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Online Data Science Certificates: Analytics and Programming for Data Science


Statistics.com, a leading provider of online education in statistics and analytics announces two new online certificates for Data Science - "Analytics for Data Science" and "Programming for Data Science".



Statistics.com "Analytics for Data Science" certificate focuses on concepts and applications. Standard software is used, providing relatively short learning curves. Topics include predictive analytics, machine learning, forecasting, social network analysis, optimization, data visualization, data mining, statistical modeling, risk modeling, queuing, spatial analysis, recommender systems, and time-to-event analysis. This certificate can be completed in about 18 months, with flexible start times throughout the year.

"Programming for Data Science" certificate focuses on the open-source programming environments seen in analytics and Big Data applications - R, Python, Hadoop, SQL. The program includes applications to predictive modeling, social data mining and text analytics. This certificate provides exams to test programming proficiency. A typical student profile in this program might be someone working in IT who wants to expand into analytics; a student with programming experience would require about a year to complete all courses. Instruction in basic programming is available for novice programmers (in this situation, the course of study would require 2-3 years - becoming a good programmer requires lots of practice).

These certificates are part of The Institute Programs in Analytics and Statistical Studies (PASS): part-time programs for professionals seeking to add to their skill set. The courses are typically four weeks, and do not require that you be online at any particular time of day.

Statistics.com instructors are noted authorities in their field, people like:

  • Nitin Indurkhya, text mining expert who coined the term “Big Data”
  • Galit Shmueli, author of the best selling "Data Mining for Business Intelligence" and a number of other books
  • Jen Golbeck, author of "Mining the Social Web"
  • Paul Murrell, member of the core development team for R


The instructors answer student queries on a private discussion forum throughout each course. See the faculty at http://www.statistics.com/faculty/

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