Data Science, R, Mahout, SAS Training – Combo Course

Classification and Recommendation, Clustering in Mahout, Pattern Mining, Understanding machine Learning, Using Model diagram to decide the approach, Data flow, Supervised and Unsupervised learning

Concept of Recommendation, Recommendations by E-commerce site, Comparison between User Recommendations and Item recommendation, Define recommenders and Classifiers, Process of Collaborative Filtering, Explaining Pearson coefficient algorithm, Euclidean distance measure, Implementing a recommender using map reduce

Defining Clustering, User-to-user similarity, Clustering Illustration, Euclidean distance measure, Distance measure vector, Understanding the process of Clustering, Vectorizing documents-Unstructured data

Document clustering, Sequence-to-sparse Utility, K-Mean Clustering

Terminology, Predictor and Target variable, Classifiable DataKey Challenges in Classification algorithm, Vectorizing Continuous data, Classification Examples, Logic Regression and its examples

Clustering, Clustering Process, Transaction Clustering, Different techniques of Vectorization, Distance measure, Clustering algorithm-K-MEAN, Clustering Application-1, Clustering Application-2, Sentiment Analyzer

Pearson Coefficient, Collaborative Filtering Process, Collaborative Filtering, Similarity Algorithms, Pearson Correlation, Euclidean Distance Measure -Frequent Pattern & Association rules, Frequent Pattern Growth

Introduction to Base SAS, Installation of SAS tool, Getting started with SAS, various SAS Windows – Log, Explorer, Output, Search, Editor, etc. working with data sets, overview of SAS Functions, Library Types and programming files

Import/Export Raw Data files, reading and sub setting the data set, various statements like WHERE, SET, Merge

Hands-on Exercise – Import Excel file in workspace, Read data, Export the workspace to save data

Various SAS Operators – Arithmetic, Logical, Comparison, various SAS Functions – NUMERIC, CHARACTER, IS NULL, CONTAINS, LIKE, Input/Put, Date/Time, Conditional Statements (Do While, Do Until, If, Else)

Hands-on Exercise – Apply logical, arithmetic operators and SAS functions to perform operations

Understanding about Input Buffer, PDV (Backend), learning what is Missover

Defining and Using KEEP and DROP statements, apply these statements, Format and Labels in SAS.

Hands-on Exercise – Use KEEP and DROP statements

Understanding Delimiter, dataline rules, DLM, Delimiter DSD, raw data files and execution, list input for standard data.

Hands-on Exercise – Use delimiter rules on raw data files

The various SAS standard Procedures built-in for popular programs – PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, , PROC CORR etc.

Hands-on Exercise – Use SORT, FREQ, SUMMARY, EXPORT and other procedures

Reading standard and non-standard numeric inputs with Formatted inputs, Column Pointer Controls, Controlling while a record loads, Line pointer control / Absolute line pointer control, Single Trailing , Multiple IN and OUT statements, DATA LINES statement and rules, List Input Method, comparing Single Trailing and Double Trailing.

Hands-on Exercise – Read standard and non-standard numeric inputs with Formatted inputs, Control while a record loads, Control a Line pointer, Write Multiple IN and OUT statements

SAS FORMAT statements – standard and user-written, associating a format with a variable, working with SAS FORMAT, deploying it on PROC Data sets, comparing ATTRIB and FORMAT statements.

Hands-on Exercise – Format a variable, deploy format rule on PROC DATA set, Use ATTRIB statement

Understanding PROC GCHART, various Graphs, Bar Charts – Pie, Bar, 3D, plotting variables with PROC GPLOT.

Hands-on Exercise – Plot graphs using PROC GPLOT Display charts using PROC GCHART

SAS advanced data discovery and visualization, point-and-click analytics capabilities, powerful reporting tools.

Character Functions, Numeric Functions, Converting Variable Type.

Hands-on Exercise – Use Functions in data transformation

Introduction to ODS, Data Optimization, How to generate files (rtf, pdf, html, doc) using SAS

Hands-on Exercise – Optimize data, generate rtf, pdf, html and doc files

Macro Syntax, Macro Variables, Positional Parameters in a Macro, Macro Step

Hands-on Exercise – Write a macro, Use positional parameters

SQL Statements in SAS, SELECT, CASE, JOIN, UNION, Sorting Data

Hands-on Exercise – Create sql query to select and add a condition
Use a CASE in select query

Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval, descriptive statistics.

Hands-on Exercise – Use web UI to do statistical operations

Report Enhancement, Global Statements, User-defined Formats, PROC SORT, ODS Destinations, ODS Listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT, PROC PRINT

Hands-on Exercise – Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT

Project 1

Domain – Restaurant Revenue Prediction

Data set – Sales

Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.

Project 2

Domain – Data AnalyticsObjective – To predict about the class of a flower using its petal’s dimensions

Project 3

Domain – FinanceObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors

Project 4

Domain – Stock MarketObjective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices

Project 1 – Understanding Cold Start Problem in Data Science

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 following:

• 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 2 – 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 provider 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 – Data Analysis Project

Data – Sales

Problem Statement – It includes the following actions:

Understand the business solutions, Discussion with the warehouse team, Data Collection & Storage, Data Cleaning, Build a Hypothesis Tree around the business problem, Produce the final result.

Project 1 – Build analytical solution for patients taking medicines

Domain: Health Care

Objective – This project aims to find out descriptive statistics & subset for specific clinical data problems. It will give them brief insight about BASE SAS procedures and data steps.