Courses

Data Analytics

Exploratory Data Analysis (SPSS & R)

Prerequisites for this Course:

  • Knowledge on Excel.
  • Idea about Data base.
  • Knowledge on Mathematics is advance.
  • Software Installation Purpose (Windows 7 above 64-bit), MS-Office.

                                                                        

spss

 

Course Description:

Data Analytics plays a key role in the Social and Sciences of the data. Data Analyst is very help full to very one and they explain Business queries to customers in easy manner. The Analyst explains the predictive details for research area of Business Data through Graphical representation, life tables and model formation of the Business Problem. Data Analytics is a theoretical combination of Statistics & Data Mining. Statistics plays very important role in the Data Analytics. Statistics provides the detailed information of the Business Problem and Data Mining gives information about Decision details of Business Problem.  The Data Analytics gives the clear picture of Decision Management and statistical Inference of Business Problem.

S.No

Topic

Duration In Hours

Description

Data Analysis With SPSS

1

Fundamentals Of SPSS

1

Data Types, Data Import & Export, Data Base Connections.

2

Descriptive Statistics

2

Basic information of Data.

3

Data Manipulation

1

Recode the variable values,

Sort, Filter, Split files, Grouping.

4

Generating Data

1

Generating Random data using Distribution.

5

Data Visualization

1

Creating Graphs for Business Problem.

6

Correlation & Regression

2

Relationship b\w variables and model building for data.

7

Hypothesis

4

Parametric test and Non Parametric tests.

8

ANOVA

4

Application of One way, 2 way and MANOVA for complex experimental designs.

9

Classification of Data Analysis

2

Application of Cluster Analysis.

10

Data Reduction Technique

2

Application of Factor Analysis and Discernment Analysis.

11

Survival Analysis

2

Importance of Survival Analysis in real life.

12

Predictive Analysis

2

Time series of Data.

13

Decision Management

2

Decision Tree, Neural Network.

14

Model Building

2

Regression Types.

15

Document Preparation

1

Output preparation for Business Data.

Data Analysis With R

16

Packages & Installations

2

Use of Package & Installation in R.

17

Data Manipulation

5

Data Fames, Vectors, Functions, Arrays, Matrix.

18

Statistical Concepts

10

Central tendency, Relationship b\w Variables, Model Building, mean difference b\w Variables.

19

Advanced Statistical Concepts

15

MANOVA, Classification Methods, Predictive Modeling & Machine Learning.

20

Package Creation

4

Creating Own Packages in R.

   

Total = 65

 
         

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