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EDUCBA
By the end of this course, learners will be able to import and manage datasets in SPSS, apply descriptive statistics, analyze correlations, construct linear and multiple regression models, and interpret logistic and multinomial regression outputs. Through hands-on practice with real-world case studies—including heart pulse, copper expansion, energy consumption, and debt assessment—learners will evaluate predictors, interpret coefficients, and validate results.
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By the end of this course, learners will be able to import and manage datasets in SPSS, apply descriptive statistics, analyze correlations, construct linear and multiple regression models, and interpret logistic and multinomial regression outputs. Through hands-on practice with real-world case studies—including heart pulse, copper expansion, energy consumption, and debt assessment—learners will evaluate predictors, interpret coefficients, and validate results.
Read more
By the end of this course, learners will be able to import and manage datasets in SPSS, apply descriptive statistics, analyze correlations, construct linear and multiple regression models, and interpret logistic and multinomial regression outputs. Through hands-on practice with real-world case studies—including heart pulse, copper expansion, energy consumption, and debt assessment—learners will evaluate predictors, interpret coefficients, and validate results.
This course is designed to build a step-by-step mastery of predictive analytics using SPSS, starting from data handling fundamentals to advanced regression modeling. Each module integrates theory with applied case studies, enabling learners to connect statistical concepts to practical decision-making.
What makes this course unique is its structured approach that combines clear explanations, SPSS demonstrations, and diverse datasets across health, psychology, and finance domains. Learners will gain not only technical proficiency in SPSS but also the confidence to apply predictive modeling techniques in real-world research, business, and academic contexts. Whether you are a student, researcher, or professional, this course equips you with the tools to transform raw data into actionable insights.
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Syllabus
Importing Data and SPSS Fundamentals
This module introduces learners to importing data into SPSS, navigating software menus, and applying basic statistical concepts such as mean and standard deviation. Learners will also practice handling different data formats and explore essential data management tasks within SPSS.
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Syllabus
Importing Data and SPSS Fundamentals
This module introduces learners to importing data into SPSS, navigating software menus, and applying basic statistical concepts such as mean and standard deviation. Learners will also practice handling different data formats and explore essential data management tasks within SPSS.
Read more
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Predictive Analytics with SPSS: Analyze & Apply.
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Career center
Learners who complete Predictive Analytics with SPSS: Analyze & Apply will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
A Statistician is a professional who designs experiments, collects, analyzes, and interprets quantitative data to solve problems across various fields. They are experts in statistical methods and predictive modeling. This course helps build a foundation in these core competencies by providing step-by-step mastery of predictive analytics using SPSS. Learners will gain technical proficiency in importing and managing datasets, applying descriptive statistics, analyzing correlations, and constructing and interpreting linear, multiple, logistic, and multinomial regression models. This hands-on approach, with real-world case studies, is particularly valuable for a Statistician, equipping them with the tools to transform raw data into actionable insights and validate results systematically. A master's or doctoral degree is typically required for this role.
Credit Risk Analyst
Credit Risk Analyst
A Credit Risk Analyst evaluates the creditworthiness of individuals or entities and predicts the likelihood of loan defaults, utilizing sophisticated predictive models. This course is exceptionally relevant, as it directly addresses crucial skills for this career, particularly through its emphasis on logistic and multinomial regression. Learners will gain hands-on practice with real-world case studies, including debt assessment and credit card data, which directly simulates scenarios faced by a Credit Risk Analyst. The ability to evaluate predictors, interpret coefficients, and validate results, alongside mastering data handling fundamentals in SPSS, equips learners with the confidence to apply these techniques in complex financial contexts.
Market Research Analyst
Market Research Analyst
A Market Research Analyst studies market conditions to assess the potential sales of products and services, using statistical methods to forecast trends and understand consumer behavior. This course is highly beneficial for aspiring Market Research Analysts, as it provides mastery of predictive analytics using SPSS, a widely used tool in the field. Learners gain technical proficiency in importing and managing datasets, applying descriptive statistics, analyzing correlations, and constructing and interpreting linear and multiple regression models. The course's hands-on practice with real-world case studies enables connection of statistical concepts to practical decision-making, transforming raw data into actionable insights for marketing strategies.
