Data Science for Business Part 1
Your Data Science Journey Starts Now! Learn the fundamentals of data science for business with the tidyverse.
Get Started Today!Advance Your Career with Data Science
Accelerate your career by learning data science, the most in-demand skill of this century!
Everything You Need to Become the Data Scientist Level 1 for Your Organization
407 Lessons, 33.9 Hours of Video, and 4 Challenges to Test Your Skills
This course is designed for...
- Business Analysts that want to advance your career with Data Science
- Microsoft Excel Users that are ready to go beyond Excel to a more powerful, data-specific programming language
Example of what you create in the course
Customer Segmentation Report (created in Week 7: Communication)
Machine Learning Foundations
We've streamlined machine learning into 5 hours of video, 44 lessons that you will learn in Week 6 covering all of the major algorithms including:
- Linear Regression
- Generalized Linear Models (GLM)
- Decision Trees
- Random Forest
- XGBoost
- Bonus - Support Vector Machines
Get started now!
Learning data science used to be difficult
So you're a business professional that wants to accelerate your career by learning data science.
This is an excellent decision! Until you try to figure out where to start.
The prospect of learning data science seems daunting. There are an endless series of decisions that need to be made.
"Which language is right for me?"
"What libraries should I use?"
"How much time will this take me?"
"What results will I get?"
"WHERE DO I START?!?!"
We've been there, which is why we've created Business Analysis With R for you.
Taking the guess-work out of learning data science
Business Analysis With R is a revolutionary program that takes the guess-work out of learning data science.
We provide you:
- A complete learning path with R: R is the perfect data science language to learn if transitioning from Microsoft Excel. R is functional, which is very similar to Excel.
- A cohesive tool chain: You use a common programming API called the tidyverse that simplifies the process of importing, joining, cleaning, wrangling, visualizing, modeling, and communicating data & results.
- Comprehensive resources: You are provided cheat sheets, code templates, and resources that speed up learning and make referring back to materials simple.
- A 7-week system that packs in a year of knowledge: It normally takes a year or more to learn the tool chain. You learn everything in 7 weeks w/ 5-10 hours per week of coursework.
- Full life-time access: Once you purchase the course, you gain life-time access to content now and any updates in the future.
We accelerate your learning through a methodical approach combining tools, resources, & projects.
Tools you will learn
You will learn an amazing tool chain called the tidyverse that makes performing data analysis fast and efficient. You'll learn how to use each one of the R packages, enabling you to perform the entire data analysis workflow including:
- Data import
- Joining Data
- Data Cleaning
- Data Manipulation
- Visualization
- Modeling
- Reporting
Resources you will get
The Business Analysis with R course comes packed with resources, tools, frameworks, and templates. Too many to go over. To talk numbers, you get:
- 100+ Code Lectures
- 15+ Hours of recorded content
- 10+ Cheatsheets & Walkthroughs
- 20+ Downloadable code templates
- 2 Projects
- and more!
Projects you will complete
The Business Analysis With R Course incorporates two projects as a guide for our learning:
- R&D Project - Goal: Utilize data to assist the Research & Development department in coming up with a new product idea and a targeted price point
- Marketing Project - Goal: Use data mining techniques to segment the customer base empowering the Marketing department to implement targeted emails to customers in turn increasing engagement
In week 7, you will create two reproducible business reports containing the results of the analysis.
Challenges you will conquer
The Business Analysis with R Course includes a number of challenges where you will apply what you've learned. Shown is the Week 6 Challenge that extends the Customer Segmentation to a real-world challenges where you will segment Companies by their stock price movements.
Summary of what you get
- A methodical training plan that encapsulates 1+ years of knowledge into an accelerated 7 week training - compared to on premise workshop ($5000 value)
- Hundreds of resources ($1000 value):
- 10+ Cheatsheets with Walkthroughs
- 20+ Downloadable Code Templates
- 2 Business Projects
- 4+ Challenges
Adding it up: $6000 value
Purchase today for: 👇
*Price excludes local taxes & VAT
Get started now!
Your Instructor
Founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane startups! Matt loves educating data scientists on how to apply powerful tools within their organization to yield ROI. Matt doesn't rest until he gets results (literally, he doesn't sleep so don't be suprised if he responds to your email at 4AM)!
