Section 1
##### Introduction to Analytics

Section 2
##### Understanding Probability and Probability Distribution

1

Introduction to Probability theory

2

Types of probability distribution – Discrete Distribution and Continuous distribution

3

Understanding Probability Mass Function and Probability Density Function

4

Normal Distribution and Standard Normal Distribution

5

Understanding Binomial Distribution and Poisson Distribution

6

Application on Binomial Distribution

7

Application on Normal Distribution

Section 3
##### Introduction to Sampling Theory and Estimation

1

Concept of Population and Sample

2

Introduction to Some important terminologies

3

Parameter and Statistic

4

Properties of a good estimator

5

Standard Deviation and Standard Error

6

Point and Interval Estimation

7

Confidence level and level of Significance

8

Constructing Confidence Intervals

9

Formulation of Null and Alternative hypothesis and performing simple test of Hypothesis

Section 4
##### Introduction to Segmentation Techniques: Factor Analysis

1

Introduction to Factor Analysis and various techniques

2

Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA)

3

KMO MSA test, Bartlett’s Test Sphericity

4

The Mineigen Criterion, Scree plot

5

Introduction to Factor Loading Matrix and various rotation techniques like Varimax

6

Application of the technique on a case study

7

Interpretation of the result

Section 5
##### Introduction to Segmentation Techniques: Cluster Analysis

1

Introduction to Cluster Analysis and various techniques

2

Hierarchical and Non – Hierarchical Clustering techniques

3

Using Hierarchical Clustering in R

4

Performing K – means Clustering in R

5

Divisive Clustering, Agglomerative Clustering

6

Application of Cluster Analysis in Analytics with Examples with profiling of the clusters and interpretation of the clusters

7

Application of the techniques on a case study

8

Interpretation of the result

Section 6
##### Correlation and Linear Regression

1

Introduction to Pearson’s Correlation coefficient

2

Correlation and Causation- Fitting a simple linear regression model

3

Introduction to CLRM

4

Assumptions of CLRM

5

Understanding the MLRM technique

6

Understanding the related statistic to linear regression

7

Goodness of fit test for linear regression

8

Importing dataset in R to apply linear regression

9

Splitting of dataset – Training and testing

10

Conducting several tests to understand the results obtained

11

Checking for the accuracy of the linear regression model

12

Assessing Collinearity, Heteroskedasticity and Auto – Correlation

Section 7
##### Introduction to categorical data analysis and Logistic Regression

1

Comparison between Liner Regression and Logistic Regression

2

Performing Goodness of fit test of the model

3

Introduction to Percent Concordant, AIC, SC, and Hosmer – Lemeshow

4

Receiver Operating Characteristics (ROC) Curve and Area under Curve (AUC)

5

Interpretation of the model: overall fit of the model and finding out the influential variables using Odds ratio criteria

6

Understanding the ROC testing

7

Checking for the accuracy of the model

8

Application and interpretation using case study

Section 8
##### Introduction to Time Series Analysis

1

What is Time series Analysis, Objectives and Assumptions of Time Series

2

Identifying pattern in Time series data: Decomposition of the time series data

3

Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average

4

Exponential Smoothing, Holt’s Linear Exponential Smoothing Examples of Seasonality and detecting Seasonality in Time series data

5

Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology

6

Introduction to Autoregressive Moving Average (ARMA) model and Autoregressive Integrated Moving Average (ARIMA) model

7

Building an ARIMA Model

8

Detection of Stationarity, Seasonality in ARIMA Model

9

Detecting the order of AR and MA of ARIMA model

10

Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)

11

Detecting the order by using AIC and BIC criterion

12

Estimation and forecast using R

Section 9
##### Text Mining

1

Introduction to text mining

2

Importance of applying this technique

3

Package required in R to do text mining

4

Understanding WordCloud methodology

5

Performing text mining analysis using a data

6

Understanding the Sentiment Analysis

7

Application of the technique on a dataset

8

Interpretation of the result

Section 10
##### Market Basket Analysis

Section 11
##### Statistical Significance T Test Chi Square Tests and Analysis of Variance

1

Performing test of one sample mean

2

Difference between two group means (independent sample)

3

Difference between two group means (Paired sample)

4

Performing Chi square tests: Test of Independence

5

Descriptive statistics and inferential statistics

6

T-tests and it’s application on case studies

7

ANOVA testing and its application on case studies

8

Interpretation of the test results

9

Chi-square test of independence

10

Test for correlation and partial-correlation test

11

Performing post-hoc multiple comparisons tests in R using Tukey HSD

12

Performing two-way ANOVA with and without interactions

**Introduction to Excel**

MS-Excel is a spreadsheet package developed by Microsoft Corporation. By spreadsheet, we mean that Excel is a computer application for organizing, analysis, storage of data in tabular format. This program operates on data entered in the cells. A user can enter a number or numeric value in the cell and the number will be used for calculation using formulas or functions.

Let’s have a look at the various components of MS-Excel environment:

- Excel is the Spreadsheet application from Microsoft.
- The spreadsheets come as worksheets which belong to a workbook.
- Each worksheet contains rows and columns.
- Each worksheet in Excel 2013 and above has 1,048,576 rows and 16,384 columns.

**Creating Tables Manually in Excel**

In Excel 2007, and later versions, the Table command can be used to convert a list of data into a formatted Excel Table.

Preparing the Data

Before creating an Excel Table, we should follow these guidelines for organizing the data.

- The data should be
**organized in rows and columns**, with each row containing information about one record, such as a sales order, or inventory transaction. - In the first row of the list, each column should contain a short, descriptive and
**unique heading**. - Each column in the list should contain
**one type of data**, such as dates, currency, or text. - Each row in the list should contain the
**details for one record**, such as a sales order. - The list should have
**no blank rows**within it, and no completely blank columns. - The list should be
**separated from any other data**on the worksheet, with at least one blank row and one blank column between the list and the other data.

After your data is organized, as described above, you’re ready to create the formatted Table.

- Select a cell in the list of data that you prepared.
- On the Ribbon, click the Insert tab.
- In the Tables group, click the Table command.
- In the Create Table dialog box, the range for your data should automatically appear, and the
*My table has headers*option is checked. If necessary, you can adjust the range, and check box. - Click OK to accept these settings.

**Renaming an Excel Table**

When it is created, an Excel table is given a default name, such as Table 3. You should change the name to something meaningful, so it will be easier to work with the table later.

To change the table name:

- Select any cell in the table
- On the Ribbon, under the Table Tools tab, click the Design tab.
- At the far left of the Ribbon, click in the Table name box, to select the existing name
- Then, type a new name, such as Orders, and press the Enter key

**Creating an Excel Table With Specific Style**

When you create a table with the Table command on the Ribbon’s Insert tab, the table retains any formatting that it currently has, and the default Table Style is applied.

If you want to apply a specific table style when creating an Excel Table:

- Select a cell in the list of data that you prepared.
- On the Ribbon, click the Home tab.
- In the Styles group, click Format as Table
- Click on the Style that you want to use
- In the Create Table dialog box, the range for your data should automatically appear, and the
*My table has headers*option is checked. If necessary, you can adjust the range, and check box. - Click OK to accept these settings.