What You’ll Discover in DS4B 101-R Business Analysis With R
DS4B 101-R – Business Analysis With R
Take the Business Analysis With R Course is equivalent to:
50+ tools-Courses that are based on real life
2 ends-To-End projects
2 frameworks
1 Foundational Data Science Education
1 clear-You can learn data science in 7 weeks. This is a cut-through on the path that would normally take you 1 year+.
This course is for…
Business Data Science is a great way to grow your career as an analyst
Microsoft Excel users who are prepared to move beyond Excel to more powerful data-Specific programming languages
Exemplary of what you create during the course
Customer Segmentation Report (created during Week 7: Communications)
Machine Learning Foundations
Machine learning has been simplified into five hours of video. Week 6 will include 44 lessons covering all the major algorithms, including:
Linear Regression
Generalized Linear Models (GLM)
Decision Trees
Random Forest
XGBoost
Bonus – Support Vector Machinery
Get ed now!
One Purchase
1X Payment
$399
3 Monthly Low Payments
3X Payments
3 monthly payments of $159
6 Low Monthly Payments
6X Monthly
6 payments of $85/month
It used to be hard to learn data science
If you are a business professional looking to advance your career, then data science is for you.
This is a wonderful decision! You can’t figure out where you should go until then.
It seems impossible to imagine learning data science. There are so many decisions to make.
“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 ?!?!”
We have been there and that is why we created it. Business Analysis With R For you.
Take the guess-Learn data science and make it your profession
Business Analysis With R It is a revolutionary program that eliminates guesswork-Work out of data science learning.
We offer the following:
Complete learning path R: R If you are moving from Microsoft Excel, this is the ideal data science language to learn. R Functional, which is very similar with 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: These resources include cheat sheets, code templates and other resources that will speed up learning. They also make it easy to refer back to the materials.
A 7-A week system that packs in an entire year of knowledge. It typically takes one year to learn the tool chains. Everything is covered in seven weeks with 5 instructors.-Coursework is completed for 10 hours per week.
Full life-Access to the course at any time: After you purchase it, you get access for life-Access to the content at any time, including updates.
We accelerate your learning through a methodical approach combining tools, resources, & projects.
You will be able to learn tools
This amazing tool chain, the tidyverse, will help you perform data analysis quickly and efficiently. Each tool will be described. R Packages that allow you to complete the data analysis workflow, including:
Data import
Joining Data
Data Cleaning
Data manipulation
Visualization
Modeling
Reporting
You will find resources
The Business Analysis With R Courses come packed with tools, templates, frameworks, resources and tools. There are too many to mention. Talk numbers and you will get:
100+ Code Lectures
Recorded content for 15+ hours
10+ Cheatsheets & Walkthroughs
20+ Free Downloadable Code Templates
2 Projects
You can find out more about it!
Complete these projects
The Business Analysis With R As a guide, our course includes two projects:
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 for segmenting the customer base. This allows the Marketing department to send targeted emails to customers, increasing engagement.
You will create two reproducible business documents that contain the results of your analysis in week 7.
Take on the challenges
The Business Analysis With R This course will teach you how to apply what you have learned. The Week 6 Challenge extends Customer Segmentation to a real world.-World challenges that you can segment Companies by stock price movements
Summary of what do you get?
An accelerated seven-week training that integrates 1+ years’ knowledge. This is a more efficient training method than an on-premise workshop ($5000).
There are hundreds of resources worth $1,000
10+ Cheatsheets and Walkthroughs
20+ Code Templates You Can Download
2 Business Projects
4+ Challenges
Add it up: $6000 value
Buy Today for $399
Get ed now!
One Purchase
1X Payment
$399
3 Monthly Low Payments
3x Payments
3 monthly payments of $159
6 Low Monthly Payments
6X Monthly
6 monthly payments of $85
Your instructor
Matt Dancho
Matt Dancho
Download immediately DS4B 101-R – Business Analysis With R
The founder of Business Science and general business & finance guru, He has worked with many clients from Fortune 500 to high-octane ups! Matt enjoys teaching data scientists how to use powerful tools within their organizations to increase ROI. Matt is relentless in his pursuit of results and doesn’t stop until he achieves them.