Course Curriculum
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StartInstalling R & RStudio IDE, Part 1: Installing R (3:06)
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StartInstalling R & RStudio IDE, Part 2: The RStudio IDE (3:03)
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StartRStudio IDE: Setup & Customization (5:40)
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Start🔽 Setting Up The Project (File Download) (2:38)
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StartProject Directory Structure & Contents (5:25)
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StartWindows Users: Install RTools
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Start🔽 Installing R Packages (File Download) (11:47)
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StartPackage Installation Checkpoint
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StartWho Should Watch This?
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PreviewTransactional Data: What Is It? What Will We Do With It In This Course? (1:41)
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Start🔽 Orders: The Building Blocks Of Transactional Data (File Download) (3:53)
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StartDatabase 101: The Entity Relationship Diagram (ERD) (2:14)
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StartUnderstanding Database Relationships (6:18)
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StartImporting Excel Files (6:18)
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StartExamining Data: Console, Data Window, glimpse() (4:27)
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StartData Model (1:12)
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StartJoining Data, Part 1: Combining 2 Tibbles With left_join() (6:04)
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StartJoining Data, Part 2: Combining Multiple Tibbles With The Pipe (5:07)
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Start🔽 Code Checkpoint: Joining Data (File Download)
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StartWrangling Data Overview (3:00)
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StartSplitting Description Into Category 1, Category 2, & Frame Material: separate() (4:35)
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StartSplitting Location Into City & State: separate() (1:55)
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StartAdding Total Price Column: mutate() (2:41)
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StartRemoving Unnecessary Columns: select() (3:59)
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StartGetting The Order ID Column Back: bind_cols() (2:34)
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StartReordering Columns: select() (4:53)
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StartRenaming Columns: rename() & set_names() (5:28)
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StartStoring The Wrangled Data (1:10)
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Start🔽 Code Checkpoint: Wrangling Data (File Download)
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StartSales Analysis Visualizations Overview (1:19)
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StartSales By Year: Data Manipulation (7:23)
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StartSales By Year: ggplot geometries (10:28)
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StartSales By Year: ggplot2 formatting (5:45)
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StartSales By Year & Category 2: Data Manipulation (7:11)
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StartSales By Year & Category 2: ggplot geometries + facet_wrap (6:45)
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StartSales By Year & Category 2: ggplot2 Formatting (5:38)
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Start🔽 Code Checkpoint: Visualizing Data (File Download)
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PreviewImporting Data Overview (0:41)
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StartData Import Cheatsheet (2:44)
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Start🔽 Setup (File Download) (2:04)
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StartCSV Files (7:35)
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StartCSV Files: Fixing Parsing Errors With Column Specifications (3:41)
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StartRDS Files (3:11)
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StartExcel Files (3:50)
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StartDatabase Connection Resources (2:39)
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StartConnecting To Databases: SQLite (8:13)
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Start🔽 Code Checkpoint: Importing Data (File Download)
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StartAggregating Data: group_by() + summarize() (5:18)
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StartSummary Functions (5:48)
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StartDetecting & Handling Missing Values: summarize_all() & filter() (8:20)
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StartRenaming Columns for Presentation: Part1, rename() (4:59)
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StartRenaming Columns For Presentation, Part 2: set_names() (2:40)
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Start🔽 Code Checkpoint: summarize (File Download)
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StartCheat Sheet: lubridate (3:37)
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Start🔽 Setup (File Download) (2:28)
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StartDate Basics (4:39)
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StartCharacter & Date Classes (5:05)
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StartConverters: lubridate basics part 1 (3:37)
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StartExtractors: lubridate basics, part 2 (4:30)
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StartHelpers: lubridate basics, part 3 (1:07)
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StartPeriods & Durations: lubridate basics, part 4 (3:04)
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StartIntervals: lubridate basics, part 5 (5:35)
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StartTime Series Aggregation: group_by() + summarize() (9:29)
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StartTime Series Aggregation: floor_date() (4:27)
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StartMeasuring Change: lag(), part 1 - Annual Change (7:05)
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StartMeasuring Change: lag(), part 2 - Monthly Change (2:48)
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StartMeasuring Change: first(), part 1 - From First Year (3:22)
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StartMeasuring Change: first(), part 2 - From First Month (5:24)
