Predictive Analytics & Modeling R – Minitab – SPSS – SAS

Free Download Predictive Analytics & Modeling: R | Minitab | SPSS | SAS
Last updated: 7/2024
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 49h 46m | Size: 19.3 GB
Master predictive analytics and become a data expert with our all-inclusive course on R, Minitab, SPSS, and SAS!

What you’ll learn

Data Importing and Preparation: Learn how to import, clean, and prepare datasets in R, Minitab, SPSS, and SAS for predictive analysis.
Information Value (IV) Calculation: Understand how to calculate Information Value (IV) and use it to assess the predictive power of variables in R
Model Building and Optimization: Gain proficiency in building and optimizing logistic regression models, decision tree models, and other predictive models
Data Visualization: Master data visualization techniques using tools like ggplot2 in R and various plotting options in Minitab, SPSS, and SAS
Descriptive Statistics and Graphical Representations: Perform and interpret measures of dispersion, descriptive statistics, and create graphical presentations
Hypothesis Testing and ANOVA: Conduct hypothesis testing, ANOVA, and other statistical analyses to make informed decisions based on data.
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 non-linear 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: Gain practical experience through hands-on projects, such as card purchase prediction in R, to reinforce learning and apply skills

Requirements

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.

Description

IntroductionWelcome 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 BasicsThis 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 PredictionIn 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 LearnDive 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 BeyondThis 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 MinitabBuilding 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 ApplicationsIn 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 SASThe 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!

Who this course is for

Data Analysts: Seeking to enhance their predictive modeling skills using industry-standard tools.
Business Analysts: Interested in leveraging predictive analytics to make data-driven decisions.
Statisticians: Looking to apply statistical models to predict outcomes.
Researchers: Wanting to use predictive modeling in their research projects.
Graduate Students: Pursuing studies in data science, statistics, or related fields.
Professionals: From diverse domains interested in using predictive analytics for problem-solving.
Anyone Interested: In learning and applying predictive modeling techniques using R, Minitab, SPSS, and SAS.
Homepage:

https://www.udemy.com/course/predictive-analytics-modeling-r-minitab-spss-sas/