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Algorithms for Data Analytics

Algorithms for Data Analytics. B.Ramamurthy. Data Analytics (Data Science). Intuition/ understanding. Big-data analytics StatsAlgs. Data. *. Discoveries/ intelligence. Statistical Inference. EDA. Decisions/ Answers/ Results. *. Three T ypes of Data Science Algorithms.

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Algorithms for Data Analytics

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  1. Algorithms for Data Analytics B.Ramamurthy

  2. Data Analytics (Data Science) Intuition/ understanding Big-data analytics StatsAlgs Data * Discoveries/ intelligence Statistical Inference EDA Decisions/ Answers/ Results *

  3. Three Types of Data Science Algorithms • Pipelines to prepare data • Three types: • Data preparation algorithms such as sorting, workflows • Optimization algorithms stochastic gradient descent, least squares… • Machine learning algorithms…

  4. Machine Learning Algorithms • Comes from Artificial Intelligence • No underlying generative process • Build to predict or classify something • Three basic algorithms: • linear regression, k-nn, k-means • We already looked at linear regression as a case study for R/Rstudio • We will start with k-means…

  5. K-means • K-means is unsupervised: no prior knowledge of the “right answer” • Goal of the algorithm Is to determine the definition of the right answer by finding clusters of data • Kind of satisfaction survey data, incident report data, • Assume data {age, gender, income, state, household, size}, your goal is to segment the users. • K-means is the simplest of the clustering algorithms. • Lets understand kmeans using an example.

  6. Lets examine an example • {Age, income range, education, skills, social, paid work} • Lets take just the age { 23, 25, 24, 23, 21, 31, 32, 30,31, 30, 37, 35, 38, 37, 39, 42, 43, 45, 43, 45} • Classify this data using K-means • Lets assume K = 3 or 3 groups • Give me a guess of the centroids? Lets assume initial value of centroids to {21, 30, 40} • First lets hand calculate and then use R-Studio

  7. K-NN • Supervised ML • You know the “right answers” or at least data that is “labeled”: training set • Set of objects have been classified or labeled (training set) • Another set of objects are yet to be labeled or classified (test set) • Your goal is to automate the processes of labeling the test set. • Intuition behind k-NN is to consider most similar items --- similarity defined by their attributes, look at the existing label and assign the object a label.

  8. Lets look at an example

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