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StartCumulative Calculations: cumsum() (3:21)
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StartCumulative Calculations: Cumulative Percentage (5:31)
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StartRolling Calculations: zoo::rollmean() (7:54)
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StartFiltering Date Ranges (4:24)
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Start🔽 Code Checkpoint: lubridate (File Download)
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StartCheat Sheet: stringr (3:38)
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Start🔽 Text Analysis Setup (File Download) (3:08)
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StartText Detection: str_detect() (5:10)
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StartChanging Case: str_to_upper(), str_to_lower(), & str_to_title() (1:47)
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StartConcatenation, Part 1: str_c() (2:40)
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StartConcatenation, Part 2: str_glue() (6:11)
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StartSeparating Text: tidyr::separate() & str_split() (6:42)
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StartTrimming Text: str_trim() (1:38)
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StartReplacing Text: str_replace() & str_replace_all() (6:23)
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StartFormatting Numbers: scales library (6:22)
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StartFormatting Column Names Programmitcally, Part 1: set_names() (4:46)
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StartFormatting Column Names Programmatically, Part 2: rename_at() (7:12)
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StartFeature Engineering Part 1: Overview (2:30)
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StartFeature Engineering Part 2: Data Cleaning (Fixing Typo No. 1) (4:34)
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StartFeature Engineering Part 3: Separating Model Text (6:55)
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StartFeature Engineering Part 4: Making A Model Base, Pt1 (6:44)
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StartFeature Engineering Part 5: Making A Model Base Pt2 (4:15)
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StartFeature Engineering Part 6: Fixing Typo No. 2 (1:25)
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StartFeature Engineering Part 7: Making A Model Tier Column (2:40)
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StartFeature Engineering Part 8: Remove Unnecessary Columns (1:31)
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StartFeature Engineering Part 9: Fix Missed Model Concatenation (1:45)
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StartFeature Engineering Part 10: Mining Model Tier For Flags, Pt1 (5:52)
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StartFeature Engineering Part 11: Fixing Typo No. 3 (1:21)
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StartFeature Engineering Part 12: Mining Model Tier For Flags, Pt2 (1:31)
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Start🔽 Code Checkpoint: stringr (File Download)
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StartDocumentation: forcats (1:44)
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Start🔽 Categorical Data Setup (File Download) (2:29)
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StartWhy Factors? (3:07)
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StartMotivating Example, Part 1: Visualizing Sales By Secondary Product Category (8:20)
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StartMotivating Example, Part 2: Finishing Up The Sales By Secondary Product Category Visualization (4:43)
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StartFactors & forcats Basics (6:01)
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Startas_factor() vs as.factor() (2:43)
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StartReordering Factors: fct_reorder() & fct_rev() (4:53)
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StartTime-Based Factor Reordering: fct_reorder2() (8:54)
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StartMaking An "Other" Category: fct_lump() & fct_relevel() (6:18)
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Start🔽 Code Checkpoint: forcats (File Download)
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Start🔽 Geometries Setup (File Download) (2:33)
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StartScatter Plot, Part 1: Data Manipulation (4:49)
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StartScatter Plot, Part 2: geom_point() & geom_smooth() (10:21)
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StartLine Plot, Part 1: Data Manipulation (4:27)
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StartLine Plot, Part 2: geom_line() & geom_smooth() (6:35)
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StartBar/Column Plot, Part 1: Data Manipulation (3:11)
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StartBar/Column Plot, Part 2: geom_col() (5:41)
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StartHistogram Plot: geom_histogram() (6:18)
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StartHistogram - Faceted (6:10)
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StartDensity Plot: geom_density() (3:04)
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StartBox Plot: geom_boxplot() (7:11)
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StartViolin Plot: geom_violin() & geom_jitter() (5:19)
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StartText & Labels, Part 1: Data Manipulation (3:36)
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StartText & Labels: geom_text() (7:39)
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StartText & Labels: geom_label() (6:51)
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Start🔽 Code Checkpoint (File Download)
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StartCheat Sheet - ggplot - Page 2 (1:01)
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Start🔽 Formatting Setup (File Download) (5:16)
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StartColors & Color Conversions (5:55)
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StartColor Palettes: tidyquant, Brewer, & Viridis (9:14)
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StartAesthetic Mappings: color (5:56)
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StartAesthetic Mappings: fill (3:33)
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StartAesthetic Mappings: size (3:40)
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StartFaceting (7:06)
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StartPosition Adjustments: Bar Plot, Stacked & Dodge (3:57)
