Business Analytics With R - A Comprehensive Guide

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Free Download Business Analytics With R - A Comprehensive Guide
Published: 12/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.09 GB | Duration: 16h 6m
Master business analytics using R to make data-driven decisions with real-world applications and statistical modeling.

What you'll learn
How to use R for business analytics, including data manipulation and statistical analysis.
The business analytics life cycle and how to deploy analytics models.
The fundamentals of statistics, probability, and distributions, including hypothesis testing.
Advanced forecasting techniques like ARIMA and time-series analysis.
How to create compelling data visualizations to communicate insights.
Requirements
Basic knowledge of statistics and business concepts is helpful. No prior programming experience is required, though familiarity with basic programming concepts will be beneficial. A willingness to learn R and apply it to real-world business analytics problems.
Description
Course IntroductionThis course is designed to teach students how to harness the power of R programming for business analytics. Whether you're an aspiring data scientist or a business professional, this course will guide you through every step-from understanding basic data concepts to implementing complex statistical models and machine learning techniques. You'll work with practical examples, data manipulation, visualization, and forecasting, giving you a solid foundation to analyze business data and drive decisions using R.Section-Wise WriteupSection 1: Introduction to Business Analytics and RThe course begins by introducing the concept of business analytics and its evolution in modern business. We start with a discussion on discriminant analysis and move into an introduction to R and its application in business analytics. This section also covers fundamental business examples, such as hotel data, to illustrate how analytics can be applied in real-world scenarios. You will learn about different types of data used in analytics, including ordinal data, and explore decision models used to solve business problems.Section 2: Business Analytics Life CycleThis section dives into the Business Analytics Life Cycle, providing insights into how analytics processes are structured. You'll learn about model deployment, which is critical for turning your models into actionable business strategies. We also explore the steps in the problem-solving process, introduce software commonly used in business analytics, and guide you through setting up R and R Studio for effective use in your analytics projects.Section 3: Understanding R ProgrammingR is the core tool used in this course, and here you'll get a comprehensive introduction to it. The section covers basic R functions, data types, and key concepts such as recycling rules, special numerical values, and logical conjunctions. You will also learn about arrays, matrices, and factors in R, along with how to work with repositories and install packages. The practical aspects of working with data, importing, and aggregating data will be demonstrated.Section 4: Data Manipulation & Statistics BasicsIn this section, you'll focus on data manipulation techniques like merging and data creation, followed by an introduction to basic statistics. You will learn how to compute variance, covariance, and cumulative frequency, while also getting hands-on experience with functions in R like head() and scatterplot(). The section also explores control flow, which helps in making decisions based on data.Section 5: Statistics, Probability & DistributionThis section covers core concepts of statistics and probability necessary for business analytics. You'll learn about random variables, discrete and continuous distributions, and how to calculate expected values. The section also explores binomial distributions and uniform random variables, alongside examples such as gambling and decision-making games like "Deal or No Deal."Section 6: Business Analytics Using RFocusing on advanced business analytics, this section delves into statistical concepts like Normal and t-distributions, along with tools for hypothesis testing. You'll work with real-world examples, such as SAT scores and birth weights, to understand estimation, confidence intervals, and central limit theorem. The section culminates in building confidence intervals and learning about kurtosis, all while gaining practical experience using R.Section 7: Examples, Testing & ForecastingThis section emphasizes hypothesis generation and testing using R. You will work with sample differences, calculate Z values, and perform one-sided P-value tests. Additionally, you will learn about forecasting, time-series analysis, and methods such as ARIMA and double exponential smoothing. These tools are essential for predicting future trends and making informed decisions in business.Section 8: Understanding VisualizationsData visualization is a powerful tool for business analytics, and in this section, you will master how to create effective visual representations of data in R. You'll learn why and how to visualize data, overlay plots, and use advanced graphs such as bubble charts. The section also covers the concept of ANOVA (Analysis of Variance) and regression modeling, providing you with the skills to build and interpret statistical models.ConclusionBy the end of this course, you will have a strong understanding of business analytics concepts and the practical skills to implement them using R. From basic data manipulation and statistical analysis to advanced forecasting and visualizations, this course will prepare you to tackle complex business problems with confidence. You'll be equipped to use R for data-driven decision-making and analysis, giving you the tools to succeed in any business analytics role.
