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

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.