What you'll get
- 54+ Hours
- 9 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Predictive modeling tools such as SPSS, Minitab, and SAS Enterprise Miner enable users to analyze and visualize data.
- Provides one year of course access for flexible learning.
- Open to individuals aiming to build a career in predictive analytics.
- Recommends basic familiarity with predictive analytics concepts.
- Includes a course completion certificate.
- Offers verifiable certificates with unique links for courses and projects, suitable for resumes and professional profiles.
- Delivered as a self-paced video-based training program.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Predictive Modeling using Minitab | 15h 32m | ✔ | View Curriculum |
| Predictive Modeling using SPSS | 13h 17m | ✔ | View Curriculum |
| SAS - Predictive Modeling with SAS Enterprise Miner | 9h 19m | ✔ | View Curriculum |
| Predictive Modeling Training | 1h 6m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression Project using SAS Stat | 4h 26m | ✔ | View Curriculum |
| Project on Linear Regression in Python | 2h 28m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using R | 3h 2m | ✔ | View Curriculum |
| Credit-Default using Logistic Regression | 3h 3m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
Description
This course equips learners with the skills to analyze data, build predictive models, and generate actionable insights using industry-standard tools like SPSS, Minitab, and SAS Enterprise Miner. Designed for students, professionals, and anyone aiming to advance a career in data science, the program covers essential concepts, practical applications, and hands-on projects.
Participants gain a strong foundation in statistics, programming (Python and R), and predictive modeling techniques, preparing them for roles such as Data Analyst, Statistician, or IT professional in data-driven industries. The course is self-paced, includes verifiable completion certificates, and provides real-world project experience to showcase skills effectively.
Sample Certificate
Requirements
- The program does not require mandatory prerequisites, though prior exposure to relevant concepts and tools can enhance the learning experience.
- Learners demonstrate a genuine interest in data science and analytics.
- A solid understanding of descriptive and inferential statistics is strongly associated with success in the program.
- Having experience with programming languages like Python and R, coupled with practical coding practice, can greatly enhance learning.
- Basic knowledge of programming fundamentals, including variables, control structures, functions, and common data structures, is advised.
- Participants should be self-motivated, committed to continuous learning, and prepared to dedicate approximately 10 hours per week to meet course expectations.
Target Audience
- Students pursuing a diploma, undergraduate, or graduate degree in statistics, mathematical modeling, or predictive analytics can benefit from this course, which covers the essential fundamentals.
- Professionals aiming to become data analysts, statisticians, or IT specialists seeking a career transition into data analysis and predictive modeling are eligible to enroll.
- Research scientists, IT experts, educators, and professors with an interest in data can leverage this course to enhance their analytical skills.
- Individuals keen on learning to harness data to predict future trends will gain practical knowledge through real-world projects included in the training.
