Digital Badge Program SPSS

This credential earner has completed instructor-led learning for understanding preparation of data for a successful data science project. This includes: using functions to cleanse and enrich data; using additional field transformations; working with sequence data; sampling, partitioning and balancing data; and improving efficiency.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Advanced Data Preparation Using IBM SPSS Modeler (v18.1.1) - Code: 0A058G

This credential earner has completed instructor-led learning for understanding advanced models to predict categorical & continuous targets. This includes: data preparation issues such as partitioning, detecting anomalies & balancing data; PCA/Factor to reduce number of fields to number of core fields, referred to as components or factors; supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost; & combining supervised models & executing them in a single run.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1) - Code: 0A038G

This credential earner has completed instructor-led learning for an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. This includes a variety of advanced statistical techniques and situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, and how to interpret the results. Also covered is a broad range of techniques for predicting variables, and methods to cluster variables and cases.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Advanced Statistical Analysis Using IBM SPSS Statistics (V25) - Code: 0G09AG

This credential earner has completed instructor-led learning for understanding an application-oriented introduction to advanced statistical methods available in IBM SPSS Statistics. Topics include: a review of a variety of advanced statistical techniques and situations where each technique would be used; assumptions made by each method; how to set up the analysis; how to interpret the results; a broad range of techniques for predicting variables; and methods to cluster variables and cases.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Advanced Statistical Analysis Using IBM SPSS Statistics (V26) - Code: 0G09BG

This credential earner has completed instructor-led learning for understanding the fundamentals of using IBM SPSS Statistics for typical data analysis process. This includes learning the basics of reading data, data definition, data modification, and data analysis and presentation of analytical results. The course covers how easy it is to get data into IBM SPSS Statistics to be able to focus on analyzing the information. Shortcuts to save time are also included.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: IBM SPSS Statistics Essentials (V25) - Code: 0G53AG

This credential earner has completed instructor-led learning for understanding the fundamentals of using IBM SPSS Statistics for typical data analysis. This includes the basics of reading data, data definition, data modification, data analysis, and presentation of analytical results. In addition to the fundamentals, shortcuts to help save time are covered.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: IBM SPSS Statistics Essentials (V26) - Code: 0G53BG.

This credential earner has completed instructor-led learning for understanding the fundamentals of using IBM SPSS Modeler and an introduction to data science. This includes the principles and practice of data science illustrated through the use of the CRISP-DM methodology. Topics also cover the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.1.1, and an introduction to modeling.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Introduction to IBM SPSS Modeler and Data Science (v18.1.1) - Code: 0A008G

This credential earner has completed instructor-led learning for understanding how to analyze text data using IBM SPSS Modeler Text Analytics. Skills covered include understanding the complete set of steps involved in working with text data, from reading the text data to creating the final categories for additional analysis, as well as, how to apply the model to perform churn analysis in telecommunications.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Introduction to IBM SPSS Modeler Text Analytics (v18.1.1) - Code: 0A108G

This credential earner has completed instructor-led learning for understanding a set of procedures for analyzing time series data. This includes: forecasting using a variety of models including regression, exponential smoothing, & ARIMA, which take into account different combinations of trend and seasonality; Expert Modeler features designed to automatically select the best fitting exponential smoothing or ARIMA model; specifying custom models; & identifying ARIMA models with diagnostic tools.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Introduction to Time Series Analysis Using IBM SPSS Modeler (v18.1.1) - Code: 0A028G

This credential earner has completed instructor-led learning for understanding the statistical component of IBM SPSS Statistics. This includes reviewing several statistical techniques, discussing situations to use each technique, setting up the analysis, and interpreting the results. Skills covered include: a broad range of techniques for exploring and summarizing data; investigating and testing relationships; when and why to use various techniques; and how to apply, interpret and display them.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Statistical Analysis Using IBM SPSS Statistics (V25) - Code: 0G51AG

This credential earner has completed instructor-led learning for understanding the statistical component of IBM SPSS Statistics. This includes understanding statistical techniques, situations to use each technique, how to set up the analysis, how to interpret the results, exploring and summarizing data, as well as investigating and testing relationships. Topics also cover when and why to use these various techniques, how to apply them, interpret their output, and graphically display the results.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Statistical Analysis Using IBM SPSS Statistics (V26) - Code: 0G51BG

This credential earner has completed instructor-led learning for understanding advanced models in IBM SPSS Modeler, including the PCA/Factor technique to reduce the number of fields to a number of core factors, (i.e. components or factors). Topics include supervised models - Support Vector Machines, Random Trees and XGBoost; Text data analysis methods; Combining individual models into a single model; and Enhancing the power of IBM SPSS Modeler by adding external models, developed in Python or R.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course:

This credential earner has completed instructor-led learning for understanding the foundations of using IBM SPSS Modeler with an introduction to data science. This includes the principles and practice of data science using the CRISP-DM methodology. Other topics covered including the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.2, and an introduction to modeling.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: IBM SPSS Modeler Foundations (V18.2) - Code: 0A069G

This credential earner has completed instructor-led learning for understanding an introduction to supervised models, unsupervised models, and association models. This application-oriented course covers topics which include predicting whether customers will cancel their subscription, predicting property values, how to segment customers based on usage, and market basket analysis.

Course Objectives

Badge Criteria and Activities

Successfully complete the following IBM Instructor-Led course: Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2) - Code: 0A079G