Course Curriculum
We are glad you came to Business Analysis With R (DS4B 101-R)!
Your Journey To Learning R For Business s Now! (2:33)
Instructions for Course Certificate
How to join the Private Slack channel
Prerequisites
Prerequisites
How to Get Help
Getting Help (IMPORTANT !!!)
Week 1: Jump
Week 1 Overview (1.33)
1.1 Goal: Improve Customer Service With Data
The Business Case (0.54)
1.2 What Tools Are in Our Toolbox?
The Ultimate R Cheatsheet (File download) (2:14
How to Use The Ultimate R Cheatsheet (2.39)
1.3 Data Science Project Settingup
Installation R & RStudio IDE, Part 1: Installation R (3:06)
Installing R & RStudio IDE, Part 2: The RStudio IDE (3:03)
RStudio IDE: Setup & Customization (5:40)
Setting up the Project (File Download). (2:38).
Project Directory Structure & Contents (5:25)
Installation R Packages (File download) (11:47).
Package Installation Checkpoint
1.4 Optional – Transactional Database Primer
Who Should Be Watching This?
What is transactional data? What Can We Do? With Is It in This Course? (1:41)
The Building Blocks of Transactional Data (File Download).
Database 101 (ERD) (2)
Understanding Database Relationships (6.18)
1.5 Sales Analysis – Go Diving!
Check This Out! Jump Intent
Overview (1:27).
Setup (File download) (4:40).
1.5.1 Sales Analysis, Part 1 – Importing, Examining, & Joining Data
Importing Excel Files (6:18)
Examining Data: Console, Data Window, glimpse() (4:27)
Data Model (1:12)
Joining Data, Part 1: Combining 2 Tibbles With left_join() (6:04)
Joining Data, Part 2: Combining Multiple Tibbles With The Pipe (5.07)
Code Checkpoint: Joining Data (File Download)
1.5.2 Sales Analysis, Part 2 – Wrangling Data With dplyr
Wrangling Data Overview (3:00)
Splitting Description Into Category 1, Category 2, & Frame Material: separate() (4:35)
Splitting Location Into City & State: separate() (1:55)
Adding Total Price Column: mutate() (2:41)
Select: Removing unnecessary columns() (3:59)
Getting the Order ID Column Back: Bin_cols() (2:34)
Select Column Reordering:() (4:53)
Renaming Columns: rename() & set_names() (5:28)
Storing the Wrangled Data (1.10)
Code Checkpoint: Wrangling Data (File Download)
1.5.3 Sales Analysis, Part 3 – Visualizing Data With ggplot2
Sales Analysis Visualizations Overview (1.19)
Sales By Year: Data Manipulation (7:23)
Sales By Year: ggplot geometries (10:28)
Sales By Year: ggplot2 formatting (5:45)
Sales By Year & Category 2: Data Manipulation (7:11)
Sales By Year & Category 2: ggplot geometries + facet_wrap (6:45)
Sales By Year & Category 2: ggplot2 Formatting (5:38)
Code Checkpoint: Visualizing Data (File Download)
1.5.4 Visualization Process & Saving Key Data
Data Visualization Process (1:38)
Writing Files Excel, CSV and RDS (7.32)
Code Checkpoint: Writing Files (File Download)
Week 2: Import & Data Wrangling Foundations (Level 1)
Week 2 Overview (1.41)
Important Concepts Before You Go Diving
Data Type & Structure Basics (File Download) (7:01)
Tidy data primer (File Download), (8:20)
2.1 Importing Data: readr, readxl, odbc, & RSQLite
Overview of Importing Data (0:41).
