Business Analytics with Python

Live Online (VILT) & Classroom Corporate Training Course

Business Analytics using Python is a five-day instructor-led course. The course covers from basic level to advanced topics carefully designed to make it ideal for participants with or without prior experience in Python programming and data analytics.
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Overview

Business Analytics using Python is a five-day instructor-led course. The course covers from basic level to advanced topics carefully designed to make it ideal for participants 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.

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Objectives

At the end of Business Analytics with Python training course, participants will be able to

  • Gain working knowledge in machine learning with hands-on Python experience
  • Learn skills to build machine learning models in data analytics
  • Develop skills required to build data models using supervised and unsupervised methods
  • Gain insights on deriving hidden information from voluminous and complex data
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Prerequisites

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

  • 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
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