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Business Analytics using R and Tableau - Advanced is a four-day, instructor-led classroom course using R and Tableau. This course is ideal for participants who are familiar in building predictive models using R. The course is aimed to enhance the skill of participants in advanced analytical techniques. Topics covered are Advanced R programming, Regular Expressions and Data Structure Manipulation, Supervised learning, Ensemble Modelling using boosting and bagging. It also covers visualisation using Tableau which involves data preparation, views and filters and creating dashboards.
Prerequisites
- Completion of Business Analytics using R – Professional course or thorough working knowledge on the modelling techniques listed in above course using R
Who should attend?
- Professionals interested in advanced data analytics and visualisation techniques
- Professionals willing to advance their career in data analytics on their path to being a data scientist
- Aspirants from science or management or engineering or economics or commerce backgrounds who have thorough knowledge on supervised and unsupervised modelling like Classification, Regression and Clustering using R
Course outcomes
- Participant should be able to build complex data analytic models involving boosting and bagging
- Participant acquires sound knowledge on model selection and bias, under fitting and over fitting
- Participant should be able to add value in data analytics project by building highly accurate models like ADA boost, XGBoost.
- Participant gains good hands-on exposure to Tableau for data visualisation and be able to build dashboard and various reports.
- Certification of completion on successfully completing the course requirements
Course content
Understanding Advanced Analytics
Advanced R programming
Family of functions
Regular Expressions and Data Structure Manipulation
Data Cleaning
Data Imputation
Creating tidy data
Visualisation using Tableau
Data Preparation
Joining and Blending Data
Views and Filters
Creating Dashboards
Supervised learning
Classification techniques – SVM (Support Vector Machine), Naïve Bayes
Time Series Analysis – ARIMA Method
Unsupervised learning
Dimensionality Reduction – Principal Component Analysis, Linear Discriminant Analysis
Ensemble Modelling
Understand Bagging
Understand Boosting
Adaptive Boosting (ADA BOOST)
Gradient Tree Boosting
XGBoost
Understand, develop models on models
Duration: Four days
Training locations: Bengaluru, Delhi, Mumbai, Pune, Hyderabad, Kolkata, Chennai
Call us : +91 8800892097 (Amit Kumar) and +91 9447494118 (Lijin)
Mail us : in-fmdxtraining@kpmg.com
KPMG in India reserves the right to restrict the number of participants per batch and to cancel or postpone any batch.
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