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-Data science can be learned in just 7 weeks, instead of taking 1+ years.

This course is for…

Business Data Science is a great way to grow your career as an analyst
Microsoft Excel Users who are ready to take Excel beyond Excel and create more powerful data-Programming language specific

Exemplary of what you create during the course

Customer Segmentation Report (created Week 7: Communication).
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 machines

 

One Purchase

1X Payment
$399

3 Monthly Payments Low

3X Payments
3 monthly payments of $159

6 Monthly Low 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! Do not try to find out where to go.

It seems impossible to imagine learning data science. There are 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 This revolutionary program takes the guesswork out of your life.-Work out of data science learning.

We offer the following:

Complete learning path R: R If you’re moving from Microsoft Excel to data science, it is the best 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-Time access to content, now and in the future.

 

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:

Import data
Joining Data
Data Cleaning
Data manipulation
Visualization
Modeling
Reporting

You’ll get resources

The Business Analysis With R Every course is packed with resources, tools and templates. There are too many to mention. You can talk numbers by:

100+ Code Lectures
Recorded Content: 15+ hours
10+ Cheatsheets & Walkthroughs
20+ Free Downloadable Code Templates
2 Projects
More!

You will 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 – Objective: To use data mining techniques to segment customer base, empowering Marketing department to send targeted emails to customers. This will increase engagement

You will create two reproducible business documents that contain the results of your analysis in week 7.
You will overcome all challenges

The Business Analysis With R This course will teach you how to apply what you’ve learned. This Week 6 Challenge will help you extend the Customer Segmentation into a real challenge.-You will need to segment companies based on their stock price movements in order to tackle world problems.
Summary of what you get

This methodical training program combines 1+ years of knowledge in a 7-week accelerated training. It is cheaper than a workshop ($5000)
Many resources (1000 Value)
10+ Cheatsheets and Walkthroughs
20+ Code Templates You Can Download
2 Business Projects
4+ Challenges

Add it all up: $6000 worth

Buy Today for $399

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

Your instructor

Matt Dancho
Matt Dancho

Download immediately DS4B 101-R – Business Analysis With R

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 will not rest until he sees results. Literally, Matt does not sleep. Don’t be surprised if Matt responds to your emails at 4AM!

Course Curriculum

You are welcome to Business Analysis With R (DS4B 101-R)!

Your Journey To Learning R For Business s Now! (2:33)

Instructions for Course Certificate

Private Slack Channel: How to Join

Prerequisites

Prerequisites

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 included in our toolbox?

The Ultimate R Cheatsheet (FileDownload) (2.14)

How To Use The Ultimate R Cheatsheet (2.39)

1.3 Data Science Project Setup

Installing 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)

Installing R Packages (File download) (11:47).

Package Installation Checkpoint

1.4 Optional – Transactional Database Primer

Who Should Have a Look?

What is transactional data? What Can We Do? With Is It in This Course? (1:41)

Orders of Transactional Data (File download) (3:53).

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)

Remove Unnecessary Columns() (3:59)

How to get the Order ID column back: bind_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 (File download) (1:38).

Writing Files: Excel CSV, 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 Diving in

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)

Get Vectorized Contents() (2:56)

Select Data by Type: select_if() (3:31)

Sorting Columns() (2:59)

Code Checkpoint: select (File Download)

2.2.3 With Rows (Observations).

Filtering rows: filter() (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-Calculations based

Adding 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)

Renaming columns for presentation: Part 1, 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 Long to Wide: Gather() (6:56)

Code Checkpoint: Pivoting (File Download)

2.2.7 Combining Data (Joining & Binding)

Joining Data: left_join() (4:59)

Binding data: bind_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)

Challenges in Organizing The Directory (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: Most Common Business Operations

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).

Measuring change: first(), Part 1 – From the First Year (3:22).