Overview
Section 1: Introduction
Lecture 1 Course Introduction
Lecture 2 Course Curriculum
Lecture 3 Discriminant Analysis
Lecture 4 Introduction to R & Analytics
Lecture 5 Evolution of Business Analytics
Lecture 6 Business Example- Hotel
Lecture 7 Data for Business Analytics
Lecture 8 Ordinal Data
Lecture 9 Decision Model Example
Lecture 10 Descriptive Decision Models
Section 2: Business Analytics Life Cycle
Lecture 11 Business Analytics Life Cycle
Lecture 12 Model deployment
Lecture 13 Steps in Problem Solving Process
Lecture 14 Software used in Business Analytics
Lecture 15 Getting Started with R
Lecture 16 Installing R Studio
Section 3: Understanding R
Lecture 17 Basics of R
Lecture 18 Basic R Functions
Lecture 19 Data Types
Lecture 20 Recycling Rule
Lecture 21 Special Numerical Values
Lecture 22 Parallel Summary Functions
Lecture 23 Logical Conjunctions
Lecture 24 Pasting Strings together
Lecture 25 Type Coercion
Lecture 26 Array & Matrix
Lecture 27 Factor
Lecture 28 Repository & Packages
Lecture 29 Installing a Package
Lecture 30 Importing Data
Lecture 31 Importing Data SPSS
Lecture 32 Working with Data
Lecture 33 Data Aggregation
Section 4: Data Manipulation & Statistics Basics
Lecture 34 Data Manipulation & Statistics Basics
Lecture 35 Merging
Lecture 36 Data Creation
Lecture 37 Merge Example
Lecture 38 What is Statistics
Lecture 39 Variables
Lecture 40 Quantiles
Lecture 41 Calculating Variance
Lecture 42 Calculating Covariance
Lecture 43 Cumulative Frequency
Lecture 44 Library (mass)
Lecture 45 Head (faithful)
Lecture 46 Scatter Plot
Lecture 47 Control Flow
Section 5: Statistics, Probability & Distribution
Lecture 48 Statistics, Probability & Distribution
Lecture 49 Random Variable
Lecture 50 Random Example
Lecture 51 Discrete Example
Lecture 52 Practice problem
Lecture 53 Continuous Case
Lecture 54 Exponential Distribution Practice Problem
Lecture 55 Expected Value
Lecture 56 Gambling Example
Lecture 57 Deal or no deal
Lecture 58 Distribution details
Lecture 59 Binomial Distribution continued
Lecture 60 Expected Value from Binomial
Lecture 61 Uniform Random Variables
Lecture 62 Probability distributions examples
Lecture 63 Probability distributions examples continued
Section 6: Business Analytics using R
Lecture 64 Business Analytics using R
Lecture 65 Normal PDF
Lecture 66 What is Normal, Not Normal
Lecture 67 SAT Example
Lecture 68 Example- Birth Weights
Lecture 69 dNorm, pNorm, qNorm
Lecture 70 Understanding Estimation
Lecture 71 Properties of Good Estimators
Lecture 72 Central Limit Theorem
Lecture 73 Kurtosis
Lecture 74 Constructing Central Limit Theorem
Lecture 75 Confidence Intervals for the Mean
Lecture 76 Confidence Intervals Examples
Lecture 77 Computer Lab Example
Lecture 78 t-distribution
Lecture 79 t-distribution continued
Section 7: Examples, Testing & Forecasting
Lecture 80 R Examples
Lecture 81 Standard error of the mean
Lecture 82 Downloading the Package
Lecture 83 Sample Differences
Lecture 84 Hypothesis Generation & Testing
Lecture 85 Hypothesis Testing
Lecture 86 One sided P Value
Lecture 87 Power & Sample Size
Lecture 88 Testing Hypothesis using R
Lecture 89 Calculating the Z value
Lecture 90 Lower Tail proportion of population proportion
Lecture 91 Forecasting
Lecture 92 Time Series Analysis Applications
Lecture 93 Approaches to Forecasting
Lecture 94 Observation Components
Lecture 95 Traditional Approaches
Lecture 96 Double Exponential Smoothing
Lecture 97 ARIMA Steps
Lecture 98 Forecasting Performance
Lecture 99 Univariate ARIMA
Section 8: Understanding Visualizations
Lecture 100 R Visualization
Lecture 101 Why Visualize
Lecture 102 Overlaying Plots
Lecture 103 Graphs representation of Data
Lecture 104 Graphs representation of Data continued
Lecture 105 Advanced Graphs
Lecture 106 Bubble Charts
Lecture 107 Anova
Lecture 108 Concept of effect
Lecture 109 Estimate of Treatment effect
Lecture 110 Factorial Anova
Lecture 111 Regression
Lecture 112 Regression Model
Lecture 113 Linear Relationship
Lecture 114 Output of Regression Model
Business professionals and analysts looking to use data to drive decisions. Aspiring data scientists or analysts who want to build a career in business analytics. Students or individuals with an interest in learning R programming and applying it to business contexts. Anyone looking to expand their knowledge of statistical analysis and forecasting techniques for business.


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