Curriculum

Utilities: Shell and POSIX

• Pipes and directing output
• Essential utilities
  • Explore (head, tail, more, less, grep)
  • Transform (sed, awk, cut, tr, sort, join)
  • Schedule (cron, watch)
  • Visualize (gnuplot)
• Regular Expressions

Software Engineering

• Git and version control
• Data Structures
  • Dictionaries and Hash Tables
  • Trees (binary, balanced, splay, B)
  • Heaps
  • Stacks and Queues
  • Graphs and Networks
  • Sets
• Algorithms
  • Search (BFS, DFS, A*, Dijkstra's)
  • Sorting (merge, quick, heap, radix)
  • Selection
• Performance (Asymptotic Analysis, hardware restrictions, indexing, etc.)

Acquire

• HTTP
• APIs and ReST
• HTML and XML
• Parsing (CSS and XPath)
• Web Scraping
• PDF parsing

Statistics

• Descriptive statistics (mean, mode, variance, skew, etc.)
• Estimation (confidence intervals, bias and error, etc.)
• Correlation (covariance, goodness of fit, causation, etc.)
• Distributions (PMF, CDF, Normal, Binomial, convolution, etc.)
• Significance (Hypothesis testing, p-value, ANOVA, etc.)
• Bayesian Statistics
• Monte Carlo Methods

Transform

• Sampling
• Feature Preparation
  • Vectorization (binning, bag of words, tf-idf)
  • Selection (automatic and manual)
  • Normalization
  • Regularization and Smoothing
• Natural Language Processing
  • N-grams
  • Tokenization
  • Sentiment Analysis
  • Information Retrieval

Store

• SQL
• NoSQL (document, graph, key-value)
• Filesystem and Text

Visualize and Present

• Grammer of Graphics (ggplot2, Bokeh)
• Interactivity
• Geographic display

Data at Scale

• MapReduce paradigm (Hadoop)
• Distributed Datastores (HDFS, Cassandra, HBase)
• Hadoop Ecosysytem (Pig, Hive, HBase, Flume, Sqoop, etc.)
• Real-Time (Spark, Storm, Shark)
• Distributed Machine Learning

Machine Learning

• Unsupervised
  • Clustering (K-means, Hierachical, etc.)
  • Association Analysis (FP-Growth, MDS, etc.)
  • Dimensionality Reduction (PCA, SVD, etc.)
• Supervised
  • Classification (Naive Bayes, kNN, Logistic Regression, etc.)
  • Regression (Linear, Polynomial, Tree, etc.)
• Recommendation
  • Similarity metrics (Jaccard, Pearson, Euclidean, etc.)
  • Item vs. User vs. Content based
  • Limitations (Cold-start problem, preference collection, performance)
• Optimization (cost functions, hill climbing, simulated annealing, etc.)
• Anomaly Detection and timeseries
• Evaluation
  • Cross Validation
  • ROC plot
  • Bias vs. Variance
  • Recall vs. Precision
  • Bootstrap