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StartStacked Area: geom_area() (1:21)
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StartScales: Setup (8:22)
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StartScales: Scale Color Continuous (7:22)
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StartScales: Scale Color Discrete (5:57)
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StartScales: Scale Fill Discrete (2:21)
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StartScales: Scale X&Y (4:40)
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StartLabels & Legends: labs() (5:04)
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StartTheme: theme() (9:25)
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StartPutting It All Together (11:05)
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Start🔽 Code Checkpoint (File Download)
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Start🔽 Advanced Business Plotting: Setup (File Download) (1:19)
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StartTop N Customers: Lollipop Plot, Part 1 - Data Manipulation (14:44)
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StartTop N Customers: Lollipop Plot, Part 2 - Geometries (11:07)
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StartTop N Customers: Lollipop Plot, Part 3 - Formatting (10:50)
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StartCustomer Buying Habits: Heatmap, Part 1 - Data Manipulation (8:37)
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StartCustomer Buying Habits: Heatmap, Part 2 - Geometries & Scales (8:37)
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StartCustomer Buying Habits: Heatmap, Part 3 - Labels & Theme (9:59)
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Start🔽 Code Checkpoint (File Download)
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PreviewCheat Sheet: Base R (3:10)
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Start🔽 Setup: Functional Programming (File Download) (3:44)
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StartAnatomy of a Function, Part 1: Why Customize the mean() Function? (3:53)
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StartAnatomy of a Function, Part 2: Customizing the mean() Function (5:41)
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StartExample Data Manipulation: Sales By Year & Category 2 (9:45)
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StartExample Data Visualization: Sales By Year & Category 2 (5:42)
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StartThe 2 Types of Functions: Vectorized vs Data Frame (5:27)
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StartControlling Flow: If Statements, Messages, Warnings, & Errors (9:41)
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Startdetect_outliers(), Part 1: Building A Vectorized Function (3:08)
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Startdetect_outliers(), part 2: Function Setup (3:54)
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StartHow A Box Plot Detects Outliers (1:03)
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Startdetect_outliers(), Part 3: Implement Box Plot Outlier Logic (6:07)
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Startdetect_outliers(), Part 4: Adding a Flag with case_when() (3:07)
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Startdetect_outliers(), Part 5: Testing Our Function (1:23)
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Previewdetect_outliers(), Part 6: Visualizing Outliers (5:24)
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Startseparate_bike_model(), Part 1: A Data Frame Function (8:34)
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Startseparate_bike_model(), Part 2: Testing Our Function (2:38)
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StartSaving Functions, Part 1: Creating Files & Folders (2:16)
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StartSaving Functions, Part 2: Creating a header with write_lines() (4:12)
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StartSaving Functions, Part 3: Writing Functions with dump() (1:31)
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StartSaving Functions, Part 4: Loading Functions with source() (1:32)
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Start🔽 Checkpoint: Functional Programming (File Download)
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PreviewCheat Sheet: purrr (5:16)
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Start🔽 Setup: Iteration with purrr (File Download) (3:39)
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Startpurrr primerrr, Part 1: Reading Many Excel Files in a Directory (3:08)
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Startpurrr primerrr, Part 2: For Loop (3:26)
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Startpurrr primerrr, Part 3: map() (6:23)
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Startpurrr primerrr, Part 4: Reading Multiple Excel Sheets in an Excel File (2:20)
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StartMapping Over Data Frames (10:01)
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StartNested Data, Part 1: unnest() (5:42)
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StartNested Data, Part 2: nest() (1:33)
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StartMapping Nested Data, Part 1: Create Function that Works on 1 Element (4:21)
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StartMapping Nested Data, Part 2: Scale with mutate() + map() (6:21)
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StartModeling with purrr, Part 1: LOESS Smoother for Time Series (4:58)
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StartModeling with purrr, Part 2: Make a LOESS model with loess() (7:31)
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StartModeling with purrr, Part 3: Intro to broom, augment() (3:42)
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StartModeling with purrr, Part 4: Creating a tidy_loess() function for mapping (8:53)
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PreviewModeling with purrr, Part 5: Mapping tidy_loess() to all Categories (8:10)
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Start🔽 Checkpoint: Iteration with purrr (File Download)
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Start🔽 Customer Segmentation with K-Means Clustering Setup (File Download) (2:53)
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StartAnalyzing Customer Trends & The User-Item Matrix (2:17)
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StartSetting Up Customer Trends, Part 1: Aggregation (7:44)
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StartSetting Up Customer Trends, Part 2: Normalization (3:42)
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StartUser-Item Matrix (3:50)
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PreviewK-Means Clustering for Customer Segmentation: Algorithm Overview (4:23)
