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

Let’s say you have a worksheet with thousands of rows of data. It would be extremely difficult to see patterns and trends just from examining the raw information. Similar to charts, conditional formatting provides another way to visualize data and make worksheets easier to understand.

Thus, Conditional Formatting is a feature of Excel which allows you to apply a format to a cell or a range of cells based on certain criteria. Conditional Formatting are of the following types:

- Highlight Cell Rules
- Top/Bottom Rules
- Data Bars
- Color Scales
- Icon Sets

**To create a conditional formatting rule
**Say, we have a worksheet containing sales data, and we’d like to see which salespeople are meeting their monthly sales goals. The sales goal is $4000 per month, so we’ll create a conditional formatting rule for any cells containing a value higher than 4000.

- Select the desired cells for the conditional formatting rule.
- From the Hometab, click the Conditional Formatting A drop-down menu will appear.
- Hover the mouse over the desired conditional formatting type, then select the desired rule from the menu that appears. In our example, we want to highlight cells that are greater than $4000.
- A dialog box will appear. Enter then desired value(s) into the blank field. In our example, we’ll enter 4000 as our value.
- Select a formatting style from the drop-down menu. In our example, we’ll choose Green Fill with Dark Green Text, then click OK.
- The conditional formatting will be applied to the selected cells. In our example, it’s easy to see which salespeople reached the $4000 sales goal for each month.

**Conditional formatting presets**

Excel has several predefined styles—or presets—you can use to quickly apply conditional formatting to your data. They are grouped into three categories:

- Data Bars are horizontal bars added to each cell, much like a bar graph.
- Color Scales change the color of each cell based on its value. Each color scale uses a two- or three-color gradient. For example, in the Green-Yellow-Red color scale, the highest values are green, the average values are yellow, and the lowest values are red.

- Icon Sets add a specific icon to each cell based on its value.

**To use preset conditional formatting**

- Select the desired cellsfor the conditional formatting rule.
- Click the Conditional Formatting A drop-down menu will appear.
- Hover the mouse over thedesired preset, then choose a preset style from the menu that appears.
- The conditional formatting will be applied to the selected cells.