What you'll get
- 79+ Hours
- 17 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- Fundamentals of predictive modeling
- Building models with SAS, Minitab, and SPSS
- Data preparation and cleaning
- Regression, correlation, and hypothesis testing
- Analyzing and interpreting results
- Integrating Excel data
- Applying linear algebra, calculus, and programming basics
- Solving real-world problems across industries
- Hands-on project experience
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Predictive Modeling using Minitab | 15h 32m | ✔ | View Curriculum |
| SAS - Predictive Modeling with SAS Enterprise Miner | 9h 19m | ✔ | View Curriculum |
| Predictive Modeling using SPSS | 13h 17m | ✔ | View Curriculum |
| Predictive Modeling Training | 1h 6m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Predictive Modeling with Python | 8h 26m | ✔ | View Curriculum |
| EViews:04 - Regression Modeling | 3h 12m | ✔ | View Curriculum |
| Logistic Regression | 1h 58m | ✔ | View Curriculum |
| Logistic Regression with R | 4h 14m | ✔ | View Curriculum |
| Machine Learning Project #3 - Predicting Prices using Regression | 2h 18m | ✔ | View Curriculum |
| ggplot2 Project | 2h 07m | ✔ | View Curriculum |
| Logistic Regression Project using SAS Stat | 4h 26m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using R | 3h 2m | ✔ | View Curriculum |
| Card Purchase Prediction using R | 2h 28m | ✔ | View Curriculum |
Description
This Predictive Modeling course offers a comprehensive learning experience in building data-driven predictive models using industry-standard tools like SAS, Minitab, and SPSS. Designed for learners aiming to pursue a career in data and statistical analysis, the course provides a strong foundation in predictive modeling techniques, from basic statistical concepts to advanced analytical methods.
Through hands-on exercises and real-world projects, participants will learn how to prepare data, select appropriate models, perform analysis, and interpret results effectively. The course also integrates practical applications using Excel datasets and familiarizes learners with essential concepts in linear algebra, calculus, and programming, ensuring readiness for real-world predictive analytics challenges.
By the end of the course, learners will confidently apply predictive modeling techniques across industries such as finance, insurance, research, software, and healthcare to make effective data-driven decisions.
Sample Certificate
Requirements
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Basic understanding of statistics (mean, median, mode, standard deviation)
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Familiarity with MS Excel
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Basic knowledge of linear algebra (matrices, determinants) and simple calculus (differentiation)
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Exposure to at least one programming language (e.g., C or C++).
Target Audience
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Students from technical, computer science, mathematics, or statistics backgrounds
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Entry-level professionals in software, banking, insurance, IT, and finance looking to move into data analysis
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Managers and industry professionals aspiring to become consultants or data scientists
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Professionals from engineering, biotechnology, law, medicine, research, and other fields applying data analysis in their domains
