Courses and training on analytics, big data, data mining, and data science

R Programming Advanced taught by Dr. Data recovery software reviews Oliva Lau (self-described R ninja at Google) – master the key concepts for writing advanced R code, emphasizing the design of functional and efficient code (repeats on May 19)

Predictive Analytics for the Enterprise: Model Development: A Practitioner’s View of Predictive Modeling Methods, Tactics, Tools And Techniques

Advancing the Analytics-Driven Organization: The Essential Soft Skills and Strategic Framework for Leading a Goal-Centered Analytics Practice

Text Mining, taught by taught by Dr. Cnet data recovery Nitin Indurkhya (author of Emerging Trends in Predictive Text Mining) and Dr. Database systems Anurag Bhardwaj (Data Scientist at QuadAnalytix, and before that at eBay) – hands-on introduction to text mining, using Python (repeats on June 9)

Geospatial Data Mapping with QGIS, taught by Nick Bearman (Senior GIS Analyst at Clear Mapping Co) – learn mapping with this popular open source GIS

Natural Language Processing (NLP), taught by Dr. Data recovery for mac Nitin Indurkhya (author of Emerging Trends in Predictive Text Mining) – an introduction to the algorithms, techniques and software used in NLP (repeats on July 14)

Persuasion Analytics and Targetting, taught by Ken Strasma (co-founder and CEO of HaystaqDNA) – learn to apply predictive modeling methods to micro-target individuals and induce them to act (repeats on Aug 25)

Logistic Regression, taught by Dr. Data recovery damaged hard drive James Hardin (Research Associate Professor at the Univ of South Carolina and co-author of Generalized Estimating Equations) – modeling binary outcome data (repeats on Sept 1)

Bayesian Statistics in R, taught by Peter Congdon (Research Professor in Quantitative Geography and Health Statistics at Queen Mary University of London, the author of Bayesian Statistical Modeling and Applied Bayesian Modeling) – learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data (repeats on Sept 22)

Big Data Computing with Hadoop, taught by Marck Vaismann (lead data scientist at Booz Allen) – analytics professionals will be introduced to Hadoop and Spark, and provided with an exemplar workflow for using Hadoop. Database builder They also will be introduced to writing Spark and MapReduce jobs, and leveraging Hadoop Streaming to conclude work in an analytics programming language such as Python.(repeats on Oct 27)

Big Data Ingestion – How to use RESTful APIs, taught by Sarah Kelley (Knowledge Engineer and Scrum Master at Carney Labs) – learn how to write Python code to ingest data from and communicate with RESTful API’s on the web

Anomaly Detection, taught by Nitin Indurkhya (author of Emerging Trends in Predictive Text Mining) – learn how to examine data with the goal of detecting anomalies or abnormal instances (repeats on Oct 20)

Advancing the Analytics-Driven Organization: The Essential Soft Skills and Strategic Framework for Leading a Goal-Centered Analytics Practice

Predictive Analytics for the Enterprise: Model Development: A Practitioner’s View of Predictive Modeling Methods, Tactics, Tools And Techniques

Predictive Analytics for the Enterprise: Model Development: A Practitioner’s View of Predictive Modeling Methods, Tactics, Tools And Techniques

Advancing the Analytics-Driven Organization: The Essential Soft Skills and Strategic Framework for Leading a Goal-Centered Analytics Practice

Natural Language Processing using NLTK, taught by Nitin Indurkhya (author of Emerging Trends in Predictive Text Mining) – use Python and NLTK to perform natural language processing on medium size text corpora.

Deep Learning, taught by Dr. Data recovery cnet Alan Blair and Dr. Database log horizon Nitin Indurkhya – understand the basic concepts underlying the representations and methods in deep learning and see some applications where deep learning is most effective.

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