Data Import Cheatsheet (2.44)
Setup (File Upload) (2.04)
CSV Files (7:35)
CSV Files: Fixing Parsing Errors With Column Specifications (3.41)
RDS Files (3:11)
Excel Files (3:50)
Database Connection Resources (2.39)
Connecting to Databases: SQLite (8.13)
Code Checkpoint: Importing Data (File Download)
2.2 Wrangling Data (Cleaning, Preparing, & Manipulating Data): dplyr & tidyr
Data Wrangling Overview (1:41)
Setup (File download) (1:19).
Data Wrangling Outline (2:19)
Examining the Data (1.35)
2.2.1 Using THE ULTIMATE R Cheatsheet For Data Wrangling
Use The R Cheatsheet For Data Wrangling (5:10)
2.2.2 With Columns (Features).
Select Columns() & select helpers (8:02)
Take Vectorized Contents() (2:56)
Select Data by Data Type: select_if() (3:31)
Sorting Columns() (2:59)
Code Checkpoint: select (File Download)
2.2.3 With Rows (Observations).
Filtering Rows() (5:58)
Filtering Categorical Information: %in% and == (6:00)
Filtering by row number: slice() (3:45)
Filtering Unique Observations: distinct() (3:39)
Code Checkpoint: filter (File Download)
2.2.4 Performing Feature-Based Calculations
Add Calculated Columns: Mutate() (8:36)
Binning Numeric data: mutate() + ntile() (2:29)
If-Statements Inside Mutate: then() + case_when() (9:40)
Code Checkpoint: mutate (File Download)
2.2.5 Performing Summary Calculations, Analyzing Groups, & Renaming Columns for Presentation
Aggregating Data: group_by() +() (5:18)
Summary Functions (5.48)
Detecting & Handling Missing Values: summarize_all() & filter() (8:20)
Part1: Renaming columns to present: rename() (4:59)
Part 2: Renaming columns for presentation, set_names() (2:40)
Code Checkpoint: summarize (File Download)
2.2.6 Reshaping Data (Pivoting)
Spread from Long to Wide Format() (4:59)
From wide to long: gather() (6:56)
Code Checkpoint: Pivoting (File Download)
2.2.7 Combining Data (Joining & Binding)
Joining Data() (4:59)
Binding Data: binding_cols() & bind_rows() (6:05)
Code Checkpoint: Combining Data (FIle Download)
2.2.8 Splitting & Combining Column Text
Separate Manipulating Text in Columns() & unite() (7:26)
Code Checkpoint: Splitting & Combining Text
2.3 Week 2 Challenge
Week 2 Challenge (File Download) (8:00)
Week 2 Challenge – Solution (File Download) (10:42)
The Directory Organization Challenges (2.49)
Week 3: Data Wrangling Foundations Level 2
Data Wrangling Level 2 Overview (2:02)
3.1 Time Series Fundamentals: lubridate
Cheat Sheet: Lubridate (3.37)
Setup (File download) (2:28).
Date Basics (4:39)
Character & Date Classes (5:05)
Converters: lubridate basics part 1 (3:37)
Extractors: Lubridate basics, Part 2 (4.30)
Helpers: Lubridate basics, Part 3 (1:07).
Periods & Durations: lubridate basics, part 4 (3:04)
Intervals: Lubridate basics, Part 5 (5:35).
3.2 Time Series Analysis: The Most Common Business Operation
Time Series Aggregation – group_by() +() (9:29)
Time Series Aggregation floor_date() (4:27)
Measuring change: lag(), Part 1 – Annual change (7:05).
Measuring change: lag()Part 2 – Monthly Change (2.48)
Measurement of Change: First(), Part 1 – From the First Year (3:22).
Measurement of Change: First()Part 2 – Beginning the First Month (5.24).
Cumulative Calculations() (3:21)
Cumulative Calculations – Cumulative Percentage (5.31)
Rolling Calculations: zoo::rollmean() (7:54)
Filter Date Ranges (4.24)
Code Checkpoint: lubridate (File Download)
3.3 Text Fundamentals: stringr
Cheat Sheet stringr (3.38)
Text Analysis Setup (File download) (3:08).