Data Analyst
Data Analyst
A Data Analyst collects, processes, and performs statistical analyses on large datasets to help organizations make informed decisions. This course is directly applicable to the responsibilities of a Data Analyst, providing essential skills in predictive analytics using SPSS. Learners will master importing and managing datasets, applying descriptive statistics, analyzing correlations, and constructing and interpreting linear, multiple, logistic, and multinomial regression models. The hands-on practice with real-world case studies across health, psychology, and finance domains ensures that learners can transform raw data into actionable insights, evaluate predictors, and validate results, equipping them for data-driven problem-solving.
Healthcare Data Analyst
Healthcare Data Analyst
A Healthcare Data Analyst specializes in analyzing complex health-related data to identify trends, predict patient outcomes, optimize operational efficiency, or evaluate public health initiatives. This course is exceptionally beneficial for a Healthcare Data Analyst, as it includes hands-on practice with real-world health datasets. Learners will gain technical proficiency in importing, managing, and applying descriptive statistics to health data, as well as constructing and interpreting linear, multiple, and particularly logistic regression models. The ability to evaluate predictors and validate results from health data equips learners to transform raw data into actionable insights for healthcare decision-making.
Business Analyst
Business Analyst
A Business Analyst works to improve processes, products, services, and software by bridging the gap between business needs and data-driven solutions. They often leverage predictive analytics to forecast outcomes, identify opportunities, and optimize performance. This course helps build a strong foundation in predictive analytics using SPSS, a valuable tool for a Business Analyst. Learners will connect statistical concepts to practical decision-making through constructing and interpreting various regression models, evaluating predictors, and validating results using diverse datasets, empowering them to transform raw data into actionable business insights.
Quantitative Researcher
Quantitative Researcher
A Quantitative Researcher applies statistical and mathematical methods to conduct rigorous investigations, especially in academic, financial, or social science fields, to test hypotheses and draw robust conclusions. This course is highly relevant for a Quantitative Researcher, providing comprehensive coverage of predictive analytics using SPSS. Learners will master data handling fundamentals, applying descriptive statistics, correlation analysis, and constructing and interpreting linear, multiple, logistic, and multinomial regression models. The hands-on practice with diverse datasets across health, psychology, and finance domains enhances technical proficiency and confidence in applying modeling techniques to real-world research. An advanced degree is typically required for this role.
Economist
Economist
An Economist utilizes statistical methods to analyze economic data, forecast trends, and evaluate policies, frequently employing regression analysis to understand relationships between economic variables. This course helps build a foundation in the core methodologies essential for an Economist. Learners will gain proficiency in constructing and interpreting linear, multiple, logistic, and multinomial regression models, evaluating predictors, and validating results. The course's emphasis on transforming raw data into actionable insights and its structured approach to connecting statistical concepts to practical decision-making are invaluable. An advanced degree is typically required for this role.
Epidemiologist
Epidemiologist
An Epidemiologist studies disease patterns and health outcomes in populations, frequently using statistical models to identify risk factors, predict disease spread, and evaluate interventions. This course is highly relevant for an Epidemiologist, given its focus on statistical modeling and its inclusion of health datasets. Learners will acquire skills in applying descriptive statistics, correlation, and constructing linear, multiple, and particularly logistic regression models. The ability to interpret coefficients and odds ratios, along with validating results from real-world health data, equips learners to generate critical public health insights. An advanced degree is typically required for this role.
Fraud Analyst
Fraud Analyst
A Fraud Analyst identifies and investigates fraudulent activities by meticulously examining transaction data and building predictive models to detect anomalies and high-risk behaviors. This course is particularly relevant for a Fraud Analyst, as it provides mastery of predictive analytics, including the construction and interpretation of logistic and multinomial regression models, which are critical for binary outcomes like fraud detection. Learners will gain hands-on practice in importing and managing datasets, applying descriptive statistics, and analyzing correlations to identify patterns. The ability to evaluate predictors and validate results equips learners to transform raw data into actionable insights for effective fraud prevention.
Research Scientist
Research Scientist
A Research Scientist conducts scientific studies, often involving complex data analysis to test hypotheses, identify patterns, and draw conclusions in various scientific disciplines. This course is highly advantageous for a Research Scientist, as it provides comprehensive training in predictive analytics using SPSS. Learners will master data handling fundamentals, applying descriptive statistics, correlation analysis, and constructing and interpreting linear, multiple, logistic, and multinomial regression models. The hands-on practice with diverse datasets across health, psychology, and finance domains enhances confidence in applying these modeling techniques in real-world research. An advanced degree is typically required for this role.
