dturbay2 / gads9-nyc-spring2014 Goto Github PK

Course repository for the Spring 2014 Data Science course at General Assembly, NYC.

Note: The specific topics and pacing will be adjusted to best fit the needs of the students

####Introduction to Data Exploration

• Describe the data mining workflow and the key traits of a successful data scientist.
• Extract, format, and preprocess data using UNIX command-line tools.
• Explore & visualize data using R and ggplot2.

####Introduction to Machine Learning

• Explain the concepts and applications of supervised & unsupervised learning techniques.
• Describe categorical and continuous feature spaces, including examples and techniques for each.
• Discuss the purpose of machine learning and the interpretation of predictive modeling results.

###Unit 2: Fundamental Modeling Techniques

####K-Nearest Neighbors Classification

• Describe the setting and goal of a classification task.
• Minimize prediction error using training & test sets, optimize predictive performance using cross-validation.
• Understand the kNN classification algorithm, its intuition and implementation.
• Implement the "hello world" of machine learning (kNN classification of iris dataset).

####Naive Bayes Classification

• Outline the basic principles of probability, including conditional probability and Bayes� theorem.
• Describe inference in the Bayesian setting, including the prior and posterior distributions and the likelihood function.
• Understand the naive Bayes classifier and its assumptions.
• Implement a spam filter using the naive Bayes technique.

####Regression & Regularization

• Explain the concepts of regression models, including their assumptions and applications.
• Discuss the motivation for regularization techniques and their use.
• Implement a regularized fit.

####Logistic Regression

• Describe the applications of logistic regression to classification problems and probability estimation.
• Introduce the concepts underlying logistic regression, including its relation to other regression models.
• Predict the probability of a user action on a website using logistic regression.

###Unit 3: Further Modeling Techniques

####K-Means Clustering with Python

• Introduce Python and its usefulness for data analysis tasks.
• Experiment with scikit-learn, a general-purpose machine learning library for Python.

####Decision Trees & Random Forests

• Describe the use and construction of decision trees for classification tasks.
• Create a random forest model for ensemble classification.

####Dimensionality Reduction

• Explain the practical and conceptual difficulties in working with very high-dimensional data.
• Understand the application and use of dimensionality reduction techniques.
• Draw inferences from high-dimensional datasets using principal components analysis.

####Recommendation Systems

• Explain the use of recommendation systems, and discuss several familiar examples.
• Understand the underlying concepts, including collaborative & content-based filtering.
• Implement a recommendation system.

###Unit 4: Other Tools

Database Technologies

• Introduce concepts and use of relational databases, alternative database technologies such as NoSQL, and popular examples of each.

####Map-Reduce

• Describe the concepts of parallel computing and applications to problems in big data.
• Introduce the map-reduce framework.
• Implement and explore examples of map-reduce tasks