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

Details

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).

Prerequisites

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/

Outcomes

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.

Syllabus

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.

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