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This course helps you learn simple but powerful ways to work with data.

It is designed to be help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace.

In this course you will use R (an open-sourced, easy to use data mining tool) and practice with real life data-sets.

We focus on the application and provide you with plenty of support material for your long term learning.

It also includes a project that you can attempt when you feel confident in the skills you learn.

CURRICULUM

SECTION 1: Welcome
  • Agenda
  • Our course design
SECTION 2: Linear regression
  • What is a predictive model?
  • Step 1: Building your first model using R
  • Step 2: Use the lm function
  • Step 3: Split your data
  • Step 4: Model selection
  • Step 5: Multicollinearity
  • Predictions and quality checks
  • FAQ
SECTION 3: Logistic Regression
  • How to spot dissatisfied customers
  • The math behind it
  • Building a logistic regression using R
  • Step 1: Import your data
  • Step 2: Use the "glm" function to build a model
  • Step 3: Split your data
  • Step 4: Model selection
  • Step 5: Make your predictions
  • Step 6: Checking your model performance
  • FAQ
SECTION 4: Cluster analysis
  • Segmenting data with K-Means algorithm
  • Import your data
  • Specify number of clusters
  • Interpret your cluster output
  • FAQ
SECTION 5: Factor analysis
  • Where do we use factor analysis
  • Using R for factor analysis
  • Computing factor loadings
  • Scoring survey
  • FAQ
SECTION 6: Project
  • Elections data
SECTION 7: Advanced reading
  • How cluster analysis is at the heart of Amazon's business model
Click here for details.

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