Mastering Data Science & Statistical Analytics with R: From Foundational Concepts to Machine Learning

What you will learn:

Drive strategic business decisions using robust statistical insights.
Master R programming from fundamental concepts to advanced applications.
Grasp the core principles of probability, random variables, and sample spaces.
Comprehend the properties and applications of continuous and discrete distributions.
Effectively fit statistical distributions to diverse datasets.
Develop and execute powerful business simulations for predictive analytics.
Perform rigorous hypothesis testing to validate various business assumptions.
Interpret and infer meaning from advanced regression models.
Quantify relative risk, odds, and odds ratios in decision-making scenarios.
Implement data-driven strategies for enhanced business outcomes.
Execute comprehensive data cleaning, manipulation, and advanced visualization (ggplot2) techniques.
Apply feature selection and regularized regression models for robust predictive analytics.
Demystify binomial and multinomial logistic regression models and their interpretation.
Accurately detect and effectively manage outliers in your data.
Calculate and interpret essential measures of spread and centrality.
Understand the fundamentals of Bayesian analysis for distribution estimation.
Learn practical machine learning applications using R's cutting-edge tidy models framework.

Description

Latest Update: Machine Learning with R's powerful tidy models framework has been fully integrated into the final chapter, enhancing your practical skillset. (August 2023)

Beyond just coding in R, this course empowers you to leverage advanced statistical methods and machine learning algorithms for impactful decision-making across various business domains!

Having transitioned from traditional Excel-based analysis to the robust capabilities of R six years ago, I've experienced firsthand the transformative power of data science. With over eleven years of diverse experience spanning procurement, academic lecturing, and training more than 2000 professionals in supply chain and data science using both R and Python, I've consolidated my expertise into a thriving consulting business. Now, I'm thrilled to present this comprehensive course, designed with one core objective: to transform you into an expert in R programming, sophisticated statistical thinking, and cutting-edge Machine Learning. This curriculum is a culmination of practical techniques and theoretical insights, packaged as your essential guide to data science with R.

Upon successful completion of this immersive learning journey, you will possess the ability to:

Master R programming from the ground up, even with no prior experience.
Deeply understand foundational concepts of probability, including random experiments, variables, and sample spaces.
Implement robust methods for identifying and handling outliers within complex datasets.
Optimize resource allocation and enhance efficiency through data-driven statistical analysis.
Rigorously test business hypotheses, such as comparing product quality from different suppliers or evaluating the effectiveness of marketing campaigns.
Quantify the precise impact of promotional activities on sales figures using advanced statistical models.
Construct powerful simulations to forecast expected business revenues and understand potential outcomes.
Develop and apply machine learning models for both classification and regression tasks, grounded in statistical principles.
Interpret the intricacies of logistic regression models, including log odds, odds ratios, and their conversion to probabilities.
Select and create appropriate data visualizations (using ggplot2) for both categorical and continuous data to uncover insights.
Capture and quantify data uncertainty using various probability distributions, identifying the best fit for your specific data.
Confidently apply machine learning techniques to solve real-world business challenges.

If these critical questions resonate with your daily professional challenges, then this course is meticulously crafted to be your definitive guide. In today's data-rich environment, a strong statistical and probabilistic foundation is indispensable across critical sectors like finance, marketing, supply chain management, product development, and data science itself. It's the cornerstone for making astute, evidence-based business decisions.

While mastering R syntax is an integral part of this course, our primary emphasis extends beyond mere coding to cultivate your critical thinking skills. We focus on enabling you to profoundly understand and interpret the outputs of statistical and machine learning models, moving beyond just running algorithms. This crucial advantage ensures you're not just a data analyst, but a strategic decision-maker.

This meticulously structured course guides you step-by-step through the world of R and statistics, offering a rich blend of practical exercises, insightful quizzes, downloadable templates, and essential resources. It's designed to solidify your grasp of core R language constructs and fundamental statistical concepts vital for advanced data science and business analytics. Expect a learning experience that is:

Highly Practical and Application-Oriented.
Deeply Analytical and Insight-Driven.
Enhanced with engaging Quizzes and challenging Assignments.
Includes supplementary Excel tutorials for foundational understanding.
Provides comprehensive R scripts and dedicated tutorials.
Designed for easy comprehension and seamless navigation.
Emphasizes active learning ("Learn by Doing") over passive lectures.
Exhaustively Comprehensive in its coverage.
Rooted in Data-Driven methodologies.
Introduces you to the powerful R statistical programming language.
Explores diverse data visualizations using the renowned ggplot2 package.
Equips you with essential skills for cleaning, transforming, and manipulating data efficiently.

I eagerly anticipate welcoming you into this transformative learning experience!

Haytham

Curriculum

Introduction

This introductory section sets the stage for your data science journey. You'll begin with a welcoming overview of the course, learn how to maximize your learning experience, and get a detailed look at the curriculum ahead. We then dive into the various types of analytics, exploring the core objectives and diverse applications of data science across industries. Finally, you'll gain an understanding of the end-to-end data science process and discover why R is a powerful and indispensable language for this field.