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Previewkmeans() Function (6:56)
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Startbroom: Tidy the kmeans() Output (5:44)
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Startpurrr: Cluster Iteration with map() (8:37)
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StartScree Plot: Visualize & Evaluate K-Means Clusters (7:36)
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StartK-Means Recap (1:30)
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PreviewUMAP: High-Performance Dimension Reduction (1:09)
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Startumap() Function (8:36)
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StartCombining UMAP & K-Means Results (4:48)
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StartCustomer Segment Visualization (6:36)
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StartCustomer Segment Purchasing Trends Analysis, Step 1: Joining Clusters (3:15)
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StartCustomer Segment Purchasing Trends Analysis, Step 2: Binning Price (5:47)
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StartCustomer Segment Purchasing Trends Analysis, Step 3: Rearranging Columns (2:58)
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StartCustomer Segment Purchasing Trends Analysis, Step 4: Aggregation (2:29)
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StartCustomer Segment Purchasing Trends Analysis, Step 5: Normalization (1:46)
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StartCustomer Segment Purchasing Trends Analysis, Step 6: Cluster 1 (4:01)
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StartCustomer Segment Purchasing Trends Analysis, Step 7: Clusters 2-4 (4:39)
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StartCustomer Segment Purchasing Trends Analysis, Step 8: Visualization (8:03)
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Start🔽 Code Checkpoint (File Download)
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Start🔽 Regression Modeling Setup (FIle Download) (9:31)
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StartDocumentation: parsnip, rsample, recipes, & yardstick (8:19)
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StartBusiness Problem Review: Product Gaps (7:23)
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StartTrain / Test Part 1: Data Preparation & Feature Engineering (4:37)
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StartTrain / Test Part 2: Splitting the Data with rsample::initial_split() (8:06)
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StartLinear Regression - Theory Explained
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StartParsnip Model List (Amazing Resource) (1:56)
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StartLinear Regression, Part 1 (Model 01): The parsnip::linear_reg() function (8:14)
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StartLinear Regression, Part 2 (Model 01): The predict() function (2:29)
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StartLinear Regression, Part 3 (Model 01): Calculating Model Metrics (6:49)
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StartModel Interpretability for Linear Models - How it works
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StartLinear Regression, Part 4 (Model 01): Model Explanation (11:37)
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StartCalculating Metrics: Model Helper (3:44)
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StartLinear Regression Complex (Model 02): Adding Engineered Features (6:44)
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StartLinear Regression Complex (Model 02): Model Explanation (4:51)
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StartGLM Regularized Regression: Theory Explained
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StartGLM Regularized Regression (Model 03): GLMNET (Elastic Net) (9:01)
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StartGLM Regularized Regression (Model 03): Model Explanation (3:53)
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StartTree-Based Methods & Parsnip Documentation (2:37)
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StartDecision Trees: Theory Explained
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StartDecision Trees (Model 04): rpart (8:39)
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StartDecision Trees (Model 04): Model Explanation with rpart.plot() (8:57)
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StartRandom Forest: Theory Explained
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StartRandom Forest (Model 05): ranger (12:01)
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StartRandom Forest (Model 05): Ranger Model Explanation (7:20)
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StartRandom Forest (Model 06): randomForest (3:26)
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StartRandom Forest (Model 06): randomForest Model Explanation (5:22)
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StartReproducibility: set.seed() (2:09)
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StartGradient Boosted Machines (GBM): Theory Explained
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StartGradient Boosted Machines (Model 07): XGBoost (7:53)
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StartMini Challenge: Tune Your XGBoost Model (0:31)
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StartMini Challenge Solution: Tune Your XGBoost Model (1:23)
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StartGradient Boosted Machines (Model 07): XGBoost Model Explanation (5:05)
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StartPreprocessing Pipelines with recipes (10:03)
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StartApplying Transformations & Getting Step Information with tidy() (4:14)
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StartSupport Vector Machine (SVM): Theory Explained
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StartSupport Vector Machine (Model 08): kernlab (7:46)
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StartSupport Vector Machine (Model 08): Tuning the Model (3:32)
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StartSVM (Model 08): Testing on New Bike Models (6:32)
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PreviewRMarkdown Cheat Sheet (5:58)
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Start🔽 Setup - RMarkdown (File Download) (8:30)
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StartYAML: Controlling Your Document Properties (10:11)
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StartKnitr Global Options, Part 1: echo, eval, results, fig.keep (7:42)
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StartKnitr Global Options, Part 2: message, warning, dpi, and more (4:00)
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StartRMarkdown: Used for Reports, Websites, Books, Apps, & More! (3:23)
