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Online Courses in Predictive Analytics, Machine Learning, Data Science from Statistics.com


Many interesting online courses covering Decision Trees, Machine Learning Tools, Python for Analytics, Social Network Analysis, Hadoop, Forecasting, Data Visualization, and more, from Statistics.com.



Word cloud for online courses at Statistics.com

Here is a schedule of upcoming courses from Statistics.com.

Jan 17 -
Feb 14

Decision Trees and Rule-Based Segmentation. (4 weeks, online) This course teaches you data mining classification/rule generation for prediction (classification and regression trees) and for recommender systems (association rules).

Jan 31–
Feb 28

July 18-
Aug 15

Predictive Analytics 1 - Machine Learning Tools.  (4 weeks, online)  Introduces the concept of predictive modeling and use of holdout samples and various metrics for model assessment; covers K-nearest-neighbor (KNN), Naive Bayes, CART, ensembles.

Feb 14 –
Mar 14


Aug 15 -
Sep 12

Introduction to Python for Analytics.  (4 weeks, online)  An introduction for those with some programming or command line scripting familiarity.  Data structures, strings, data handling, Pandas, merging, joining, visualization with matplotlib.

Feb 21 -
Mar 21


Aug 1-29

SQL and R - Introduction to Database Queries.  (4 weeks, online)  An introduction to using SQL to extract data from relational databases, and then work with it in R.  Database structure, SQL functions for selection, counting and arithmetic, subqueries, joins, and then work in R & Plyr.

Feb 28 –
Mar 29


Aug 15 -
Sep 12

Introduction to Social Network Analysis (SNA).  (4 weeks, online)  Basic terms and metrics, constructing plots, measuring tie strength and trust, content analysis, propagation, sampling and analysis, illustrations of applications. Taught Jen Golbeck, Director of the Human-Computer Interaction Lab at the University of Maryland, College Park.

Mar 7 –
Apr 4


Aug 22 –
Sep 19

Predictive Analytics 2 - Neural Nets and Regression.  (4 weeks, online)  Logistic and linear regression, discriminant analysis, neural networks.  Assumes familiarity with the modeling process and model assessment taught in Predictive Analytics 1.

Mar 7 –
Apr 4

Political Analytics.  (4 weeks, online)  Predictive modeling applied to political campaigns - "microtargeting."  The instructor, Ken Strasma, directed targeting for the Obama campaign.

Mar 28 -
Apr 25


Oct 31-
Nov 28

Introduction to Analytics using Hadoop.  (4 weeks, online)  Hands-on: set up your own Hadoop development environment.  HDFS, MapReduce, data flow, functional programming with Mappers and Reducers, Hadoop streaming.

Mar 28 -
Apr 25


Sep 12 -
Oct 10

Forecasting Analytics. (4 weeks, online) Leach how to choose an appropriate time series model, fit the model, to conduct diagnostics, and use the model for forecasting. Regression models, Moving Average, exponential smoothing, Autoregressive models.  Taught by Galit Shmueli, author of numerous books in data mining and analytics.

Apr 25 -
May 23


Oct 24 -
Nov 21

Interactive Data Visualization.  (4 weeks, online)  The focus is not on presentation, but on exploration and analysis.  Time series, scatterplots, trellis plots, parallel coordinate plots, treemaps.  Choose different variables, different plots, and different filters all on the same dataset to gain knowledge.

Jun 6 -
Jul 4

Text Mining. (4 weeks, online)  This course extends data mining's standard predictive methods to unstructured text.  Tokenizations, dictionary creation, vector generation, feature generation.

Jun 28
- Jul 26

Data Mining in R. (4 weeks, online)  This course uses cases to teach you how to use R (with which you should be familiar) for predictive modeling. Taught by Luis Torgo, author of "Data Mining with R."

Jul 19
- Aug 16

Natural Language Processing. (4 weeks, online)  This course teaches the concepts and techniques of NLP - text preprocessing, corpus creation, lexical analysis, tagging, parsing, semantic analysis. Taught by author Nitin Indurkhya, who first coined the term Big Data in 1998.

Aug 29
- Sep 26

Sentiment Analysis. (3 weeks, online)  Sentiment Analysis refers to the process of identifying, extracting and classifying opinions in text segments (used in CRM, online advertising and brand analysis). Taught by author Nitin Indurkhya, who first coined the term Big Data in 1998.

Sep 26 -
Oct 24

Bayesian Statistics in R   (4 weeks, online)   Learn how to run Bayesian regression models in R - linear, linear regression, poisson, logit and negative binomial regression, and ordinal regression.  Use JAGS and R-INLA.   Taught by Peter Congdon, author of several books on Bayesian computing.

Oct 10 -
Dec 7

Data Mining: Unsupervised Techniques  (4 weeks, online) This course covers Principal Components Analysis, hierarchical and k-means clustering, association rules, and the integration of unsupervised methods into predictive modeling.

Oct 31 -
Nov 28

Cluster Analysis  (4 weeks, online) This course is an in-depth look at hierarchical clustering (divisive vs. agglomerative), k-means clustering, Normal-mixture models, and two-step clustering.  Taught by Anthony Babinec, president of AB Analytics.

Nov 21
- Dec 19

Risk Simulation and Queuing. (4 weeks, online)   This course covers simulation to analyze risk, poisson arrival rate simulation, queuing, decision analysis (decision tree, payoff matrix, expected value of information, tornado charts.  Taught by Cliff Ragsdale, author of "Spreadsheet Modeling and Decision Analysis: A Practical Introduction - Management Science"