SPSS- Predictive Analytics

Course Duration : 30 hrs

About SPSS

SPSS (Statistical Package for the Social Sciences) has now been in development for more than thirty years. Originally developed as a programming language for conducting statistical analysis, it has grown into a complex and powerful application with now uses both a graphical and a syntactical interface and provides various functions for managing, analyzing, and presenting data.
Its statistical capabilities alone range from simple percentages to complex analyses of variance, multiple regressions, and general linear models. SPSS also provides extensive data management functions, along with a complex and powerful programming language.

Course Overview

This advanced-level course focuses on the following key areas:

  • Exploring the various functions for managing your data
  • Conducting statistical analyses
  • Creating tables and charts
  • Preparing your output for incorporation into external files such as spreadsheets and word processors.

What we offer

Training under the guidance of 20+ years experienced Data Scientist with post graduation from IIT, PhD from Boston University, and 40+ research papers on Data Science.
After training, Internship at our Development Partner’s house (Ideal Analytics/ ArcVision) in real-time/live project work.
Case studies on real industry data
Classroom training with flexible timing
Customized/On-demand training
Unlimited access to exclusive Study Materials on Cloud

Chapter-1: Introducing to SPSS

1.1 What is SPSS
1.2 Versions of SPSS
1.3 SPSS Environment
1.4 Introducing the interface
1.5 The data view
1.6 The variable view
1.7 The output view
1.8 The syntax view


Chapter-2: Reading Data from Other Sources

2.1 Reading IBM SPSS Statistics Data Files
2.2 Reading Data from Spreadsheets
2.3 Reading Data from a Database
2.4 Reading Data from a Text file


Chapter-3: Variables Dataset Creation

3.1 Variable names and
3.2 labels Binary Variables
3.3 Variables Type
3.4 Creating a new data set


Chapter-4: Data Transformation

4.1 Data Transformation
4.2 Creating expressions with more than one variable 4.3 Conditional Expressions


Chapter-5: Modifying Data Values

5.1 Modifying Data Values
5.2 Computing New Variables


Chapter-6: Crosstab Report

6.1 Generating Crosstab Report 6.2 Crosstab Statistics
6.3 Crosstab cells
6.4 Adding layers to crosstabs


Chapter-7: Sorting and Selecting Data

7.1 Sorting and Selecting
7.2 Data Split-File Processing
7.3 Selecting Subsets of Cases


Chapter-8: Working with Output

8.1 Working with Output
8.2 Using the Pivot Table Editor
8.3 Using Results in Other Applications
8.4 Exporting Results to Microsoft Word and Excel Files 8.5 Exporting Results to Microsoft Word and Excel Files 8.6 Exporting Results to PDF


Chapter-9: Descriptive Statistics

9.1 Frequencies
9.2 Descriptive statistics: Descriptives (univariate), Level of Measurement
9.3 Summary Measures for Categorical Data 9.4 Charts for Categorical Data
9.5 Summary Measures for Scale Variables 9.6 Recoding existing variables


Chapter-10: Univariate Analysis

10.1 Simple Bar
10.2 Graphs Line, Graphs
10.3 Graphs for cumulative frequency Pie Graph
10.4 The distribution of variables – histograms and frequency statistics
10.5 Checking the nature of the distribution of continuous variables
10.6 Other basic univariate procedures (Boxplot)
10.7 Testing if the mean is equal to a hypothesized number (the T-Test and error bar)


Chapter-11: Multivariate Analysis

11.1 Bar Graph for Means
11.2 Graphing a statistic (e.g. – the mean) of variable “Y” by categories of “X” and “Z” Line 11.3 graph for comparing median
11.4 Boxplots       Scatters
11.5 Plotting scatters of several variables against one other Correlation
11.6 Bivariate correlations Partial correlations


Chapter-12: Charts

12.1 Using the automated chart functin 12.2Using the Interactive chart function 12.3 Creating a chart from scratch


Chapter-13: Statistical Procedures

13.1 Statistical 13.2Procedures 13.3Measuring 13.4Associations Bivariate 13.5correlations Chi-13.6square test 13.7Measuring differences T-Tests
13.8 Anova
13.9 One-Way ANOVA


Chapter-14: Linear Regression

14.1 Linear Regression
14.2 Interpretation of Regression
14.3 Results Diagnostics


Chapter-15:Introduction to Time Series Analysis

15.1 Time Series Model building using SPSS

Tanushree Bhattacharyya

Guest Faculty (Advanced Excel, R)

Tanushree, a post graduate in M.Sc(Econometrics & Statistics), having 8 yrs of experience in Analytics & Mkt Research.Currently working in a big MNC house, proficient in statistical tools like SAS, Advanced Excel, VBA, Access, SQL, SPSS, Quantum. She is highly skilled in data analysis and building statistical model, creating publication quality report and automation of the models with VBA/SAS/SPSS with an excellent track record of managing clients, projects and exceeding expectations. She is an expert in handling analytical projects involving various statistical techniques like demand forecasting , multivariate techniques, optimization, segmentation and reporting the insights to the management to fulfill the business requirements. She is involved with NIVT for over a year now and has an excellent track record of providing training to professionals on Excel,VBA & R Programming. On behalf NIVT she has conducted training in some corporate houses like Dynamic Level.

Debajyoti Chakraborty

In-house Faculty (R & SAS)

Debajyoti, a Statistical Analyst, Member of Actuarial Society of India, Analytics Trainer on Statistical Softwares - SAS,R,Ms Excel with basic query language knowledge on SQL. Graduate in Statistics with Maths and Computer Science as other subjects. Having over 3yrs of work experience as Data Analyst. In Statistical Analyst role he has worked on multiple industry projects including dashboarding and analytics implementation for Retail and Healthcare projects. Also, as an Actuarial Analyst he assisted in Claim Analytics. As an Analytics Trainer, he is providing Analytics training to Industry Professionals and Academic Students on Statistical Software packages - SAS, R, MS Excel (Beginner to Advance) and SPSS, and overseeing Data Analysis projects undertaken by students and knowledge sharing for successful completion of projects on time.