BAR-EMBA-BA-19

BAR-EMBA-BA-19
MBA(Exe.)-BA-2019-20: Term-III

Business Analytics with R/Python

Ex MBA (BA)-2019-20
No. of Credits: 3
Instructor- Prof.Shabana C

Course Description
In the modern era of exorbitantly soaring quantity of raw data as well as processed information, challenges faced by decision makers including management professionals primarily consist of visualizing, analyzing and opting for the best or nearly best decisions which may help the organization in accomplishing many challenges. Business analytics is the scientific process of transforming data into insight for making better decisions used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making. This course is aimed at providing the exposure and hands on experience to the attendees on Business Analytics using Data Mining and Machine Learning principles. Along with this the attendees will be exposed to R to aid in computing utilizing the Data Mining and Machine Learning algorithms.

Learning Outcomes

At the end of the course you should
1. Be able to define a business issue as an analytical problem.
2. Be able to choose from various analytical techniques in a given situation
3. Be able use R for statistical analysis
4. Be able to understand, interpret and recommend actions based on analytical output

Session Plan

Session
Topics/Activities
Reading

1-2 Introduction to Business Analytics T

3-4 Getting stated with R
I. Basic operations in R
II. Classes and data structures
III. Importing and exporting data from local files R2

5 Data
I. Types of Data
II. Data Cleaning, Missing Data, Misclassifications, Outliers
III. Data Preprocessing
IV. Flag Variables, Duplicity, Removal and Retaining of Variables T, R1

6-7 Exploratory Data Analysis, Visualization and Dimensionality Reduction –
I. Summary Statistics
II. Visualizations
III. Principal Component Analysis T, R1

8-9 Statistical Analysis –
I. Random Variables & Probability Distributions
II. Hypothesis Testing
a. Single Sample
b. Two Sample
c. Multiple Samples
III. Simple and Multiple Linear Regression
IV. Nonparametric Tests T

10-13 Supervised Learning – Classification
I. Basic Concepts of Classification Task
II. k-NN Algorithm
III. Decision trees
IV. Logistic & Ordinal Regression
V. Neural Networks
VI. Support Vector Machines
VII. Naīve Bayes and Bayesian Networks T, R1, R2

14-15 Performance Evaluation
I. Assessing Performance of Models
II. Overfitting v/s Underfitting T, R1, R2

16-17 Unsupervised Learning – Clustering
I. Hierarchical & k-means Clustering
II. Kohonen Networks
III. Measuring Clustering Goodness T, R1, R2

18 Unsupervised Learning – Association Rule Mining T, R1

19 Model Enhancement T, R2

20 Project Presentation

Evaluation

1. Attendance 10%
2. Mid Term of 25% weight
3. Individual projects of 25% weight
4. End-term of 40% weight

No Makeup Exams. Marks for missed components will be equated to the minimum of the other attended components (in percentage terms).

Reading

1. DISCOVERING KNOWLEDGE IN DATA: An Introduction to Data Mining by DANIEL T. LAROSE & CHANTAL D. LAROSE, Wiley; Second edition (2015), ISBN-10: 9788126558346
Reference Texts

1. INTRODUCTION TO DATA MINING by Pang – NING TAN, MICHAEL STEINBACH and VIPIN KUMAR, Pearson (R1)
2. AN INTRODUCTION TO STATISTICAL LEARNING WITH APPLICATIONS IN R by GARETH JAMES, DANIELA WITTEN, TREVOR HASTIE and ROBERT TIBSHIRANI (R2)

Academic Integrity

Malpractice in any form will be dealt with as per manual of policies

Created By: Alora Kar on 11/25/2019 at 04:12 PM
Category: MBA(Exe.)BA-2019-20 T-III Doctype: Document

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Session	Topics/Activities	Reading
1-2	Introduction to Business Analytics	T
3-4	Getting stated with R I. Basic operations in R II. Classes and data structures III. Importing and exporting data from local files	R2
5	Data I. Types of Data II. Data Cleaning, Missing Data, Misclassifications, Outliers III. Data Preprocessing IV. Flag Variables, Duplicity, Removal and Retaining of Variables	T, R1
6-7	Exploratory Data Analysis, Visualization and Dimensionality Reduction – I. Summary Statistics II. Visualizations III. Principal Component Analysis	T, R1
8-9	Statistical Analysis – I. Random Variables & Probability Distributions II. Hypothesis Testing a. Single Sample b. Two Sample c. Multiple Samples III. Simple and Multiple Linear Regression IV. Nonparametric Tests	T
10-13	Supervised Learning – Classification I. Basic Concepts of Classification Task II. k-NN Algorithm III. Decision trees IV. Logistic & Ordinal Regression V. Neural Networks VI. Support Vector Machines VII. Naīve Bayes and Bayesian Networks	T, R1, R2
14-15	Performance Evaluation I. Assessing Performance of Models II. Overfitting v/s Underfitting	T, R1, R2
16-17	Unsupervised Learning – Clustering I. Hierarchical & k-means Clustering II. Kohonen Networks III. Measuring Clustering Goodness	T, R1, R2
18	Unsupervised Learning – Association Rule Mining	T, R1
19	Model Enhancement	T, R2
20	Project Presentation