Introduction to Data Science

DM105


4 Days

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Course Outline

More and more products use data to optimize and personalize their performance and offer to the customers. Self-learning algorithms allow quickly address issues that most non-hi-tech companies weren’t aware exist at all. Seems like every company – big or small, start-up venture or established corporate – everyone must stay up-to-date in all related to

data-related techniques. This course is an introductory level course to machine learning and data science. It will carefully explain the methodology of analytical thinking. Not only will you know the algorithms, but you will also know how—and when—to start and finish your projects, or which ones are likely to succeed but only with significant extra effort.

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Modules

General
  • What is Big-Data and why is it good
  • Big-Data Characteristics & types
  • Challenges and complexity
  • Use cases in today's world
Methodology
  • Defining the business problem or opportunity
  • Defining the business objective
  • Designing requirements
  • Understanding the relationship between causes and consequences
  • Setting up the environment and exploring the data
  • Supervised vs. unsupervised vs. reinforcement learning
  • Supervised learning: classification vs. regression
  • Overview of model building steps: Data preparation, Model building,Model validation, Model assessment, Model implementation
Supervised learned model R-regression
  • Targeting and scoring models
  • What you need to develop a scoring model
  • Checking model assumptions: Linearity, Normality, Equal variance
  • Definition of predicted variables
  • Calibration data and statistical model
  • Building a predictive model in R–Concept on the basis of Churn modelfor cell-phone users.
  • Overview of model building steps
Classification models
  • Explaining the cluster analyses
  • Visualizing the model output
  • Evaluating the models
  • Statistical segmentation
  • Segment Strategies
  • Selecting the "right" number of segments
  • Segmentation variables
  • Archetypical profiles
  • Running a hierarchical segmentation in R
Unsupervised Learning
  • Unsupervised Learning and Principal Components Analysis
  • Exploring Principal Components Analysis and Proportion of VarianceExplained
  • K-means Clustering
  • Hierarchical Clustering
  • Examples of case studies

Prerequisites

  • Soft skills in programing and statistics
  • Basic knowledge of SQL, Excel and any analytical experience helps

Upcoming Meetings

Not only will you know the algorithms, but you will also know how—and when—to start and finish your projects”
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Target Audience