Text Detection: str_detect() (5:10)
Str_to_upper – Changing Case(), str_to_lower(), & str_to_title() (1:47)
Concatenation, Part 1: str_c() (2:40)
Concatenation, Part 2: str_glue() (6:11)
Separating Text: tidyr::separate() & str_split() (6:42)
Trimming Text: str_trim() (1:38)
Replacing Text: str_replace() & str_replace_all() (6:23)
Formatting Numbers: Scales Library (6:22).
Part 1: Setting up Column Names Programmitcally.() (4:46)
Part 2: Rename_at() (7:12)
3.4 Text Analysis: Feature Engineering Case Study
Download immediately DS4B 101-R – Business Analysis With R
Feature Engineering Part 1: Overview (2:30)
Feature Engineering Part 2: Data Cleaning (Fixing Typo No. 1) (4:34)
Feature Engineering Part 3: Separating Model Text (6:55)
Feature Engineering Part 4: Making A Model Base, Pt1 (6:44)
Feature Engineering Part 5: Making A Model Base Pt2 (4:15)
Feature Engineering Part 6: Fixing Typo No. 2 (1:25)
Feature Engineering Part 7: Making A Model Tier Column (2:40)
Feature Engineering Part 8. Remove Unnecessary columns (1:31).
Feature Engineering Part 9 – Fix Missed Concatenation (1.45)
Feature Engineering Part 10: Mining Model Tier For Flags, Pt1 (5:52)
Feature Engineering Part 11: Fixing Typo No. 3 (1:21)
Feature Engineering Part 12: Mining Model Tier For Flags, Pt2 (1:31)
Code Checkpoint: stringr (File Download)
Categorical data fundamentals: forcats
Documentation: forcats (1:44)
File Download: Categorical Data Setup (2.29)
Why Factors? (3:07)
Motivating Example, Part 1 – Visualizing Sales by Secondary Product Category (8.20)
Motivating Example Part 2: Finalizing the Sales By Secondary Product category Visualization (4:43).
Factors & forcats Basics (6:01)
as_factor() Vs. as.factor() (2:43)
Reordering factors: fct_reorder() & fct_rev() (4:53)
Time-Based Factor Reordering – fct_reorder2() (8:54)
Making an “Other” Category: fct_lump() & fct_relevel() (6:18)
Code Checkpoint: forcats (File Download)
Week 3 Challenges
Week 3 Challenge (File Download) (7:18)
Week 3 Challenge – Solution (File Download) (23:01)
Week 4: Data Visualization using ggplot2
Data Visualization – Overview (2:59)
ggplot2 Cheat sheet: Page 1, Geometries (5.34)
ggplot2 Cheat Sheet: Page 2, Formatting (3:49)
4.1 Anatomy Of A ggplot
Anatomy (File Download)
Generating A ggplot2, Part 1: Data & Geoms (9:37)
Generating A ggplot2, Part 2: Formatting (5:41)
Anatomy and function of a ggplot2 object: What is g, you ask? (3:30)
4.2 ggplot2 Geometries
Geometries setup (File Download). (2:33)
Part 1 of Scatter Plot: Data Manipulation (4.49)
Scatter Plot, Part 2: geom_point() & geom_smooth() (10:37)
Line Plot, Part 1 Data Manipulation (4.27)
Line Plot, Part 2: geom_line() & geom_smooth() (6:35)
Part 1: Data Manipulation (Bar/Column Plot) (3:11
Bar/Column Plot, Part 2: geom_col() (5:41)
Histogram Plot: geom_histogram() (6:18)
Histogram – Faceted (6:10)
Geom_density Density Plot() (3:04)
Box Plot: geom_boxplot() (7:11)
Violin Plot: geom_violin() & geom_jitter() (5:19)
Text & Labels, Part 1: Data Manipulation (3:36)
Text & Labels: geom_text() (7:39)
Text & Labels: geom_label() (6:51)
Code Checkpoint (File Download)
4.3 ggplot2 Formatting
Cheat Sheet- ggplot Page 2 (1:01).
