Course Template

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Course Overview

Our Predictive Analytics using R course is designed to give participants hands-on experience with building predictive models as well as the skills to identify potential application areas in their organisations.

Course Design Focuses on Practical Data Science

Apart from teaching participants predictive modelling, this course focuses on imparting a solid understanding of how to introduce data science into a business environment. This includes things such as gaining buy-in from management and developing organizational capabilities in data science and predictive analytics. The skills learnt will enable participants to help build a data science capability in their organisations in a low-cost, lean manner, using industry standard technologies.

Includes Practical Exercises

Harness the power of machine learning to build predictive models using R. Theory will be supplemented by practical exercises covering diverse domains.

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Cover the Basics

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Day 1

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  • Overview of the workshop
  • What is predictive analytics? How does it differ from“traditional analytics”?
  • What is machine learning?
  • Historical context and applications
  • Introduction to machine learning
    • Supervised and unsupervised learning
    •  Classification and regression
    • The process of machine learning (what does it mean to build a predictive model from data)
    • Training vs test-Simplicity vs flexibility
    • Bias variance tradeoff and regularisation-Evaluating model accuracy

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  • The R environment
    • RStudio
  • Basic commands and libraries
    • installing and calling
  • Simple computations and datatypes
  • Scalars, vectors and lists
  • Data frames
  • Tidydata and the tidyverse
  • Plotting data using ggplot
  • Data wrangling using dplyr
  • String manipulation
  • Dates

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Day 2

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Linear predictive models
  • Linear regression
  • Logistic regression
  • Ridge and Lasso regularization
Tree based Models
  • Decision trees
  • Random forest
  • Gradient boosted trees
Other Algorithms
  • Support vector machines
  • Naive Bayes
  • Clustering and dimensionality reduction

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An internal hackathon

[/ultimate_heading][ultimate_spacer height=”30″][vc_row_inner content_placement=”middle”][vc_column_inner width=”1/2″][vc_column_text]Sed ut perspiciatis unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam, eaque ipsa quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt explicabo. Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos qui ratione voluptatem sequi nesciunt.[/vc_column_text][ultimate_spacer height=”15″][ult_buttons btn_title=”Register” btn_size=”ubtn-large” btn_title_color=”#ffffff” btn_bg_color=”#2b479c” btn_bg_color_hover=”#2b479c” btn_title_color_hover=”#ffffff” icon=”Defaults-envelope-o” icon_size=”20″ btn_icon_pos=”ubtn-sep-icon-right-push” btn_border_style=”solid” btn_color_border=”#2b479c” btn_color_border_hover=”#2b479c” btn_border_size=”2″ btn_radius=”50″ btn_font_family=”font_family:Nunito|font_call:Nunito|variant:600″ btn_font_style=”font-weight:600;”][/vc_column_inner][vc_column_inner width=”1/2″][us_image image=”69″ size=”full” align=”center”][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row]

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