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Description
Introduction
Welcome to the comprehensive course “Predictive Analytics & Modeling with R, Minitab, SPSS, and SAS”. This course is meticulously designed to equip you with the knowledge and skills needed to excel in data analysis and predictive modeling using some of the most powerful tools in the industry. Whether you are a beginner or an experienced professional, this course offers in-depth insights and hands-on experience to help you master predictive analytics.
Section 1: R Studio UI and R Script Basics
This section introduces you to the R programming environment and the basics of using R Studio. You will learn how to download, install, and navigate R Studio, along with understanding basic data types, vectors, matrices, lists, and data frames in R. The section also covers decision making, conditional statements, loops, functions, and the power of ggplot2 for data visualization. By the end of this section, you will have a solid foundation in R programming and the ability to perform essential data manipulation and visualization tasks.
Section 2: Project on R – Card Purchase Prediction
In this section, you will embark on a practical project to predict card purchases using R. The journey begins with an introduction to the project and importing the dataset. You will then delve into calculating Information Value (IV), plotting variables, and data splitting. The course guides you through building and optimizing a logistic regression model, creating a lift chart, and evaluating model performance on both training and test sets. Additionally, you will learn to save models in R and implement decision tree models, including making predictions and assessing their performance. This hands-on project is designed to provide you with real-world experience in predictive modeling with R.
Section 3: R Programming for Data Science – A Complete Course to Learn
Dive deeper into R programming with this comprehensive section that covers everything from the history of R to advanced data science techniques. You will explore data types, basic operations, data reading, debugging, control structures, and functions. The section also includes scoping rules, looping, simulation, and extensive plotting techniques. You will learn about date and time handling, regular expressions, classes, methods, and more. This section is designed to transform you into a proficient R programmer capable of tackling complex data science challenges.
Section 4: Statistical Analysis using Minitab – Beginners to Beyond
This section focuses on statistical analysis using Minitab, guiding you from beginner to advanced levels. You will start with an introduction to Minitab and types of data, followed by measures of dispersion, descriptive statistics, data sorting, and various graphical representations like histograms, pie charts, and scatter plots. The section also covers probability distributions, hypothesis testing, sampling, measurement system analysis, process capability analysis, and more. By the end of this section, you will be adept at performing comprehensive statistical analyses using Minitab.
Section 5: Predictive Analytics & Modeling using Minitab
Building on your statistical knowledge, this section delves into predictive modeling with Minitab. You will explore non-linear regression, ANOVA, and control charts, along with understanding and interpreting results. The section includes practical examples and exercises on descriptive statistics, correlation techniques, regression modeling, and multiple regression. You will also learn about logistic regression, generating predicted values, and interpreting complex datasets. This section aims to enhance your predictive modeling skills and enable you to derive actionable insights from data.
Section 6: SPSS GUI and Applications
In this section, you will learn about the graphical user interface of SPSS and its applications. You will cover the basics of using SPSS, importing datasets, and understanding mean and standard deviation. The section also explores various software menus, user operating concepts, and practical implementation of statistical techniques. By the end of this section, you will be proficient in using SPSS for data analysis and interpretation.
Section 7: Predictive Analytics & Modeling with SAS
The final section of the course introduces you to SAS Enterprise Miner for predictive analytics and modeling. You will learn how to select SAS tables, create input data nodes, and utilize metadata advisor options. The section covers variable selection, data partitioning, transformation of variables, and various modeling techniques, including neural networks and regression models. You will also explore SAS coding and create ensemble diagrams. This section provides a thorough understanding of using SAS for complex predictive analytics tasks.
Conclusion
“Predictive Analytics & Modeling with R, Minitab, SPSS, and SAS” is a comprehensive course designed to provide you with the skills and knowledge needed to excel in the field of data analytics. From foundational programming in R to advanced statistical analysis in Minitab, SPSS, and SAS, this course covers all the essential tools and techniques. By the end of the course, you will be equipped to handle real-world data challenges and make data-driven decisions with confidence. Enroll now and take the first step towards mastering predictive analytics!
| Total Students | 252 |
|---|---|
| Original Price($) | |
| Sale Price | Free |
| Number of lectures | 361 |
| Number of quizzes | 0 |
| Total Reviews | 0 |
| Global Rating | 0 |
| Instructor Name | EDUCBA Bridging the Gap |
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