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

Predictive Analytics & Modeling: R | Minitab | SPSS | SAS, available at $54.99, has an average rating of 5, with 361 lectures, based on 2 reviews, and has 1070 subscribers.

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

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

Summary

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

Price: $54.99

Average Rating: 5

Number of Lectures: 361

Number of Published Lectures: 360

Number of Curriculum Items: 361

Number of Published Curriculum Objects: 360

Original Price: $99.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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!

Course Curriculum

Chapter 1: R Studio UI and R Script Basics

Lecture 1: Overview of R Programming

Lecture 2: Downloading and Installing R Studio

Lecture 3: How to use R Studio

Lecture 4: How to use R Studio Continues

Lecture 5: R Studio Basics

Lecture 6: Basic Data Type R

Lecture 7: Vectors

Lecture 8: More on Vector

Lecture 9: Matrix

Lecture 10: Matrix Continues

Lecture 11: What is List

Lecture 12: What is List Continues

Lecture 13: Data Frame in R

Lecture 14: Data Frame in R Sub Clip

Lecture 15: Decision Making

Lecture 16: Conditional Statements

Lecture 17: Loops in R

Lecture 18: Implementing Loop with Practical Examples

Lecture 19: While Loop

Lecture 20: Break Statement

Lecture 21: Functions

Lecture 22: Alternative Loops

Lecture 23: Alternative Loops Continue

Lecture 24: User Define Function

Lecture 25: Power of GGPLOT

Lecture 26: GGPLOT 2 Visuals

Lecture 27: Use of Function

Chapter 2: Project on R – Card Purchase Prediction

Lecture 1: Introduction and Importing Dataset

Lecture 2: IV Calculation

Lecture 3: Plotting Variables

Lecture 4: Splitting

Lecture 5: Building Logistic Model

Lecture 6: Making Optimal Model

Lecture 7: Making Lift Chart for Training Set

Lecture 8: Checking Model Performance

Lecture 9: Model Performance in Test Set

Lecture 10: Saving Model in R

Lecture 11: Fitting Decision Tree Model

Lecture 12: Fitting Decision Tree Model Continue

Lecture 13: Prediction of Decision Tree and Model Performance

Chapter 3: R Programming for Data Science – A Complete Courses to Learn

Lecture 1: Overview and History of R

Lecture 2: Datatypes and Basic Operations – Part1_1 part 01

Lecture 3: Datatypes and Basic Operations – Part1_1 part 02

Lecture 4: Datatypes and Basic Operations – Part1_2 Part 01

Lecture 5: Datatypes and Basic Operations – Part1_2 Part 02

Lecture 6: Datatypes and Basic Operations – Part1_2 Part 03_part01

Lecture 7: Datatypes and Basic Operations – Part1_2 Part 03_part 02 summary

Lecture 8: Datatypes and Basic Operations – Part2_1

Lecture 9: Datatypes and Basic Operations – Part2_2

Lecture 10: ReadingData-1

Lecture 11: ReadingData-2

Lecture 12: ReadingData-3

Lecture 13: ReadingData-4a

Lecture 14: ReadingData-4b

Lecture 15: Debugging-1

Lecture 16: ControlStructures

Lecture 17: Functions Part 01

Lecture 18: Functions Part 02

Lecture 19: ScopingRules1 Part 01

Lecture 20: ScopingRules1 Part 02

Lecture 21: ScopingRules2

Lecture 22: Looping1

Lecture 23: Looping2

Lecture 24: Looping3

Lecture 25: Simulation_part-1

Lecture 26: Simulation_part-2

Lecture 27: Plotting1

Lecture 28: Plotting2

Lecture 29: Plotting3_part-1

Lecture 30: Plotting3_part-2

Lecture 31: Plotting4

Lecture 32: Plotting5

Lecture 33: Plotting Colors 1

Lecture 34: Plotting Colors 2

Lecture 35: Date and TimePart1and 5.Date and TimePart2

Lecture 36: Date andTimePart3

Lecture 37: RegEx1

Lecture 38: RegEx2

Lecture 39: RegEx3_part-1

Lecture 40: RegEx3_part-2

Lecture 41: Classes and Methods1_part-1

Lecture 42: Classes and Methods1_part-2

Lecture 43: Classes and Methods2_part-1

Lecture 44: Classes and Methods2_part-2

Lecture 45: Debugging Part2

Chapter 4: Statistical Analysis using Minitab – Beginners to Beyond

Lecture 1: Introduction to Minitab

Lecture 2: Types of Data

Lecture 3: Measure of Dispersion

Lecture 4: Descriptive Stats

Lecture 5: Data Sorting

Lecture 6: Histograms

Lecture 7: Pie Charts

Lecture 8: Bar Charts

Lecture 9: Line Graphs

Lecture 10: Scatter plots

Lecture 11: Box Plot

Instructors

EDUCBA Bridging the Gap
Learn real world skills online

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