • Project 1: Test a Perceptual Phenomenon

    _{Statistical analysis: Use descriptive statistics and a statistical test to analyze the Stroop effect, a classic result of experimental psychology.}

    _{Skills: t-test, ggplot, R, rmarkdown}

Machine Learning Engineer Nanodegree @Udacity:

• Capstone project: Right Whale call recognition using Convolutional Neural Networks

  _{Recognition of Right Whale up-call using Convolutional Neural Networks (ConvNets) models implemented on Tensorflow.}

• Projects:

  • Project 4 : Smartcab train

    _{Reinforcement Learning: Training of a smartcab to drive following traffic rules in a populated environment.}

  • Project 3 : Creating customer segments

    _{Unsupervised Learning: Analysis of a dataset containing data on various customers’ annual spending amounts (reported in monetary units) of diverse product categories for internal structure.}

    _{Description of the different types of customers that a wholesale distributor interacts with to give the distributor an insight into how to best structure their delivery service to meet the needs of each customer.}

    _{Dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/datasets/Wholesale+customers).}

    _{Skills: pandas, matplotlib, numpy, scikit-learn}

  • Project 2 : Building a Student Intervention System

    _{Supervised Learning: Analysis of a dataset of students at high school to determine whether they will or will not pass the course in order to have an intervention of the student and prevent from failing the course.}

    _{Selection and optimization of supervised learning algorithms in Python.}

    _{Skills: pandas, matplotlib, numpy, scikit-learn, Jupyter-notebook}

  • Project 1 : Predicting Boston Housing Prices

    _{Model Evaluation & Validation: Development of model for the estimation of house prices in Boston.}

    _{Skills: matplotlib, numpy, scikit-learn, Jupyter-notebook}

  • Project 0 : Titanic Survival explorations

    _{Exploration of the survival rate of the Titanic’s passengers according to their characteristics.}

    _{Skills: pandas, numpy, stats, csv, Jupyter-notebook}