Udemy – Predictive Analytics & Modeling: R | Minitab | SPSS | SAS 2024-7

Udemy – Predictive Analytics & Modeling: R | Minitab | SPSS | SAS 2024-7 Downloadly IRSpace

Apr 18, 2025 - 05:30 Updated: Apr 21, 2025 - 11:55

0 0

Udemy – Predictive Analytics & Modeling: R | Minitab | SPSS | SAS 2024-7

Predictive Analytics & Modeling course: R | Minitab | SPSS SAS. This course is precisely designed to equip you with the knowledge and skills necessary to excel in data analysis and predictive modeling using some of the most powerful tools available in the industry. Whether you’re a beginner or a seasoned professional, this course provides deep insight and hands-on experience to help you master predictive analytics.

What you will learn:

Data Import and Preparation: You will learn how to import, clean, and prepare data sets in R, Minitab, SPSS, and SAS for predictive analysis.
Calculating Information Value (IV): Understand how to calculate Information Value (IV) and use it to evaluate the predictive power of variables in R.
Model building and optimization: Gain skills in building and optimizing logistic regression models, decision tree models, and other predictive models.
Data visualization: Master data visualization techniques using tools such as ggplot2 in R and various plotting options in Minitab, SPSS, and SAS.
Descriptive Statistics and Graphical Display: Perform and interpret dispersion measures, descriptive statistics and create graphical displays.
Hypothesis Testing and Analysis of Variance (ANOVA): Perform hypothesis testing, analysis of variance (ANOVA), and other statistical analyzes to make data-driven decisions with confidence.
Control Structures and Functions in R: Learn to write functions, use control structures, and implement loops in R programming for efficient data manipulation.
Advanced statistical techniques: Apply advanced statistical techniques such as nonlinear regression, logistic regression, and multivariate analysis.
Predictive Modeling with SAS Enterprise Miner: Use SAS Enterprise Miner to build predictive models, select input data nodes, and perform variable selection.
Hands-on projects: Get hands-on experience to reinforce learning and apply skills by doing hands-on projects like predicting card purchases in R.

This course is suitable for people who:

Data Analysts: Looking to improve their predictive modeling skills using industry standard tools.
Business Analysts: Interested in using predictive analytics to make data-driven decisions.
Statisticians: seek to apply statistical models to predict outcomes.
Researchers: want to use predictive modeling in their research projects.
Graduate students: studying data science, statistics or related fields.
Professionals: from various fields who are interested in using predictive analytics to solve problems.
Anyone interested in: Learn and apply predictive modeling techniques using R, Minitab, SPSS, and SAS.

Predictive Analytics & Modeling course specifications: R | Minitab | SPSS SAS

Publisher: Udemy
Lecturer: EDUCBA Bridging the Gap
Training level: beginner to advanced
Training duration: 49 hours and 46 minutes
Number of courses: 360

Course headings

Predictive Analytics & Modeling: R | Minitab | SPSS | SAS

Predictive Analytics & Modeling course prerequisites: R | Minitab | SPSS SAS

Basic Understanding of Statistics: Familiarity with basic statistical concepts such as mean, median, mode, standard deviation, and hypothesis testing.
Basic Knowledge of Programming: Some experience with programming concepts, especially in R, is beneficial but not mandatory.
Access to Software Tools: Participants should have access to R, Minitab, SPSS, and SAS software. Instructions for downloading and installing these tools will be provided.
Computer Skills: Proficiency in using a computer, including managing files, installing software, and navigating operating systems.
Mathematical Skills: A basic understanding of algebra and calculus can be helpful for grasping the mathematical foundations of predictive modeling.
English Proficiency: Proficiency in English to follow the course instructions, lectures, and reading materials.