Data Scientist
Data Scientist
A Data Scientist extracts insights and builds predictive models from complex datasets to solve business problems and drive innovation. While often associated with other programming languages, the fundamental principles of predictive modeling and statistical inference are central to the Data Scientist role. This course builds a strong methodological foundation in predictive analytics using SPSS, enabling learners to construct and interpret various regression models and evaluate predictors to transform raw data into actionable insights. This confidence in applying modeling techniques across diverse domains is invaluable.
Social Science Researcher
Social Science Researcher
A Social Science Researcher conducts studies in fields like psychology, sociology, or political science, relying heavily on statistical analysis to understand human behavior and societal trends. This course offers highly applicable skills for a Social Science Researcher, providing mastery of predictive analytics using SPSS. Learners will gain proficiency in applying descriptive statistics, correlation, and constructing and interpreting linear, multiple, logistic, and multinomial regression models. The hands-on practice with psychology datasets mentioned in the course and the emphasis on interpreting outputs and validating results are crucial for rigorous social science research. An advanced degree is often required for this role.
Customer Relationship Management Analyst
Customer Relationship Management Analyst
A Customer Relationship Management Analyst analyzes customer data to understand behavior, predict churn, segment customers, and personalize marketing efforts to enhance customer loyalty and profitability. Predictive analytics, especially regression models, are crucial for these tasks. This course is highly valuable for a Customer Relationship Management Analyst, offering mastery of predictive analytics using SPSS. Learners will gain proficiency in descriptive statistics, correlation, and constructing and interpreting various regression models. The ability to evaluate predictors and transform raw customer data into actionable insights for business decision-making is directly applicable to optimizing customer strategies.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses advanced analytical methods, including statistical modeling, to improve decision-making and efficiency within organizations, often involving resource allocation, forecasting, and optimization. This course is highly beneficial for an Operations Research Analyst, as it builds expertise in predictive analytics using SPSS. Learners will gain proficiency in constructing and interpreting various regression models, evaluating predictors, and validating results. The course's structured approach, connecting statistical concepts to practical decision-making and transforming raw data into actionable insights, directly supports the analytical demands of this role.
For more career information including salaries, visit:
OpenCourser.com/course/6fg04o/predictive
Reading list
We've selected 24 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Predictive Analytics with SPSS: Analyze & Apply.
Is widely considered the gold standard for learning statistics through the lens of SPSS. It provides a comprehensive background on regression and correlation, which are central to this course's syllabus. The author’s engaging style makes it a preferred textbook in academic institutions for both undergraduate and graduate students. It adds significant depth to the course's module on advanced regression and logistic analysis.
This is an essential reference tool for any learner navigating the SPSS interface for the first time. It is particularly helpful for the course's 'Importing Data and SPSS Fundamentals' module, offering clear, step-by-step instructions. The book is frequently used by industry professionals to quickly verify data management procedures. It provides a practical bridge between theoretical statistics and software application.
Serves as a perfect companion for the course's practical demonstrations, covering everything from basic descriptive statistics to multinomial regression. It is highly relevant due to its focus on the specific software versions and menu paths used in modern analytics. It acts as both a current reference and a workbook for the case studies mentioned in the syllabus. It is commonly adopted in research methods courses for its clarity.
This recent publication is tailored specifically for intermediate learners who want to master regression and correlation. It mirrors the course's 'Analyze & Apply' philosophy by using real-world data examples. It useful reference tool for interpreting the 'Case Processing Summary' and 'Parameter Estimates' mentioned in the syllabus. The author is an expert in health-related data analysis, which aligns with the course's health case studies.
This text focuses heavily on the interpretation of outputs, which aligns with the course's objective to help learners 'interpret coefficients and validate results.' It provides additional breadth on multiple regression and logistic modeling. It is more valuable as a deep-dive reference for learners who have mastered the basics of data entry. It staple in graduate-level social science and health research programs.
Written by experts with decades of experience at SPSS/IBM, this book offers an insider's view on the software's capabilities. It is particularly useful for the 'Advanced Regression and Logistic Analysis' module. It serves as a comprehensive reference for both researchers and business analysts. It provides practical tips on data cleaning that are often overlooked in introductory courses.
Is excellent for providing prerequisite knowledge for those who find the course's introductory module moving too quickly. It breaks down complex statistical concepts into digestible terms, making it a useful background reading. It is particularly strong on data visualization and initial data exploration. The authors are recognized authorities in the SPSS community and industry training.
