Advanced Data Science

• Lasso Regression
• Ridge Regression
• Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques Lasso and Ridge techniques are discussed in this module.

Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros, and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

• User-based Collaborative Filtering
• A measure of distance/similarity between users
• Driver for Recommendation
• Computation Reduction Techniques
• Search based methods/Item to Item Collaborative Filtering
• SVD in recommendation
• The vulnerability of recommendation systems

Learn about improving the reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go-to techniques in ensemble techniques. The parallel and sequential approaches are taken in Bagging and Boosting methods are discussed in this module.

• Overfitting
• Underfitting
• Pruning
• Boosting
• Bagging or Bootstrap aggregating

The study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

• Definition of a network (the LinkedIn analogy)
• The measure of Node strength in a Network
• Degree centrality
• Closeness centrality
• Eigenvector centrality
• Adjacency matrix
• Betweenness centrality
• Cluster coefficient
• Introduction to Google page ranking