R for Business Analysts, NYC Data Science Academy in-person training | November 6 @NYC

Posted on Oct 13, 2017

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

R is a powerful language used widely for business analytics. More companies are seeking business analysts with knowledge of R for data.

In this R for Business Analysts course, students will learn how data science is done in the wild, with a focus on data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Students use the R statistical programming language to work through real-world examples that illustrate these concepts. Concurrently, students learn some of the statistical and mathematical foundations that power the data-scientific approach to problem solving.

Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class. Due to the focused nature of this course, there will be no individual class projects but the instructors will be available to help students who are applying R to their own work outside of class.

Designed and taught by Brennan Lodge, Team Lead at Bloomberg. Watch his interview here.

Learn More and Sign Up


Who Is This Course For?

This course is for anyone with a basic understanding of data analysis techniques and business analysts interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. A familiarity with the R programming language is helpful, but unnecessary if the pre-work for the course is completed (more on that below).


Students should have some experience with programming and have some familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. In R, it will be helpful to know basic data structures such as data frames and how to use R Studio.Students should complete the following pre-work (approximately 2 hours) before the first day of class:
  • R Programming – https://www.rstudio.com/online-learning/#R
  • R Studio Essentials Programming 1: Writing Code https://www.rstudio.com/resources/webinars/rstudio-essentials-webinar-series-part-1/


Upon completing the course, students have:

  • An understanding of data science business problems solvable using R and an ability to articulate those business use cases from a statistical perspective.
  • The ability to create data visualization output with Rmarkdown files and Shiny Applications.
  • Familiarity with the R data science ecosystem, strategizing and the various tools a business analyst can use to continue developing as a data scientist.


Unit 1: Data Science and R Intro

  • Big Data
  • Data Science
  • Roles in Data Science
  • Use Cases
  • Data’isms
  • Class Format overview
  • R Background
  • R Intro
  • R Studio

Unit 2: Visualize

  • Rules of the road with data viz
  • Chart junk
  • Chart terminology
  • Clean chart
  • Scaling data
  • Data Viz framework
  • Code plotting

Unit 3: R Markdown

  • Presenting your work
  • R markdown file structure
  • Code chunks
  • Generating a R markdown file
  • Rmarkdown Exercise

Unit 4: Shiny

  • Shiny structure
  • Reactive output
  • Widgets
  • Rendering Output
  • Stock example
  • Hands-on challenge

Unit 5: Data Analysis

  • How to begin your data journey?
  • The human factor
  • Business Understanding
  • Dplyr
  • EDA – Exploratory Data Analysis
  • Data Anomalies
  • Data Statistics
  • Key Business Analysis Takeaways
  • Diamond data set exercise
  • Hands on challenge with Bank Marketing

Unit 6: Introduction to Regression

  • Regression Definition
  • Examples of regression
  • Formulize the formula
  • Plotting
  • Statistical definitions involved
  • mtcars regression example
  • Business use case with regression

Unit 7: Introduction to Machine Learning

  • ML Concept
  • Types of ML
  • CRISP Model
  • Modeling
  • Evaluation
  • Titanic Example
  • Decision Trees
  • Feature Engineering

Unit 8: Strategy

  • Data Driven Decision Making
  • Data Science Strategy
  • Strategy Fails
  • Macroeconomic strategy
  • Adapting
  • Data Science Project
  • Data Impact
  • Project guide
  • Opportunities for improvement
  • Big Box Store Strategic Exercise

Seats are filling up fast! Sign up now.

If you have any questions about our course or the application process, please do not hesitate to reach out to us via email.

About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI