Predictive Analytics & Modeling: R | Minitab | Spss | Sas

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!

Overview

Section 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

Section 2: Project on R - Card Purchase Prediction

Lecture 28 Introduction and Importing Dataset

Lecture 29 IV Calculation

Lecture 30 Plotting Variables

Lecture 31 Splitting

Lecture 32 Building Logistic Model

Lecture 33 Making Optimal Model

Lecture 34 Making Lift Chart for Training Set

Lecture 35 Checking Model Performance

Lecture 36 Model Performance in Test Set

Lecture 37 Saving Model in R

Lecture 38 Fitting Decision Tree Model

Lecture 39 Fitting Decision Tree Model Continue

Lecture 40 Prediction of Decision Tree and Model Performance

Section 3: R Programming for Data Science - A Complete Courses to Learn

Lecture 41 Overview and History of R

Lecture 42 Datatypes and Basic Operations - Part1_1 part 01

Lecture 43 Datatypes and Basic Operations - Part1_1 part 02

Lecture 44 Datatypes and Basic Operations - Part1_2 Part 01

Lecture 45 Datatypes and Basic Operations - Part1_2 Part 02

Lecture 46 Datatypes and Basic Operations - Part1_2 Part 03_part01

Lecture 47 Datatypes and Basic Operations - Part1_2 Part 03_part 02 summary

Lecture 48 Datatypes and Basic Operations - Part2_1

Lecture 49 Datatypes and Basic Operations - Part2_2

Lecture 50 ReadingData-1

Lecture 51 ReadingData-2

Lecture 52 ReadingData-3

Lecture 53 ReadingData-4a

Lecture 54 ReadingData-4b

Lecture 55 Debugging-1

Lecture 56 ControlStructures

Lecture 57 Functions Part 01

Lecture 58 Functions Part 02

Lecture 59 ScopingRules1 Part 01

Lecture 60 ScopingRules1 Part 02

Lecture 61 ScopingRules2

Lecture 62 Looping1

Lecture 63 Looping2

Lecture 64 Looping3

Lecture 65 Simulation_part-1

Lecture 66 Simulation_part-2

Lecture 67 Plotting1

Lecture 68 Plotting2

Lecture 69 Plotting3_part-1

Lecture 70 Plotting3_part-2

Lecture 71 Plotting4

Lecture 72 Plotting5

Lecture 73 Plotting Colors 1

Lecture 74 Plotting Colors 2

Lecture 75 Date and TimePart1and 5.Date and TimePart2

Lecture 76 Date andTimePart3

Lecture 77 RegEx1

Lecture 78 RegEx2

Lecture 79 RegEx3_part-1

Lecture 80 RegEx3_part-2

Lecture 81 Classes and Methods1_part-1

Lecture 82 Classes and Methods1_part-2

Lecture 83 Classes and Methods2_part-1

Lecture 84 Classes and Methods2_part-2

Lecture 85 Debugging Part2

Section 4: Statistical Analysis using Minitab - Beginners to Beyond

Lecture 86 Introduction to Minitab

Lecture 87 Types of Data

Lecture 88 Measure of Dispersion

Lecture 89 Descriptive Stats

Lecture 90 Data Sorting

Lecture 91 Histograms

Lecture 92 Pie Charts

Lecture 93 Bar Charts

Lecture 94 Line Graphs

Lecture 95 Scatter plots

Lecture 96 Box Plot

Lecture 97 Discrete Random Variable

Lecture 98 Binomial Distribution

Lecture 99 Normal Distribution

Lecture 100 Normality Test

Lecture 101 Data Transformation

Lecture 102 Sampling and Sample Size

Lecture 103 Sample Size for Estimation

Lecture 104 Parameter Estimation

Lecture 105 Power Analysis

Lecture 106 Measurement System Analysis

Lecture 107 MSA Gage R and R

Lecture 108 MSA Attribute Agreement Analysis

Lecture 109 Process Capability Analysis

Lecture 110 Hypothesis Testing

Lecture 111 Hypothesis Testing Mean

Lecture 112 Paired-T Test

Lecture 113 Anova

Lecture 114 Pareto Analysis

Lecture 115 Correlation

Lecture 116 Regression

Lecture 117 Regression Continue

Lecture 118 Control Charts

Lecture 119 P-Chart

Section 5: Predictive Analytics & Modeling using Minitab

Lecture 120 Introduction of Predictive Modeling

Lecture 121 Non Linear Regression

Lecture 122 Anova and Control Charts

Lecture 123 Understanding, Interpretation and implementation using Minitab

Lecture 124 Continue on Interpretation and implementation using Minitab

Lecture 125 Observation

Lecture 126 Results for NAV Prices

Lecture 127 NAV Prices - Observations

Lecture 128 Descriptive Statistics

Lecture 129 Customer Complaints-Observations

Lecture 130 Resting Heart Rate Observations

Lecture 131 Results for Loan Applicant MTW

Lecture 132 More Details on Results for Loan Applicant MTW

Lecture 133 Features of T- Test

Lecture 134 Loan Applicant

Lecture 135 Paired T - Test

Lecture 136 Understanding and Implementation of ANOVA

Lecture 137 Pairwise Comparisons

Lecture 138 Features of Chi - Test

Lecture 139 Preference and Pulse Rate

Lecture 140 Diffe. btw Growth Plan ad Dividend Plan in MF

Lecture 141 Checking NAV Price and Repurchase Price

Lecture 142 Basic Correlation Techniques

Lecture 143 More on Basic Correlation Techniques

Lecture 144 CT Implementation Using Minitab

Lecture 145 Continue on Implemetation using Minitab

Lecture 146 Interpretation of Correlation Values

Lecture 147 Results for Return

Lecture 148 Correlation Values - Observations

Lecture 149 Correlation Values - Interpretations

Lecture 150 Heart Beat - Objective

Lecture 151 Heart Beat - Interpretation

Lecture 152 Demographics and Living Standards

Lecture 153 Demographics and Living Standards - Observation

Lecture 154 Graphical Implementation

Lecture 155 Add Regression Fit

Lecture 156 Scatterplot with Regression

Lecture 157 Scatterplot of Rhdeq vs Rhcap

Lecture 158 Introduction to Regression Modeling

Lecture 159 Identify Independent Variable

Lecture 160 Regression Equation

Lecture 161 Tabulating the Values

Lecture 162 Interpretation and Implementation on Data Sets

Lecture 163 Continue on Interpretation on Database

Lecture 164 Significant Variable

Lecture 165 Calculating Corresponding Values

Lecture 166 Identify Dependent Variable

Lecture 167 Generate Descriptive Statistics

Lecture 168 Scatterplot of Energy Consumption

Lecture 169 Identity Equation

Lecture 170 P - Value and T - Value

Lecture 171 Changes in Tem. and Expansion

Lecture 172 Objective of Stock Prices

Lecture 173 Interpretations of Example 5

Lecture 174 Reliance Return Change

Lecture 175 Generate Predicted Values

Lecture 176 Scatterplot Return RIL

Lecture 177 Basic Multiple Regression

Lecture 178 Basic Multiple Regression Continues

Lecture 179 Basic Multiple Regression - Interpretation

Lecture 180 Generate Basic Statistics

Lecture 181 Working on Scatterplot

Lecture 182 Dependent Variable Objective

Lecture 183 Concept of Multicollinearity

Lecture 184 Identify Dependent Variable Y

Lecture 185 Outputs and Observation

Lecture 186 Interpretations - Example 3

Lecture 187 Calculate with and without Flux

Lecture 188 Scatterplot of Heart FLux Vs Insolation

Lecture 189 Interpretation of Datasets

Lecture 190 Implementation of Datasets

Lecture 191 Example 4 Observations

Lecture 192 Display Descriptive Statistics

Lecture 193 Predicted Values Example 4

Lecture 194 Scatterplot of Example 4

Lecture 195 Calculating IV - Multiple Regression

Lecture 196 Calculating Independent Multiple Regression

Lecture 197 Understanding Basic Logistic Scatter Plot

Lecture 198 Basic Logistic Scatter Plot Continues

Lecture 199 Generation of Regression Equation

Lecture 200 Tabulated Values

Lecture 201 Interpretation and Implementation on Dataset

Lecture 202 Interpretation and Implementation on dataset Continues

Lecture 203 Output and Observation - Tabulated Values

Lecture 204 Business Metrics Example

Lecture 205 Example Two and Three Interpretations

Lecture 206 Regression Equation Group

Lecture 207 Interpretation and Implementation of Scatter Plot

Lecture 208 More on Implementation of Scatter Plot

Lecture 209 Plastic Case Strength

Lecture 210 Separate Equations

Lecture 211 Generation of Predicted Values

Lecture 212 Scatter Plot Strength Vs Temp

Lecture 213 Data of Cereal Purchase

Lecture 214 Children Viewed and RE

Lecture 215 Predicted Values for Individual Customers

Lecture 216 Income Independent Variable

Lecture 217 Example of Credit Card Issuing

Lecture 218 Example Five - Tabulated Values

Lecture 219 Generating Outputs

Lecture 220 Example Five Interpretations

Lecture 221 Situations Income

Lecture 222 Scatterplot

Lecture 223 Scatter Plot Scale

Lecture 224 Using Data Analysis Toolpak

Lecture 225 Implementation of Descriptive Statistics

Lecture 226 Descriptive statistics - Input Range

Lecture 227 Implementation of ANOVA

Lecture 228 Implementation of T - Test

Lecture 229 Implementation Using Correlation

Lecture 230 Implementation Using Regression

Section 6: SPSS GUI and Applications

Lecture 231 Implementation using SPSS

Lecture 232 Implementation using SPSS Continues

Lecture 233 Importing Datasets in Text and CSV

Lecture 234 Other Concepts of Understanding Mean SD

Lecture 235 Software Menus

Lecture 236 Understanding Mean Standard Deviation

Lecture 237 Understanding User Operating Concepts

Section 7: Predictive Analytics & Modeling with SAS

Lecture 238 Introduction of SAS Enterprise Miner

Lecture 239 Select a SAS Table

Lecture 240 Creating Input Data Node

Lecture 241 Metadata Advisor Options

Lecture 242 Add More Data Sources

Lecture 243 Sample Statistics

Lecture 244 Trial report

Lecture 245 Properties of Cluster Node

Lecture 246 Variable Selection

Lecture 247 Input Variable

Lecture 248 Input Variable Continues

Lecture 249 Values of R-Square

Lecture 250 More on Variable Selection

Lecture 251 Binary Target Variable

Lecture 252 Variable and Effect Summary

Lecture 253 Variable Selection - Variable ID's

Lecture 254 Variable Frequency Table

Lecture 255 Variable S - Updating Model Comparison

Lecture 256 Run Data Partition Node

Lecture 257 Variable Selection - Fit Statistics

Lecture 258 Understanding Transformation of Variables

Lecture 259 Score Ranking Overlay Res.