Formatting Setup File Download (5:16).
Colors & Color Conversions (5:55)
Color Palettes: tidyquant, Brewer, & Viridis (9:14)
Aesthetic Mappings: color (5:56)
Fill (3:33)
Size of Aesthetic Mappings (3:40).
Faceting (7:06)
Position Adjustments: Bar Plot, Stacked & Dodge (3:57)
Stacked Area: geom_area() (1:21)
Scales Setup (8.22)
Scales: Continuous Scale Color (7:22)
Scales: Scale Color Discrete (5:57)
Scales: Scale Fill Discrete (2:21).
Scales: Scale X&Y (4:40)
Labels & Legends: labs() (5:04)
Theme: theme() (9:25)
Putting It All Together (11.05)
Code Checkpoint (File Download)
Advanced Business Plotting using ggplot2
Advanced Business Plotting – Setup (File Upload) (1.19)
Top N Customers: Lollipop Poplo, Part 1 – Data Manipulation
Top N Customers: Lollipop Part 2 – Geometries (1:07)
Top N Customers: Lollipop Plot, Part 3 – Formatting (10:50)
Customer Buying Habits: Heatmap Part 1 Data Manipulation (8.37).
Customer Buying Habits: Heatmap, Part 2 – Geometries & Scales (8:37)
Customer Buying Habits: Heatmap, Part 3 – Labels & Theme (9:59)
Code Checkpoint (File Download)
Week 5: Functional Programming & Iteration with purrr
Functional Programming & Iteration – Overview (0:59)
5.1 Functional Programming
Cheat Sheet: Base R (3:10)
Setup: Functional Programming (File download) (3:44
Anatomy and Function, Part 1: Why Customize Your Mean() Function? (3:53)
Part 2: Customizing the meaning of Anatomy Of A Function() Function (5:41).
Example Data Manipulation: Sales By Year & Category 2 (9:45)
Example Data Visualization: Sales By Year & Category 2 (5:42)
The 2 Types Functions: Vectorized or Data Frame (5.27)
Controlling Flow: If Statements, Messages, Warnings, & Errors (9:41)
detect_outliers()Part 1: Building an Automated Function (3:08).
detect_outliers()Part 2: Function Setup (3.54)
How A Box Plot Detects outliers (1:03).
detect_outliers(), Part 3 : Implement Box plot Outlier Logic (6.07)
detect_outliers(), Part 4 – Adding a Flag to case_when() (3:07)
detect_outliers()Part 5: Testing Our Function (1.23)
detect_outliers(), Part 6: Visualizing Outliers (5:24)
separate_bike_model(), Part 1: Data Frame Function (8.34)
separate_bike_model()Part 2: Testing Our Function (2.38)
Saving Functions, Part 1: Creating Files & Folders (2:16)
Part 2: Saving Functions, Part 3: Creating a header using write_lines() (4:12)
Part 3: Saving Functions, Writing Functions with dump() (1:31)
Part 4: Saving Functions, Loading Functions With Source() (1:32)
Checkpoint: Functional programming (File Download).
5.2 Iteration using purrr
Cheat Sheet: purrr (5.16)
Setup: Iteration (File Download). (3:39).
Purrr primerrr Part 1: Reading Many Excel Files within a Directory (3.08)
purrr primerrr, Part 2: For Loop (3:26)
purrr primerrr, Part 3: map() (6:23)
purrr primerrr Part 4: Reading Multiple Excel sheets in an Excel File (2.20).
Mapping Over Data Frames (10.01).
Nested Data, Part 1: unnest() (5:42)
Nested Data, Part 2: nest() (1:33)
Part 1 of Mapping Nested data: Create Functions That Work on One Element (4.21).
