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Data Analytics with IBM-SPSS


Modules Included

The Research Process
Initial Observation
Generate Theory
Generate Hypotheses
Data collection to Test Theory
What to measure
How to Measure
Analyze data
Descriptive Statistics: Overview
Central Tendency
Measure of variation
Coefficient of Variation
Fitting Statistical Models
Types of statistical models
Populations and samples
Simple statistical models
The mean as a model
The variance and standard deviation
Central Limit Theorem
The standard error
Confidence Intervals
Test statistics
Non-significant results and Significant results:
One- and two-tailed tests
Type I and Type II errors
Effect Sizes
Statistical power
Accessing SPSS
To explore the key windows in SPSS
Data editor
The viewer
The syntax editor
How to create variables
Enter Data and adjust the properties of your variables
How to Load Files and Save
Opening Excel Files
Recoding Variables
Deleting/Inserting a Case or a Column
Selecting Cases
Using SPSS Help
The art of presenting data
The SPSS Chart Builder
Histograms: a good way to spot obvious problems
Boxplots (box–whisker diagrams)
Graphing means: bar charts and error bars
Simple bar charts for independent means
Clustered bar charts for independent means
Simple bar charts for related means
Clustered bar charts for related means
Clustered bar charts for ‘mixed’ designs
Line charts
Graphing relationships: the scatterplot
Simple scatterplot
Grouped scatterplot
Simple and grouped -D scatterplots
Matrix scatterplot
Simple dot plot or density plot
Drop-line graph
Editing graphs
What are assumptions?
Assumptions of parametric data
The assumption of normality
Quantifying normality with numbers
Exploring groups of data
Testing whether a distribution is normal
Kolmogorov–Smirnov test on SPSS
Testing for homogeneity of variance
Correcting problems in the data
Looking at relationships
Standardization and the correlation coefficient
The significance of the correlation coefficient
Confidence intervals for r
Correlation in SPSS
i. Bivariate correlation
ii. Pearson’s correlation coefficient
iii. Spearman’s correlation coefficient
iv. Kendall’s tau (non-parametric)
v. Biserial and point–biserial correlations
vi. Partial correlation
vii. The theory behind part and partial correlation
viii. Partial correlation using SPSS
ix. Semi-partial (or part) correlations
Comparing correlations
Comparing independent rs
dependent rs
Calculating the effect size
How to report correlation coefficients
An introduction to regression
Some important information about straight lines
The method of least squares
Assessing the goodness of fit: sums of squares, R and R2
Doing simple regression on SPSS
Multiple regression: the basics
How to do multiple regression using SPSS
Checking assumptions
Background to logistic regression
What are the principles behind logistic regression?
Assessing the model: the log-likelihood statistic
Assessing the model: R and R2
Methods of logistic regression
Interpreting logistic regression
How to report logistic regression
Testing assumptions
Predicting several categories: multinomial logistic regression
Running multinomial logistic regression in SPSS
Looking at differences
The t-test
Rationale for the t-test
Reporting the dependent t-test
Reporting the independent t-test
Between groups or repeated measures?
The t-test as a general linear model
Comparing several means : ANOVA (GLM)
The theory behind ANOVA
Inflated error rates
Interpreting f-test
ANOVA as regression
Assumptions of ANOVA
Planned contrasts
Post hoc procedure

What You Get?

Learn from our comprehensive collection of project case-studies, hand-picked by industry experts, to give you an in-depth understanding of how data science moves industries like telecom, transportation, e-commerce & more.
  1. Global Sales Store Data Analytics - WallMart
  2. Service Calls & Engineers Utilization Data Analytics – HCL Services
  3. Clinical Data Analytics of Cancer Patients Diagnosis & Medication – Global Health Care
  4. Data Analytics of Training & Development Program of Defense Forces – Indian Navy ...many more...
You will be having the opportunity of 10-15 Hrs e-learning exercises along with instructor-led-training which enable candidates to get the maximum out of the subjects and empowering them to build logics to hand any new requirement.
This program has been designed in collaboration with some of the most influential analytics leader and top academician in data science.

Final Outcome

Thanks to the digital revolution that is sweeping the world and India in particular, data scientists are now the most sought-after professionals by big corporations as well as startups. And companies across industries are rewarding good data analysts and scientists with desirable career growth and salaries.