Big Data and Business Analytics

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•  Syllabus to view/download click here.

• Guidelines for Writing Lab Experiments click here.

Course Materials (PPTs/PDFs/Handouts)

• Module 1: Introduction to Data Analytics

  • Need for Business Intelligence

  • Data Management, Data Visualization, Data Warehousing

  • ETL Data Processing Chain From Business Intelligence to Business Analytics

• Module 2: Business Analytics Cycle

  • Introduction, Analytical Tools & Methods, 

  • Integration Social Analytics, Operational Analytics Big Data Analytics, 

  • Hadoop, Informatics, Cognos etc. 

  • Business application of big data analytics

• Module 3: Data Mining & decision Making 

  • Predictive Analysis 

  • Forecasting Optimization

  • Simulation Gamification.

• Module 4: Business Metrics in Action

  • Data science in Startups

  • Basics of Problem-Solving

  • Design Patterns in Statistical Computing

  • Excel for Data Science.

• Module 5:  Data Driven Prediction Methods

  • NLP, Regression, 

  • Correlation, Cluster Analysis, 

  • Artificial Neural Networks,

  • BI Tools and Applications.

• Module 6: Case Studies and presentations

Course Materials (PPTs/PDFs/Handouts) - OLD

• Ch1 [3 Hrs]: Introduction to Big Data [PDF]

• Ch2 [2 Hrs]: Introduction to Hadoop [PDF]

• Ch3 [4 Hrs]: NoSQL [PDF] [MongoDB Install] [Introduction to MongoDB]

• Ch4 [6 Hrs]: MapReduce and the New Software tack [PDF]

• Ch5 [3 Hrs]: Finding Similar Items [PDF] [e.g Edit Distance]

• Ch6 [6 Hrs]: Mining Data Streams [PDF] [e.g Flajolet Martin Algorithm] [e.g Bloom Filter]

• Ch7 [5 Hrs]: Link Analysis [PDF] [e.g PageRank] [PageRank Examples][e.g TSPR][e.g HITS]

• Ch8 [5 Hrs]: Frequent Itemsets [PDF]

• Ch9 [5 Hrs]: Clustering [PDF]

• Ch10 [4 Hrs]: Recommendation Systems [PDF] [e.g TF-IDF]

• Ch11 [5 Hrs]: Mining Social-Network Graphs [PDF]

Course Lab Experiments

Guidelines for Writing Lab Experiments click here.

• • Lab 1 [Ch1]: To draw and explain Hadoop Architecture and Ecosystem with the help of a case study using WorkCount example. To define and install Hadoop.

    • Introduction

    • Hadoop Installation Steps

  • Lab 2 [Ch2]:To implement the following file management tasks in Hadoop System (HDFS): Adding files and directories, Retrieving files, Deleting files

    • Hadoop - HDFS UI Ports and Nodes 

    • Hadoop_Lab - helloHDFS

  • Lab 3 [Ch2]:To run a basic Word Count MapReduce program to understand MapReduce Paradigm: To count words in a given file, To view the output file, and To calculate execution time. 

    • Steps to download gutenberg dataset 

    • WordCount.java 

    • WordCount.jar 

    • WordCount using gutenberg dataset 

  • Lab 4 [Ch3]: To perform NoSQL database using mongodb to create, update and insert.

    • MongoDB_Lab - Installation Steps 

    • primer-dataset.json 

    • MongoDB_Lab - Operations 

  • Lab 5 [Ch5]: To study and implement basic functions and commands in R Programming. 

    • R Installation Steps   

    • R Basic Commands 

    • Exploratory Data Analysis 

  • Lab 6 [Ch5]: To finding similar documents with Cosine Similarity in R.  

  • Lab 7 [Ch6]: To implement Bloom Filters for filter on Stream Data in C++/java. 

    • About Bloom Filter   

    • Bloom Filter Example 

    • Bloom Filter Sample Code

  • Lab 8 [Ch6]: To implement Flajolet-Martin Algorithm for counting distinct elements in Stream Data. 

  • Lab 9 [Ch8]: To implement clustering program using R programming. 

    • About Clutering

    • foodstuffs_dataset.txt 

    • foodstuffs_clustering_example

  • Lab 10 [Ch1]: To build WordCloud, a text mining method using R  for easy to understand and visualization than a table data.  

    • About WordCloud 

    • Sample Dataset 

    • WorldCloud Example

  • Lab 11 [Ch5 and Ch10]: To find Term Frequency and Inverse Document Frequency (tf-idf) Matrix for Recommendation Systems and Plot TF Using R used.

    • TF-IDF Definition 

    • TF-IDF Example 

    • Dataset 

    • TF-IDF Code in R

  • Lab 12 [Ch7]:

References

Text books

1. Big Data Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses by Michael Minelli

2. Work-study by ILO

Reference books

1. Business Analytics: Data Analysis and Decision Making by S. Christian Albright

2. Big Data: Using Smart Big Data, Analytics and Metrics to Make Better Decisions and Improve Performance by Bernard Marr