Part 2 of Mapping Nested Data: Scale with Mutate() + Map() (6:21)
Part 1 of Modeling with Purrr, Part 2: LOESS Smoother to Time Series (4.58)
Part 2: Modeling with purrr with loess() (7:31)
Modeling with purrr: Part 3: Intro To Broom, augment() (3:42)
Part 4: Modeling with purrr: Making tidy_loess() function for mapping (8.53)
Part 5: Modeling with purrr: Mapping tidy_loess() All Categories (8:10)
Checkpoint: Iteration using purrr (File download)
Week 6: Part 1 of Modeling – K-Means Clustering & UMAP
Modeling, Part 1 – Overview – K-Means & UMAP (0:46)
Clustering & Dimensionality Reduction – Key Concepts & Theory Explained
Download Cheat Sheet: Segmentation, Clustering (File Download). (0:48).
Segmentation & Clustering Workflow (9:22)
6.1 Customer Segmentation using K-Means Clustering & UMAP
Customer Segmentation using K-Means Clustering Setup (File download) (2:53
Analyzing Customer Trends & The User-Item Matrix (2.17)
Setting Up Customer Trends, Part 1: Aggregation (7:44)
Setting Up Customer Trends, Part 2: Normalization (3:42)
User-Item Matrix (3.50).
K-Means of Clustering for Customer Segmentation: Algorithm Overview (4.23)
kmeans() Function (6:56).
broom: Clean the kmeans() Output (5.44)
purrr: Cluster Ieration with map() (8:37)
Scree Plot: Visualize & Evaluate K-Means Clusters (7.36)
K-Means Recap (1:30)
UMAP: High-Performance Dimension Reduction (1.09)
umap() Function (8:36).
Combining UMAP & K-Means Results (4:48)
Customer Segment Visualization (6:36)
Customer Segment Purchasing Trends Analysis, Step 1: Joining Clusters (3:15)
Customer Segment Purchasing Trends AnalysisStep 2: Setting the Binning Price (5.47)
Customer Segment Purchasing Trends Analysis, Step 3 – Rearranging Columns (2.58)
Customer Segment Purchasing Trends AnalysisStep 4: Aggregation (2.29)
Customer Segment Purchasing Trends Analysis, Step 5: Normalization (1:46)
Customer Segment Purchasing Trends Analysis, Step 6: Cluster 1 (4:01)
Customer Segment Purchasing Trends Analysis, Step 7: Clusters 2-4 (4:39)
Customer Segment Purchasing Trends Analysis, Step 8: Visualization (8:03)
Code Checkpoint (File Download)
Week 6: Challenge 1 — Company Segmentation and Stock Prices
Challenge Setup: Overview & Data (File Download) (6:46)
Questionnaire (8:37)
Challenge Bonus – Exploring Clusters using an Interactive Plot (0.51)
Challenge Solution (File Download) (25:21)
Week 6: Modeling, Part 2 – Machine Learning (Regression)
Learning Plan – Zero to machine learning pro in hours (not years)
Cheat Sheet: Machine Learning For Regression (File download) (2:24).
6.2.1 Machine Learning Concepts
Download immediately DS4B 101-R – Business Analysis With R
Introduction to Machine Learning: Key Concepts Explained
Machine Learning Summary & Terminology (9:16)
Machine Learning: Model List & Model Overview (11:18)
6.2.2 Business Problem & Data Preparation
Regression modeling setup (FIle Download). (9:31).
Documentation: parsnip, rsample, recipes, & yardstick (8:19)
Business Problem Review: Product Gaps (7:23)
Train / Test Part 1: Data Preparation & Feature Engineering (4:37)
Train / Test Part 2: Splitting the Data with rsample::initial_split() (8:06)
6.2.3 Linear Algorithms
Linear regression – Theory explained
Parsnip Model List (Amazing Resource) (1:56)
Linear Regression Part 1 (Model 01), The Parsnip::linear_reg() Function (8:14).