Measuring change: first()Part 2 – Starting the First Month (5.24 PM)

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: Formatting Column Names Programatically. rename_at() (7:12)

3.4 Text Analysis: Feature Engineering Case Study

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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 Colums (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)

Forcats Categorical Data Fundamentals

Documentation: forcats (1:44)

File Download: Categorical Data Setup (2.29)

Why Factors? (3:07)

Part 1 of Motivating Example: Visualizing Sales by Secondary Product Categories (8:20).

Motivating Example Part 2: Finalizing the Sales by Secondary Product category Visualization (4:43).

Factors & forcats Basics (6:01)

as_factor() As.factor vs.() (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 of an ggplot2 Object – What is g? (3:30)

4.2 ggplot2 geometries

Geometries setup (File Download). (2:33).

Part 1: Data Manipulation, Scatter Plot (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: Installation (8:22).

Scales: Scale color continuous (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)

4.4 Advanced Business Plotting using ggplot2

Advanced Business Plotting: Setup (File download) (1:19).

Top N Customers, Lollipop Plot Part 1 – Data Manipulation

Top N Customers: Lollipop Pllot, Part 2 Geometries (11.07).

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 a Function’s Anatomy() 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 Flags with 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 of Saving Functions: How to create a header with 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 using purrr (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: Mapping Nested data: Create Functions That Work on One Element (4.21)

Mapping Nested Data Part 2: Scale and Mutate() + Map() (6:21)

Part 1: Modeling with purrr: LOESS Smoother For Time Series (4:58).

Part 2 of Modeling with Purrr: Create a LOESS model using 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() To all Categories (8.10)

Checkpoint: Iteration using purrr (File download)

Week 6: Modeling Part 1 – K-Means Clustering & UMAP

Part 1 of Modeling: Overview – K-Means & UMAP (0:46)

Clustering & Dimensionality Reduction – Key Concepts & Theory Explained

Cheat Sheet Download: Segmentation & Clustering (File download) (0:48).

Segmentation & Clustering Workflow (9:22)

6.1 Customer Segmentation using K-Means Clustering & UMAP

K allows you to segment your customers-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 Iteration using 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: The Binning Price (5.47)

Customer Segment Purchasing Trends Analysis, Step 3 Rearranging Columns (2.58)

Customer Segment Purchasing Trends Analysis, Step 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 Using Stock Prices

Challenge Setup: Overview & Data (File Download) (6:46)

Ask the Challenge (8:37).

Challenge Bonus: Exploring Clusters with 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

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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 001): The parsnip is: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 Return: 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 04), Model Explanation using rpart.plot() (8:57)

Random Forest Theory: 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 – Aluminium – 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 Modeling Summary

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)

Theory of Support Vector Machine (SVM).

Support Vector Machine (Model 08): kernlab (7:46)

Support Vector Machine (Model 08), 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: Echo, eval, Results, Fig.Keep (7:42)

Part 2 of Knitr Global Options: 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 using 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 using 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 category vs. time

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 Data (5:33).

plot_categories(), Part 2: Handling the Inputs & Filter Logic (7:21)

plot_categories()Part 3: Generating an 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 Setup (5.43)

Knitr Global Options (3.01)

Plotting Function Setup – RMarkdown (5.46)

Report: Total Sales Section (8:39)

Road Bikes Section Report (6:00)

Report: Mountain Bike Section (1.17)

Converting to PDF Format (2.16)

Code Checkpoint (File Download)

Project 1 Report – Product Prediction Algorithm using XGBoost

Product Pricing Report: What are You Building? (1:19)

Setup (File download) (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)

Dump Saving Functions() & 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 of the Price Prediction Algorithm (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 using K-Means & UMAP

Customer Segmentation Report – This is what you are going to do! (2:18)

Setup (File download) (3:34).

Report Overview – Customer Segementation (2:16)

Heat Map, Part 1, Data Manipulation (7.35)

Heat Map, Part 2- Static Visualization With 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)

About Data Science Business With R (DS4B 201-R): Learn H2O, LIME, & the BSPF (2:30)

Get Your Certificate & Show It To The World! (1:07)

Here’s what you’ll get in DS4B 101-R – Business Analysis With R