data.py

data = [("big data", 100, 15), ("Hadoop", 95, 25), ("Python", 75, 50),
        ("R", 50, 40), ("machine learning", 80, 20), ("statistics", 20, 60),
        ("data science", 60, 70), ("analytics", 90, 3),
        ("team player", 85, 85), ("dynamic", 2, 90), ("synergies", 70, 0),
        ("actionable insights", 40, 30), ("think out of the box", 45, 10),
        ("self-starter", 30, 50), ("customer focus", 65, 15),
        ("thought leadership", 35, 35)]

grammar = {
    "_S": ["_NP _VP"],
    "_NP": ["_N", "_A _NP _P _A _N"],
    "_VP": ["_V", "_V _NP"],
    "_N": ["data science", "Python", "regression"],
    "_A": ["big", "linear", "logistic"],
    "_P": ["about", "near"],
    "_V": ["learns", "trains", "tests", "is"]
}

documents = [
    ["Hadoop", "Big Data", "HBase", "Java", "Spark", "Storm", "Cassandra"],
    ["NoSQL", "MongoDB", "Cassandra", "HBase", "Postgres"],
    ["Python", "scikit-learn", "scipy", "numpy", "statsmodels", "pandas"],
    ["R", "Python", "statistics", "regression", "probability"],
    ["machine learning", "regression", "decision trees", "libsvm"],
    ["Python", "R", "Java", "C++", "Haskell", "programming languages"],
    ["statistics", "probability", "mathematics", "theory"],
    ["machine learning", "scikit-learn", "Mahout", "neural networks"],
    ["neural networks", "deep learning", "Big Data", "artificial intelligence"],
    ["Hadoop", "Java", "MapReduce", "Big Data"],
    ["statistics", "R", "statsmodels"],
    ["C++", "deep learning", "artificial intelligence", "probability"],
    ["pandas", "R", "Python"],
    ["databases", "HBase", "Postgres", "MySQL", "MongoDB"],
    ["libsvm", "regression", "support vector machines"]
]