Installing R and R Studio

Embark on your practical R journey by setting up your development environment. This section guides you through the process of installing the R statistical language and the user-friendly RStudio integrated development environment. You'll receive a comprehensive walk-through tutorial of RStudio's interface, learn how to effectively set up your projects for efficient workflow, and master the installation of essential R packages. Concluding with a concise summary, you'll be ready to dive into coding with a fully configured setup.

R fundamentals

Build a solid foundation in R programming with this essential section. Starting with a general introduction, you'll explore different data structures (like vectors, matrices, arrays, data frames) and data types in R. Learn to perform arithmetic calculations and write custom functions to automate tasks. We'll cover creating and manipulating lists, and critically, how to import various data formats into R for basic exploration. You'll master selecting and filtering data within data frames, implement conditional logic using `if-else` statements and other conditions, and write efficient `for` loops. The section culminates in applying functions within loops and across data frames, reinforced by practical assignments and detailed solution walkthroughs, ensuring a deep understanding of core R syntax.

Descriptive statistics

Unlock insights from your data using descriptive statistics. This section introduces the fundamental concepts of central tendency (mean, median, mode) and measures of spread (variance, standard deviation, range). You'll learn to calculate these crucial metrics both theoretically and practically in R, gaining hands-on experience. A key focus is placed on methods for effectively detecting and handling outliers in your datasets, ensuring the accuracy and reliability of your statistical analysis. This section also includes an assignment to test your understanding.

Data cleaning and manipulation

Master the art of preparing and transforming your data for analysis with `dplyr`, a powerful R package. This section provides an introduction to `dplyr` and demonstrates how to investigate, filter, select, and summarize data efficiently. You'll learn techniques to identify unique records, calculate aggregate values like average bucket value per country or average items in an invoice, and perform various types of data joins. We cover essential date-time transformations, reshaping data with `pivot_wider` and `pivot_longer`, and text manipulation using `separate` and `paste` functions. The section integrates these techniques through practical 'putting it all together' exercises and detailed assignments based on real-world scenarios like New York airlines data, complete with comprehensive solution explanations.

Visulalization

Transform raw data into compelling visual stories using R's `ggplot2` package. This section introduces the principles of effective data visualization and guides you through creating a diverse range of plots. You'll learn to construct informative line plots, insightful scatter plots, comparative bar plots, detailed distribution plots, revealing box plots, and frequency histograms. Each lecture provides practical demonstrations and best practices. The section includes assignments that challenge you to apply these visualization techniques, followed by step-by-step solution explanations to refine your graphing skills.

Probabilities

Dive deep into the world of probabilities, the backbone of statistical inference. This comprehensive section starts with an introduction to core probability concepts, including variance and standard deviation, and understanding overlapping probabilities. We explore both discrete and continuous probability distributions, providing practical examples and problem-solving exercises. You'll learn about conditional probability and tackle real-world probability questions, including simulations like rolling dice. Key distributions covered include the Binomial distribution (with R implementation and looping examples) and the Poisson distribution (with R examples). The section progresses to continuous distributions like Normal and Uniform, explaining the Central Limit Theorem. Finally, you'll explore associations, calculate relative risk in R, understand correlation matrices, differentiate between cause and effect, and get an introduction to Bayes' Theorem.

Fitting Distributions

Understand how to model and characterize your data by fitting appropriate statistical distributions. This section begins by exploring different distribution shapes and their properties. You'll then learn about Chi-square tests for categorical data, with practical demonstrations in both Excel and R, including multi-part exercises. We'll delve into determining the coverage for a specific percentage of a distribution and apply these concepts through assignments related to bike demand and other real-world scenarios, complete with detailed answer walkthroughs. This section equips you with the skills to identify and fit the best distribution for your data, capturing its underlying patterns.

Simulations

Harness the power of simulations to model real-world scenarios and forecast outcomes. This section introduces simulation concepts and walks you through practical examples. You'll learn to build a restaurant simulation, modeling customer numbers and predicting expected revenue. A dedicated assignment challenges you to apply these simulation techniques, with a concluding summary of insights. The section then progresses to modeling waiting lines, demonstrating how to implement these simulations in both Excel and R. You'll also learn to run and analyze waiting line simulations hundreds of times to understand variability and typical performance.

Simulation with Capacity Constraints

Extend your simulation expertise by incorporating realistic capacity constraints into your models. This advanced section focuses on scenarios like a call center waiting line, where you'll learn to define the optimal 'K' (number of servers/resources) and model the impact of capacity limitations. Through practical assignments and solutions, you'll build robust simulations. We then explore sequential service systems and scenarios with multiple services, demonstrating how to implement these complex multiple-service simulations effectively in R. The section concludes with a comprehensive summary and further assignments to solidify your understanding of advanced simulation techniques.

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