Business Analytics with SPSS

Data Science and Machine Learning using R​
Course duration:

45 hours | 2 months (Live online/classroom training+ Projects + assignment/case studies + Interview preparation)

Training Mode:​

Online & Classroom

Course Fees:
  • Group training — INR 15000 | USD 550 (Other countries)
  • Individual training– INR 30000 | USD 750 (Other countries)
Pre-requisites for Business Analytics with SPSS course:

To attend this course candidates must have good understanding of basic maths and basic statistics.

Certification:

At end of our course, you will be work on various projects and assignmetns. Once you completed your assigned projects with expected results we will issue “Business Analytics with SPSS” Certificate.

Syllabus - program of study:

  • What is SPSS?
  • Application and uses of SPSS
  • SPSS features and limitation.
  • Comparison of SPSS with others Statistical tools
  • Download and Install SPSS: Step-By-Step Guide
  • Mouse and keyboard processing, frequently –used dialog boxes,
  • Editing output
  • Printing results
  • Creating and editing a data file
  • Merits of SPSS
  • Overview of Data view and Variable view
  • Creating SPSS dataset
  • Reading the data set from different formats
  • Defining the variable attributes
  • Different levels of measure: scale, ordinal, nominal
  • Creating new variables
  • Importing data – Excel file, CSV file, Text File, other software file
  • Exporting data – Excel file, CSV file, Text File, other software file
  • Computing new variables
  • Redefining or re-organization of existing data
  • Filtering the data Weighing Cases Sorting
  • Replacing the missing values Using subsets of variables Comparing similar variables
  • Comparing means and distribution Analysis of Variance
  • Creating New Variables (calculations & Binning)
  • Dummy variable creation
  • Applying transformations
  • Handling duplicates
  • Handling missing
  • Sorting and Filtering
  • Sub-setting (Rows/Columns)
  • Appending (Row appending/column appending)
  • Merging/Joining (Left, right, inner, full, outer etc)
  • Handling multiple choice data
  • Introduction exploratory data analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Bivariate Analysis (Cross Tabs)
  • Bar chart, pie chart, histogram, scatter plot, box plot etc.
  • Which of them should be used in different situations?
  • Basic descriptive statistics
  • Measures of central tendency: mean, median, mode
  • Measures of dispersion: range, standard deviation, variance Frequencies and Distribution
  • Other basic univariate procedures: Explore, Crosstabulation

Business Analytics using SPSS

    • Hypothesis analysis with SPSS
    • Null/Alternative Hypothesis formulation
    • P Value Interpretation
    • Chi Square Test
    • Correlation Analysis

     

    Introduction to Predictive Modelling
    • What is Predictive Modelling
    • Importance of predictive modelling
    • Types of Business problems- Mapping of Techniques
    • Different Phases of Predictive Modelling
  • One Sample t test
  • Paired Sample t test
  • Two sample (Independent) t test
    • One way analysis of variance
    • Two way analysis of variance

     

    Introduction to Predictive Modelling
    • What is Predictive Modelling Importance of predictive modelling
    • Types of Business problems- Mapping of Techniques
    • Different Phases of Predictive Modelling

     

    Factor analysis
    • Data Preparation
    • Need of Data preparation
    • Outlier treatment
    • Missing values
    • Variable Reduction Techniques (Factor Analysis)
      • Introduction to Factor Analysis - PCA
      • Scree plot, Eigen Value Interpretation
      • Factor Rotation and Extraction
      • Result Interpretation

     

    Decision Trees
    • Introduction of Decision Trees
    • Types of Decision Tree Algorithms
    • CHAID Vs. CART
    • Decision Trees - Validation
    • Overfitting - Best Practices to avoid
    • Implementation of Solution

     

    Logistic Regression
    • Introduction of Logistic Regression
    • Applications and Assumptions of Logistic Regression
    • Building Logistic Regression Model
    • Understanding standard model metrics (Concordance, Hosmer Lemeshov Test, Gini, KS, Misclassification, etc)
    • Validation of Logistic Regression Models
    • Standard Business Outputs (ROC Curve, AUC, Lift charts, Model equation, Decile Analysis, etc)
    • Interpretation of Results - Business Validation
    • Implementation on new data Logistic Regression

     


    Linear Regression
    • Introduction of Linear Regression
    • Applications and Assumptions of Linear Regression
    • Building Linear Regression Model
    • Understanding standard metrics (Variable significance, R- square/Adjusted R-square, Global hypothesis ,etc)
    • Validation of Models - Training-Validation approach
    • Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
    • Interpretation of Results - Business Validation - Implementation on new data
    • Interpretation of model parameters