Section 1
##### Introduction to Analytics

1

Introduction to Excel

2

Conditional Formatting

3

Data Summarization techniques

4

Graphical summary using SAS/GRAPH: Introduction to Bar graph

5

Graphical summary using SAS/GRAPH: Introduction to Pie graph

6

Graphical summary using SAS/GRAPH introduction to Histogram, Box plots, Scatter diagram

7

Descriptive Statistics-Introduction to various measures of Central Tendency

8

Introduction to the measures of Dispersion, Range, Mean Deviation , Standard Deviation

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

9

Introduction to Probability theory

10

Types of probability distribution – Discrete Distribution and Continuous distribution

11

Understanding Probability Mass Function and Probability Density Function

12

Normal Distribution and Standard Normal Distribution

13

Normal plot using Proc GPLOT procedure in SAS

14

Application of Normal distribution in Analytics with real life examples

15

Binomial Distribution and Binomial plot using PROC GPLOT procedure in SAS

16

Poisson distribution and Poisson plot using Proc GPLOT procedure in SAS

17

Application of Binomial and Poisson distribution in Analytics with real life examples

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

18

Concept of Population and Sample

19

Use of PROC SURVEYSELECT procedure in SAS

20

Introduction to Some important terminologies

21

Parameter and Statistic

22

Properties of a good estimator

23

Standard Deviation and Standard Error

24

Point and Interval Estimation

25

Confidence level and level of Significance

26

Constructing Confidence Intervals

27

Formulation of Null and Alternative hypothesis

28

Performing simple test of Hypothesis

Section 4

Section 5
##### Statistical Significance of T-Tests Chi Square Tests and Analysis of Variance

29

Performing test of one sample mean using Proc ttest

30

Difference between two group means (independent sample) using Proc ttest

31

difference between two group means (Paired sample) using Proc ttest

32

Performing Chi-square tests: Test of Independence

33

Performing one-way ANOVA with PROC ANOVA and PROC GLM procedure

34

Performing post-hoc multiple comparisons tests in PROC

35

GLM using Tukey’s mean test

Section 6
##### Introduction to Segmentation Techniques: Factor Analysis

36

Introduction to Factor Analysis and various techniques

37

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

38

Application of Factor Analysis using Proc Factor procedure

39

KMO MSA test, Bartlett’s Test Sphericity

40

The Mineigen Criterion, Scree plot

41

Introduction to Factor Loading Matrix

42

Various rotation techniques like Varimax

Section 7
##### Introduction to Segmentation Techniques: Cluster Analysis

43

Introduction to Cluster Analysis and various techniques

44

Hierarchical and Non – Hierarchical Clustering techniques

45

Using Hierarchical Clustering by Proc Tree procedure in SAS

46

Performing K – means Clustering in SAS

47

Divisive Clustering, Agglomerative Clustering

48

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

Section 8
##### Correlation and Linear Regression

49

Introduction to Pearson’s Correlation coefficient using PROC CORR procedure

50

Correlation and Causation – Fitting a simple linear regression model with the Proc REG procedure

51

Understanding the concepts of Multiple Regression

52

Using automated model selection techniques in PROC REG to choose the best model

53

Interpretation of the model: overall fit of the model and finding out the influential variables

54

Linear Regression diagnostics

55

Examining Residual

56

Assessing Collinearity, Heteroskedasticity and Auto – Correlation

Section 9
##### Introduction to Categorical Data Analysis and Logistic Regression

57

Comparison between Liner Regression and Logistic Regression

58

Performing Logistic regression using Proc Logistic Procedure in SAS

59

Performing Goodness of ft of the model

60

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

61

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

62

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

63

Using automated model selection techniques in PROC Logistic to choose the best model using AIC criteria

Section 10
##### Introduction to Time Series Analysis

64

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

65

Identifying pattern in Time series data: Decomposition of the time series data and general aspect of the analysis

66

Introduction to Various Smoothing techniques: Simple Moving Average, Weighted Moving Average, Exponential Smoothing, Holt’s Linear Exponential Smoothing

67

Examples of Seasonality and detecting Seasonality in Time series data

68

Introduction to Proc Forecast to generate forecast for time series data

69

Autoregressive models and Stepwise Autoregression (STEPAR) procedure

70

Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology

71

Introduction to Autoregressive Moving Average (ARMA) model

72

Autoregressive Integrated Moving Average (ARIMA) model

73

Building an ARIMA Model

74

Detection of Stationarity, Seasonality in ARIMA Model

75

Detecting the order of AR and MA of ARIMA model by Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)

76

Detecting the order by using AIC and BIC criterion

77

Estimation and forecast using Proc ARIMA in SAS

**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.