Data Science Training Course

Project 1 : Augmenting retail sales with Data Science

Industry :  Retail

Problem Statement : How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data.

Topics : In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.

Highlights:

• Association rules for transaction data
• Association mining with Apriori algorithm
• Generating rules and identifying patterns.

Project 2 : Increasing revenues of a retail enterprise using Data Science tools

Industry :  Retail

Problem Statement : How to successfully convert visitors into buyers for retail enterprise

Topics : In this Data Science project you will learn how to reduce the churn rate, recommend the right items for the customers for the ecommerce website and place the right products in the physical store. You will learn how to optimize the limited budget for marketing using the right Data Science techniques.

Highlights:

• Implementing data cleansing methods
• Logistic regression & K-Means Clustering
• Deploying Market Basket Analysis.

Project 3 : Analyzing pre-paid model of stock broking

Industry : Finance

Problem Statement: Finding out the deciding factor for people to opt for the pre-paid model of stock broking.

Topics:In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.

Highlights:

• Deploying the rules of statistical analysis
• Implementing data visualization
• Linear regression for predictive modeling.

Project 4 : Cold Start Problem in Data Science

Industry: Ecommerce

Problem Statement:  how to build a recommender system without the historical data available

Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the various ways of working with algorithms and deploying other data science techniques.

Highlight:

• Algorithms for Recommender
• Ways of Recommendation
• Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
• Complete mastery in working with the Cold Start Problem.

Project 5 : Recommendation for Movie, Summary

Topics : This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provide data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

• Recommendation for movie
• Two Types of Predictions – Rating Prediction, Item Prediction
• Important Approaches: Memory Based and Model-Based
• Knowing User Based Methods in K-Nearest Neighbor
• Understanding Item Based Method
• Matrix Factorization
• Decomposition of Singular Value
• Data Science Project discussion
• Collaboration Filtering
• Business Variables Overview

Project 6 :  Making sense of customer online buying pattern

Industry :  ecommerce

Problem Statement :  An ecommerce company wants to know how to deploy targeted selling to its customers

Description :  This Data Science project involves learning from the customer buying habits and selling them the products that they need. As part of the project you will aggregate, cleanse, transform and load the data of customer buying history. Then you will deploy statistical analysis, predictive modeling and create profiles of customers to implement targeted selling to them.

Highlights :

• Decision Tree for classification of customers
• R computing for statistical analysis
• Techniques for predictive modeling.

Project 7 :  Fraud Detection in Banking System

Industry :  Banking and Finance

Problem Statement :  A major bank wants to deploy data science to detect fraudulent activities and take remedial actions before it is too late

Description : This data science project will help you understand how you can look for fraudulent activities in a banking ecosystem. You will work with banking transactional data, look for outliers in the data, classify this data based on various parameters, apply statistics and come up with inferences to look for rogue transactions and mitigate the risk before it is too late.

Highlights :

• Data aggregation and analysis
• K-Means Clustering using R program
• Visualizing the data for inferences

Case Study

The Market Basket Analysis (MBA) case study

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.