Is specifically designed to help students apply SPSS to research questions, making it a great fit for the course's academic audience. It provides clear guidance on the 'Correlation and Initial Data Visualization' module. It is frequently updated to reflect the latest SPSS interface changes. It offers a practical look at how to structure datasets for various regression analyses.
Is structured around the process of data analysis, which complements the course's 'Analyze & Apply' methodology. It provides excellent practice exercises that mirror the course's case studies on energy and health. It common textbook in undergraduate statistics labs. It helps learners build confidence in navigating the SPSS Data Editor and Output Viewer.
Focuses on applying regression and predictive models to business scenarios, such as the energy consumption and credit card data mentioned in the course. It is an industry-standard textbook for MBA programs and business analysts. It adds breadth by showing how SPSS outputs are used for forecasting and optimization. It valuable reference for the course's financial case studies.
This concise reference tool that is ideal for learners who need a quick refresher on specific SPSS menus. It covers the core requirements of the course, including linear and multiple regression. It is particularly useful as a desk reference while working through the course's hands-on case studies. Its brevity makes it less daunting than larger textbooks.
Provides a broader industry perspective on predictive modeling, which supplements the course's focus on SPSS-specific tools. It is highly useful for understanding the real-world application of the regression models discussed in the syllabus. It acts as a bridge between academic statistics and business decision-making. It valuable reference for learners interested in the finance and debt assessment case studies.
Is highly relevant for learners who want to see how the SPSS techniques they learn fit into the broader data science landscape. It covers correlation, regression, and classification in a concise, practical manner. It very popular choice for professional development among career-changers. It provides excellent background on the statistical significance of predictors.
Is an excellent entry point for learners who are intimidated by the mathematical aspects of predictive analytics. It provides a gentle background on the fundamental statistical concepts introduced in the first module. It widely used textbook for introductory courses in social sciences and psychology. Its focus on software menus makes it highly compatible with the course's SPSS demonstrations.
Provides a modern, intuitive approach to the statistical concepts that underpin the course. It is particularly helpful for the 'Correlation and Initial Data Visualization' module. It popular textbook that emphasizes data-driven decision-making. It offers excellent background knowledge for those who need to brush up on their basic statistical literacy before diving into SPSS.
While the coding examples use R, the conceptual chapters on linear and logistic regression are among the best in the field of predictive analytics. Reading this book adds theoretical depth to the course's modeling modules, explaining the 'why' behind model validation. It is highly prestigious and widely used by data science professionals. It is recommended as additional reading for those pursuing a professional career in analytics.
Aligns perfectly with the course's case-study-driven approach, using real data to explain linear and multiple regression. It prestigious academic text that provides deep insights into model diagnostics and validation. It is more valuable as additional reading for learners wanting to master the nuances of regression coefficients. It helps learners understand the 'copper expansion' and 'pulse' examples at a deeper level.
Provides a solid theoretical foundation for the multinomial and multiple regression topics covered in the course. It helpful background for understanding the relationships between multiple variables. It is well-regarded for its accessibility compared to other multivariate texts. It adds breadth to the course by introducing the logic of complex data structures.
Save
Deep dive into one of the course's most advanced modules: logistic and multinomial regression. It is particularly helpful for learners who need a stronger foundation in odds ratios and coefficients. It is commonly used as a textbook in epidemiology and public health. It provides the mathematical rigor that supplements the software-focused approach of the course.
This high-level reference tool used by advanced researchers and industry professionals. It provides exhaustive detail on the assumptions of regression models, which is crucial for the course's 'validate results' objective. It adds immense depth to the modules on multiple and multinomial regression. It prestigious and authoritative text in the fields of psychology and social science.
This text comprehensive guide to logistic regression, providing more technical detail than the course's introductory module. It useful reference for learners who want to master the 'Model Fitting' and 'Parameter Estimates' outputs in SPSS. The author renowned expert in statistical modeling. It is especially relevant for those applying analytics to finance and debt assessment.
As the definitive authority on logistic regression, this book is essential for the course's advanced modules. It high-level reference tool for learners working with the health and psychology datasets. It provides a rigorous framework for interpreting odds ratios and model fit statistics. It standard text for industry professionals in medical research and analytics.
Although this book focuses on SPSS Modeler, the theoretical sections on predictive modeling and regression are directly applicable to SPSS Statistics users. It provides a more advanced 'data mining' perspective on the course's topics. It valuable reference for learners moving from academic statistics into corporate data science roles. It adds breadth by showing the next level of software application.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6fg04o/predictive
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