This deep dive into data science provides a balance of theory and practice through a project-based approach.

• Course Topics
  • Data sampling, measurement, and wrangling
  • Exploratory data analysis
  • Data description, visualization, and graphing
  • Bias, variance, and the bias-variance tradeoff
  • Model validation and model cross-validation
  • Hyperparameter tuning and information leakage
  • Model evaluation and comparison
  • Model weighting of costs and benefits
  • Ensemble learning and meta-learning
  • Predictive labeling and data augmentation
  • Data-driven business models
  • Big Data, Map-Reduce, and Spark
  • Virtual machines and cloud computing
  • Strategic planning for a digital transformation
  • The management of talent and strategic human capital
• Schedule

  

• Data Analysis Methods & Models
  • Normalizing and standardizing data
  • Linear and log-linear models
  • Non-parametric models, splines and locally-linear models
  • Nearest neighbor and similarity models
  • Agglomerative clustering and K-means clustering
  • Decision trees, bagging, boosting, and random forests
  • Dimension reduction, PCA, t-SNE, and manifold projections
  • Support vector machines
  • Text as data and natural language processing (NLP)
  • Word embeddings and latent topic modeling
  • Feed-forward neural networks
  • Convolutional neural networks
  • Recurrent neural networks, LSTMs, bi-lateral LSTMs
  • Generative adversarial networks
  • Reinforcement learning
• Prerequisites

  No prior training in Data Science is required, but some knowledge of Python will help you get the most out of the course. We suggest completing the 7-hour Python tutorial by Kaggle prior to the course to help you follow along with the demos and project solutions covered in class.

  For the best class experience you should:

  be familiar with linear algebra (although we use very little math)
  be familiar with statistics (although we will review the basics)
  be conversant in English (the course will be given in English)
  bring a laptop (Mac, Windows, Linux, or Chromebook)

  Several highly-qualified graduate student teaching assistants will be available to work with you one-on-one to answer questions about the programming code.