R for Business Analysts

R for Business Analysts
Course Overview

This class will be an introduction to the statistical programming language R for business analysts. We’ll explore data science use cases in the business realm and use R for data wrangling, data mining, visualization and prediction. Throughout the class we will be approaching business problems analytically and we’ll use R to explore data, make better business decisions and identify areas for improving performance. The combination of data analytics, R and the data science process will provide the foundation for using R for data science business problems. Students should come prepared with an understanding of computer programming and a curiosity for data science.

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Dates & Time Venue Tuition  
August 8, 2017 - September 5, 2017 7:00-9:30pm Workdays
Day 1: August 8, 2017
Day 2: August 10, 2017
Day 3: August 15, 2017
Day 4: August 17, 2017
Day 5: August 22, 2017
Day 6: August 24, 2017
Day 7: August 29, 2017
Day 8: September 5, 2017
New York
500 8th Ave., Suite 905
New York, NY 10018.0
$1590.00 Add to Cart
Questions? Read our FAQs & Refund Policy
For corporate training or small group training inquiry:
Instructor
Brennan Lodge
Brennan Lodge
I am a data nerd. I have been working in the financial industry for the last 10 years focusing on cyber security and data analytics. I hold a masters degree in Business Analytics from NYU Stern School of Business. In my spare time, I enjoy volunteering with DataKind to work on data science projects for non-profits. I am also an avid swimmer and water polo player. Twitter: @blodge8

Product Description


Overview

 

Data science has become the central approach to tackling data-heavy problems in both business and academia. In this course, students 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.This class will be an introduction to the statistical programming language R for business analysts. We’ll explore data science use cases in the business realm and use R for data wrangling, data mining, visualization and prediction. Throughout the class we will be approaching business problems analytically and we’ll use R to explore data, make better business decisions and identify areas for improving performance. The combination of data analytics, R and the data science process will provide the foundation for using R for data science business problems. Students should come prepared with an understanding of computer programming and a curiosity for data science.


Details


Instructor Interview


Data Science For Business Analysts from NYC Data Science Academy on Vimeo.


Goals

 

This is a “short course” of four weeks, with five hours of class per week (split into 2 ½ hour evening classes). 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.

Who Is This Course For?

 

R for Business Analysts 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
  • 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
  • Dplyr
  • 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

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