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

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

Normal plot using Proc GPLOT procedure in SAS

6

Application of Normal distribution in Analytics with real life examples

7

Binomial Distribution and Binomial plot using PROC GPLOT procedure in SAS

8

Poisson distribution and Poisson plot using Proc GPLOT procedure in SAS

9

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

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

1

Concept of Population and Sample

2

Use of PROC SURVEYSELECT procedure in SAS

3

Introduction to Some important terminologies

4

Parameter and Statistic

5

Properties of a good estimator

6

Standard Deviation and Standard Error

7

Point and Interval Estimation

8

Confidence level and level of Significance

9

Constructing Confidence Intervals

10

Formulation of Null and Alternative hypothesis

11

Performing simple test of Hypothesis

Section 4

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

1

Performing test of one sample mean using Proc ttest

2

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

3

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

4

Performing Chi-square tests: Test of Independence

5

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

6

Performing post-hoc multiple comparisons tests in PROC

7

GLM using Tukey’s mean test

Section 6
##### 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

Application of Factor Analysis using Proc Factor procedure

4

KMO MSA test, Bartlett’s Test Sphericity

5

The Mineigen Criterion, Scree plot

6

Introduction to Factor Loading Matrix

7

Various rotation techniques like Varimax

Section 7
##### 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 by Proc Tree procedure in SAS

4

Performing K – means Clustering in SAS

5

Divisive Clustering, Agglomerative Clustering

6

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

Section 8
##### Correlation and Linear Regression

1

Introduction to Pearson’s Correlation coefficient using PROC CORR procedure

2

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

3

Understanding the concepts of Multiple Regression

4

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

5

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

6

Linear Regression diagnostics

7

Examining Residual

8

Assessing Collinearity, Heteroskedasticity and Auto – Correlation

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

1

Comparison between Liner Regression and Logistic Regression

2

Performing Logistic regression using Proc Logistic Procedure in SAS

3

Performing Goodness of ft of the model

4

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

5

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

6

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

7

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

Section 10
##### 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 and general aspect of the analysis

3

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

4

Examples of Seasonality and detecting Seasonality in Time series data

5

Introduction to Proc Forecast to generate forecast for time series data

6

Autoregressive models and Stepwise Autoregression (STEPAR) procedure

7

Autoregressive and Moving Average models and Introduction to Box Jenkins Methodology

8

Introduction to Autoregressive Moving Average (ARMA) model

9

Autoregressive Integrated Moving Average (ARIMA) model

10

Building an ARIMA Model

11

Detection of Stationarity, Seasonality in ARIMA Model

12

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

13

Detecting the order by using AIC and BIC criterion

14

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.