Linear regression, Part 2 (Model 01),: The predict() Function (2:29).
Linear Regression, Part 3 (Model 01): Calculating Model Metrics (6:49)
Model Interpretability of Linear Models – How it works
Linear Regression, Part 4 (Model 01): Model Explanation (11:37)
Calculating Metrics: Model Helper (3:44)
Linear Regression Complex (Model 02), Adding Engineered Features (6.44)
Linear Regression Complex (Model 02): Model Explanation (4:51)
GLM Regularized regression: Theory explained
GLM Regularized Response (Model 03): GLMNET Elastic Net (9:01).
GLM Regularized Regression (Model 03): Model Explanation (3:53)
6.2.4 Tree-Based Algorithms
Tree-Based Methods & Parsnip Documentation (2:37)
Decision Trees: Theory explained
Decision Trees (Model 04): rpart (8:39)
Decision Trees (Model 4): Model Explanation with Rpart.plot() (8:57)
Random Forest: Theory Explained
Random Forest (Model 05): ranger (12:01)
Random Forest (Model 05): Ranger Model Explanation (7:20)
Random Forest (Model 06): randomForest (3:26)
Random Forest (Model 06): randomForest Model Explanation (5:22)
Reproducibility: set.seed() (2:09)
Gradient Boosted machines (GBM), Theory Explained
Gradient Boosted Machines (Model 07): XGBoost (7:53)
Mini Challenge: Tune Your XGBoost Model (0:31)
Mini Challenge Solution: Tune Your XGBoost Model (1:23)
Gradient Boosted Machines (Model 07): XGBoost Model Explanation (5:05)
Code Checkpoint (File Download)
6.2.5 Testing the Models & Visualization
Prediction & Evaluation Overview (1:47)
Predicting a New Bike Model: Over Mountain-Aluminium – Jekyll (10.46)
Plotting Predictions – Over Mountain – Aluminum – Jekyll (5.39)
Week 6 – Challenge 2: Predicting New Bike Model
Challenge: New Triathlon – Aluminum – Slice (0:36)
Challenge Solution: New Triathlon – Aluminum – Slice (10:05)
Code Checkpoint – With Challenge Solution (File Download)
6.2.6 Recap of Modeling
Modeling Recap & Next Steps (7:09)
6.2.7 BONUS – Preprocessing & Support Vector Machines
Preprocessing Pipelines and Recipes (10:03).
Applying Transformations & Getting Step Information with tidy() (4:14)
Support Vector Machine (SVM), Theory Explained
Support Vector Machine (Model 08): kernlab (7:46)
Support Vector Machine (Model 8): Tuning the Model (3.32)
SVM (Model 08): Testing on New Bike Models (6:32)
6.2.8 BONUS – Saving & Loading Models
Saving & Loading Models – With tibbles! (10:14)
Code Checkpoint (File Download)
Week 7: Communication
Week 7 Kickoff (1:08)
7.1 RMarkdown Primer
RMarkdown Cheat Sheet (5.58)
Setup – RMarkdown File Download (8:30).
YAML (Controlling Your Document Properties) (10:11).
Part 1 of Knitr Global Options: echo, eval and results (7:42).
Part 2 of Knitr Global Options – message, warning, dpi and more (4:00)
RMarkdown: Used for Reports, Websites, Books, Apps, & More! (3:23)
RMarkdown: Key Resources To Get ed (1:33)
RMarkdown: Headers, Text, & Lists (4:05)
RMarkdown: Tabsets & Images (6:50)
RMarkdown: Code Chunks (3:32)
RMarkdown Plotting (3.25)
RMarkdown: Tables & Footnotes (5:38)
7.2 Building interactive plots with Plotly
Setup – Plotly (3:15).
7.2.1 Part 1: Total Sales vs Time – Interactive Plotting Function
plot_total_sales(): A Custom Interactive Pitting Function (1:52).
Preparing data for plotting (5:30).