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StartRMarkdown: Key Resources To Get Started (1:33)
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StartRMarkdown: Headers, Text, & Lists (4:05)
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StartRMarkdown: Tabsets & Images (6:50)
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StartRMarkdown: Code Chunks (3:32)
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StartRMarkdown: Plotting (3:25)
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StartRMarkdown: Tables & Footnotes (5:38)
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Previewplot_total_sales(): A Custom Interactive Plotting Function (1:52)
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StartPreparing Data for Plotting (5:30)
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StartString-Format-Time Expressions: strftime Cheat Sheet (2:18)
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StartFormatting Time Stamps with strftime Expressions (5:11)
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StartMaking the Interactive Plot with plotly & ggplot2 (8:35)
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Startplot_total_sales(), Part 1: Setting up the custom time series function (6:39)
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Startplot_total_sales(), Part 2: Integrating ggplot() & ggplotly() (5:00)
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Startplot_categories(): A Custom Interactive Faceted Plotting Function (1:38)
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StartPreparing Data For Plotting (5:43)
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StartMaking the Interactive Plot with plotly & ggplot2 (8:51)
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Startplot_categories(), Part 1: Handling the Data (5:33)
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Startplot_categories(), Part 2: Handling the Inputs & Filter Logic (7:21)
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Startplot_categories(), Part 3: Generating the Interactive Plot (5:23)
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StartSaving Our Functions (1:59)
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Start🔽 Code Checkpoint (File Download)
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StartBuilding an Interactive Sales Report - HTML & PDF - RMarkdown & Plotly (1:40)
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StartRMarkdown Setup (2:35)
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StartYAML Setup (5:43)
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StartKnitr Global Options (3:01)
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StartPlotting Function Setup - RMarkdown (5:46)
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StartReport: Total Sales Section (8:39)
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StartReport: Road Bikes Section (6:00)
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StartReport: Mountain Bike Section (1:17)
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StartConverting to PDF Format (2:16)
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Start🔽 Code Checkpoint (File Download)
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PreviewNew Product Pricing Report - What You're Building! (1:19)
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Start🔽 Setup (File Download) (4:45)
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PreviewData Science Report Structure - How to Communicate Data Science Effectively (2:10)
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StartGap Analysis, Part 1: Bike List & get_bike_features() (7:12)
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StartSaving Functions with dump() & Loading Functions with source() (2:17)
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StartGap Analysis, Part 2: Analyzing the Product Gaps & plot_bike_features(), Part 1 (8:52)
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Startplot_bike_features(), Part 2: Formatting the Plot (6:47)
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StartSaving Our Functions & Re-Knitting Our Report (3:59)
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StartPrice Prediction Algorithm, Part 1 (4:56)
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StartPrice Prediction Algorithm, Part 2 (6:06)
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StartFormatted Tables: knitr::kable() (2:10)
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StartSolution Summary (3:03)
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Start🔽 Code Checkpoint (File Download)
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PreviewCustomer Segmentation Report - Yep, you're going to build this! (2:18)
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Start🔽 Setup (File Download) (3:34)
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StartReport Overview - Customer Segementation (2:16)
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StartHeat Map, Part 1 - Data Manipulation (7:35)
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StartHeat Map, Part 2 - Static Visualization with ggplot2 (8:32)
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StartHeat Map, Part 3 - Interactive Plot with ggplotly() (6:10)
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StartSaving Functions & Adding Your Analysis To The Report (4:48)
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StartCustomer Segmentation, Part 1 - Customer Trends (7:18)
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StartCustomer Segmentation, Part 2 - K-Means Clustering (4:51)
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StartCustomer Segmentation, Part 3 - UMAP (6:02)
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StartCustomer Segmentation, Part 4 - Combining K-Means & UMAP (2:54)
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StartCustomer Segmentation, Part 5 - Plotting Function (9:10)
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StartSaving Functions & Adding Your Analysis to the Report (2:45)
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StartCustomer Preferences, Part 1 - Overview (1:25)
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StartCustomer Preferences, Part 2 - Data Manipulation 1 (6:33)
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StartCustomer Preferences, Part 3 - Data Manipulation 2 (5:50)
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StartCustomer Preferences, Part 4 - Visualization 1 (9:47)
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StartCustomer Preferences, Part 5 - Visualization 2 (5:22)
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StartSaving & Loading the Functions - dump() & source() (1:53)
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StartCompleting the Analysis - Customer Preferences Statement & Solution Summary (6:02)
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Start🔽 Code Checkpoint (File Download)