Lecture 260 Update Transformation of Variables

Lecture 261 Combination of Different Models

Lecture 262 Properties of Neural Network

Lecture 263 Analyzing the Output Variable

Lecture 264 Combination of Regression Model

Lecture 265 Combination - Result of Regression Node

Lecture 267 Subseries Plot

Lecture 268 Creating Densemble Diagram

Lecture 269 SAS Code

Lecture 270 Decision Tree Model

Lecture 271 Run and Upadate Decision Tree Model

Lecture 272 Creating Dscore Node

Lecture 273 DT - Resulf of Model Comparison

Lecture 274 Leaf Statistics and Tree Map

Lecture 275 Interactively Decision Trees

Lecture 276 Result Node Data Partition

Lecture 277 Interactively Trees Window

Lecture 278 Building a Decision Trees

Lecture 279 Neural Network Model

Lecture 280 Neural Network Model Output

Lecture 281 Model Weight History

Lecture 282 Neural Network - Final Weight

Lecture 283 ROC Chart

Lecture 284 Neural Network -Iteration Plot

Lecture 285 Neural Network - SAS Code

Lecture 286 Neural Network - Cumulative Lift

Lecture 287 Decision Processing

Lecture 288 Results of Auto Neural Node

Lecture 289 Run Model Comparison

Lecture 290 DEX - Variable ID's

Lecture 291 Average Square Error

Lecture 292 Score Rating overlay - Event

Lecture 293 Run Dmine Regression Node

Lecture 294 Regression with Binary Target

Lecture 295 Regression - Table Effect Plots

Lecture 296 Result of Regression Model

Lecture 297 Update Regression Node

Lecture 298 Creating Flow Diagram

Section 8: Predictive Modeling Training

Lecture 299 What is Predictive Modelling

Lecture 300 Predictive Modelling

Lecture 301 How to Build A Predicative Model

Lecture 302 Types of Variables

Lecture 303 Difference Between Variables

Lecture 304 Other Types - Extraneous Variables

Lecture 305 How to Build A Predicative Model Steps

Lecture 306 Algorithms

Lecture 307 Forecasting Methods

Lecture 308 What is Time Series

Lecture 309 Smoothing Methods - Moving Averages

Lecture 310 Smoothing Methods - Double Exponential Smoothing

Lecture 311 Regression Algorithms - Exponential

Lecture 312 Clustering Algorithms - Definition

Lecture 313 Clustering Algorithms - Fuzzy C Means Clustering

Lecture 314 Neural Network Algorithm

Lecture 315 Support Vector Machines

Section 9: EViews - Introductory Econometrics Modeling

Lecture 316 Introduction to Eview Training

Lecture 317 Eviews GUI

Lecture 318 Eviews GUI Continues

Lecture 319 Generating Log Returns

Lecture 320 Example of Descriptive

Lecture 321 Interpretation and Graphs

Lecture 322 Interpretation and Graphs Continues

Lecture 323 Generating Log Returns and Descriptive

Lecture 324 Generating Log Returns and Descriptive Continue

Lecture 325 Example of Interpretations

Lecture 326 Volatility Graphs

Lecture 327 Generating returns Interpretation and Graphs

Lecture 328 Generating returns Interpretation Continues

Lecture 329 Basic Correlation Theory

Lecture 330 Generating Correlation Matrix in Eviews

Lecture 331 Generating Correlation Matrix in Eviews Continues

Lecture 332 Mutual Funds Correlation Matrix Percentage

Lecture 333 Scatter Plots Using Eviews

Lecture 334 Generating Correlation Matrix

Lecture 335 Scatter Plots and Volatility Graphs

Lecture 336 Generating Correlation Matrix and Interpretations

Lecture 337 Generating Correlation Interpretations

Lecture 338 Generating Correlation Interpretations Continues

Lecture 339 Scatter Plots

Lecture 340 Working on Scatter Plots

Lecture 341 Basic Regression Modelling Theory

Lecture 342 Generating Returns and Estimation Output

Lecture 343 More on Generating Returns

Lecture 344 Understanding Estimation Output

Lecture 345 Understanding Estimation Output Continues

Lecture 346 Example of Interpretations

Lecture 347 Generating Estimation Output

Lecture 348 Interpretations and Volatility Scatter Plots

Lecture 349 More on Volatility Scatter Plots

Lecture 350 Estimation Output Interpretations and Graphs

Lecture 351 Estimation Output Interpretations and Graphs Continues

Lecture 352 Example 3 - NAV Price Study

Lecture 353 Working on Volatility Graphs

Lecture 354 Correlation Matrix

Lecture 355 Correlation Matrix Continues

Lecture 356 Example 4 - Estimation Output

Lecture 357 Basic Regression Modelling

Lecture 358 Basic Regression Modelling Continues

Lecture 359 Interpretations and Scatterplot Analysis

Lecture 360 More on Scatterplot Analysis

Lecture 361 Equation Estimation

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.