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Business Analytics using Python is a five-day instructor-led classroom course. The course graduates from basic level to advanced topics carefully designed to make it ideal for candidates with or without prior experience in Python programming and data analytics. Topics covered include the basics of Python programming, supervised learning methods which will cover linear and nonlinear techniques, decision tree, k nearest neighbour, support vector machine and clustering.

Pre-requisites

  • An interest in and flair for numbers
  • Willingness to learn statistics
  • Awareness on the basics of any programming language

Who Should Attend?

  • Working professionals who are interested in upskilling or reskilling in the area of machine learning
  • Aspirants from Science, management, engineering, economics or commerce background who wish to pursue a career in machine learning
  • Professionals in finance, marketing, sales, HR, production, quality and operations who wish to apply data analytics skills in their current jobs to derive quantitative insights

You do not need any prior experience in data analysis to attend this course. Awareness of programming is required to participate in this course, which will make the learning process faster. The instructors hand hold participants through the fundamentals of Python scripting and introduce them to the world of analytics. The aim of the course is for participants to have a thorough grasp of advanced machine learning concepts.

Course outcomes

  • Working knowledge in machine learning with hands-on Python experience
  • Skills to build machine learning models in data analytics
  • Skills required to build data models using supervised and unsupervised methods
  • Insights on deriving hidden information from voluminous and complex data
  • Certification of completion on successfully completing the course requirements

Course content

Understanding Data Analytics

Importance of data in business

Data analytics ecosystem

Basis of Python programming

Basics of Python

Variables and Operators

Data types

Lists, Dictionary and Functions

Programming in Python

Introduction to Machine learning

Python Libraries

Numpy

Scikit

Pandas

Matplot lib

Data Visualisation

Supervised learning

Linear Regression

Logistic Regression

Decision Tree

Naive Bayes

K Nearest Neighbor

Random Forest

Dimensionality Reduction

Gradient Boosting algorithms

Support Vector Machine

Unsupervised learning

Clustering techniques – K means clustering 

 

Duration: Five days

Training locations: Bengaluru, Delhi, Mumbai, Pune, Hyderabad, Kolkata, Chennai

Enroll Now

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

Mail us : in-fmdxtraining@kpmg.com

Month Date Location
August 2018 20 - 23 Bengaluru

KPMG in India reserves the right to restrict the number of participants per batch and cancel or postpone any batch.

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