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Introduction to Data Science, Machine Learning & AI using R

Monday, September 14, 2020 - 9:00AM to
Friday, September 18, 2020 - 4:30PM EDT
Venue Address: 
Virtual / Herndon, VA
Monday, September 14, 2020 - 9:00AM to
Friday, September 18, 2020 - 4:30PM EDT
2020-09-14 09:00:00 2020-09-18 16:30:00 America/New_York Introduction to Data Science, Machine Learning & AI using R See more details at: Virtual / Herndon, VA, , ,
Venue Address: 
Virtual / Herndon, VA
ACT-IAC Member Pricing

IAC Member $1,950.00     
Government $1,850.00
*See registration instructions

Event Details


Registration Instructions:
  • Click Register and select Reserve your Seat for the desired date
  • The course will be added to your shopping cart Click “Continue” to checkout
  • Enter the required information and select “Prepaid Company Voucher” and “Continue”
  • Enter appropriate voucher #

Government Members: V-U22215
Industry Members: V-U22216

  • You will receive a confirmation number and email from Learning Tree International who will follow-up for payment
  • Any questions on registration, please contact Amy at [email protected] or Andrew at [email protected]

If you want to become a data scientist, this Introduction to Data Science is the course to get you started. Using open source tools, it covers all the concepts necessary to move through the entire data science pipeline, and whether you intend to continue working with open source tools, or later opt for proprietary services, it will give you the foundation you need to assess which options best suit your needs.

You Will Learn How To:

  • Translate business questions into Machine Learning problems to understand what your data is telling you
  • Explore and analyze data from the Web, Word Documents, Email, Twitter feeds, NoSQL stores, Relational Databases and more, for patterns and trends relevant to your business
  • Build Decision Tree, Logistic Regression and Naïve Bayes classifiers to make predictions about your customers’ future behaviors as well as other business critical events
  • Use K-Means and Hierarchical Clustering algorithms to more effectively segment your customer market or to discover outliers in your data
  • Discover hidden customer behaviors from Association Rules and Build Recommendation Engines based on behavioral patterns
  • Use biologically-inspired Neural Networks to learn from observational data as humans do
  • Investigate relationships and flows between people, computers and other connected entities using Social Network Analysis