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Applied Business Analytics course is an intensive instructor-led four day course which is designed for students keen to advance their knowledge on data analytics. The dynamic curriculum in applied business analytics helps the participant to understand and apply analytics in the business context. Topics covered are fundamentals of R programing, multiple linear regression, Binary logistic regression, Decision tree, Random forest, K means clustering, sentiment analysis in text analytics and social media analytics. Business cases from marketing analytics, HR analytics, financial analytics and operation analytics are covered as part of this course.

Prerequisites

  • Basic awareness of analytics and statistics

Who Should Attend?

  • Students who are interested in advanced data analytics techniques
  • Aspirants from science, management, engineering, economics or commerce background who wish to pursue a career in data analytics

Course Outcomes

  • Advanced working knowledge in data analytics
  • Ability to identify and build predictive models suitable to solve business scenarios and derive insights
  • Certification of completion on successfully completing the course requirements

Course Content

Understanding business analytics

Importance of data in business

Framework of business analytics

Basics of R programming

Basics of R

Introduction to R libraries

Data structures and data types in R

Operators, control structures and functions in R

Data cleaning

Reading data

Cleaning and preprocessing data

Modelling techniques

2) Supervised learning

Classification techniques – decision tree and random forest

Regression – Simple, multiple and logistic regression

K Nearest Neighbors (KNN)

3) Unsupervised learning

Clustering techniques – K means clustering

Association Rule Learning - Apriori

Text analytics

Social media analysis.

Duration: 4 days

Training locations: Dedicated batches in college campuses. Contact us for details.

Enroll now

Call us : +91 9061 342 432 (Raynette Furtado)

Mail us : in-fmdxtraining@kpmg.com

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