String-Format-Time Expressions: strftime Cheat sheet (2:18).
Formatting time stamps with strftime expressions (5:11).
Making the Interactive Plot with plotly & ggplot2 (8:35)
plot_total_sales()Part 1: Setting up a custom time series function (6.39)
plot_total_sales(), Part 2: Integrating ggplot() & ggplotly() (5:00)
7.2.2 Building interactive plots with plotly Part 2: Sales By Time vs Category
plot_categories(): A Custom Interactive Faceted Plotting Function (1:38)
Preparing data for plotting (5:43).
Making the Interactive Plot with plotly & ggplot2 (8:51)
plot_categories(), Part 1 – Handling the Data (5.33)
plot_categories(), Part 2: Handling the Inputs & Filter Logic (7:21)
plot_categories(), Part 3 – Generating the Interactive Plot (5.23)
Saving Our Functions (1.59)
Code Checkpoint (File Download)
RMarkdown Sales Report 7.3
Building an Interactive Sales Report – HTML & PDF – RMarkdown & Plotly (1:40)
RMarkdown Setup (2.35)
YAML Settingup (5:43).
Knitr Global Options (3.01)
Plotting Function Setup – RMarkdown (5.46)
Report: Total Sales Section (8:39)
Road Bikes Section Section Report (6:00).
Report: Mountain Bike Section (1.17)
Convert to PDF Format (2.16)
Code Checkpoint (File Download)
Project 1 Report – Product Pricing Prediction Algorithm Using XGBoost
Product Pricing Report: What are You Building? (1:19)
Setup (File Upload) (4:45).
Data Science Report Structure: How to Communicate Data Science Effectively (2.10)
Gap Analysis, Part 1: Bike List & get_bike_features() (7:12)
Save Functions with dump() & Loading Functions with source() (2:17)
Gap Analysis, Part 2: Analyzing the Product Gaps & plot_bike_features(), Part 1 (8.52)
plot_bike_features()Part 2: Formatting a Plot (6.47)
Saving Our Functions & Re-Knitting Our Report (3:59)
Part 1: Price Prediction Algorithm Part 4:56
Part 2 of the Price Prediction Algorithm (6:06).
Formatted Tables: knitr::kable() (2:10)
Solution Summary (3.03)
Code Checkpoint (File Download)
Project 2 Report: Customer Segmentation with Ka-Means & UMAP
Customer Segmentation report – This is it! (2:18)
Setup (File Upload) (3.34)
Report Overview – Customer Segementation (2:16)
Heat Map, Part 1 Data Manipulation (7.35)
Heat Map, Part 2: Static Visualization with the ggplot2 (8.32).
Heat Map, Part 3: Interactive Plot with Gggplotly() (6:10)
Saving Functions & Adding Your Analysis To The Report (4.48)
Customer Segmentation, Part 1 – Customer Trends (7:18)
Customer Segmentation, Part 2 – K-Means of Clustering (4.51)
Customer Segmentation, Part 3 – UMAP (6:02)
Customer Segmentation, Part 4 – Combining K-Means & UMAP (2:54)
Customer Segmentation, Part 5 – Plotting Function (9:10)
Saving Functions & Adding Your Analysis To the Report (2.45)
Customer Preferences Part 1 – Overview (1.25)
Part 2 Customer Preferences – Data Manipulation 1. (6:33)
Customer Preferences, Part 3, Data Manipulation 2, (5:50)
Customer Preferences, Part 4 – Visualization 1 (9:47)
Customer Preferences, Part 5 – Visualization 2 (5:22)
Saving & Loading the Functions – dump() & source() (1:53)
Completing the Analysis – Customer Preferences Statement & Solution Summary (6:02)
Code Checkpoint (File Download)
Hooray for Course Completion!
Congrats! Congratulations! (1:12)
Data Science For Business With R (DS4B 201-R): Learn H2O, LIME, & the BSPF (2:30)
Get Your Certificate & Show It To The World! (1:07)
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