Presentation is loading. Please wait.

Presentation is loading. Please wait.

TEMPLE ANALYTICS MERCK CHALLENGE By Team Jeffrey Diana.

Similar presentations


Presentation on theme: "TEMPLE ANALYTICS MERCK CHALLENGE By Team Jeffrey Diana."— Presentation transcript:

1 TEMPLE ANALYTICS MERCK CHALLENGE By Team Jeffrey Diana

2 CONTENTS Introduction Roles and Technologies Data Acquisition & Analysis Visualization Organization Strategy

3 INTRODUCTION Merck is moving  How will this move affect the employees?  How will commute times change?  Which location would be the best? Approach  Organize data by zip code and organization  Weight each zip code based off of employee numbers  Acquire data using APIs and research  Use visualization libraries in JavaScript to create a compelling narrative

4 ROLES & TECHNOLOGIES

5 TEAM ROLES Zack Smith  Backend Development Steve Bergey  Data Acquisition Jeff Diana  Data Analysis Jihun Song  Imagineer

6 TECHNOLOGIES Backend  Python, Javascript, Bash, Nginx, NodeJS, Flask Frontend  HTML, CSS, Javascript, Bootstrap Acquisition and Analysis  Python, Bokeh, Javascript, GMapsJS, Excel Management  Github Everything Else  Emacs

7 DATA ACQUISITION & ANALYSIS

8 THE BING API How can we get useful, pertinent data on commute times and locations? Bing API!  Application programming interface  Web-based interface which allows you to work directly with all of Bing’s data  Super simple

9 COMMUTE TIME ACQUISITION Primary Objective: Get a good data set for average commute times based off of zip codes Solution: Use python!  Break data in to different zip codes  Query Bing API for commute times per zip code  Do the same for organization

10 WHAT ELSE CAN BING GET US? Commute times are fantastic! How about we get some graphical data? Methodology:  Let’s break down the data the same way as before  Instead of querying the API for time, let’s query it for the route a commuter takes  A ton of points!

11 A TON OF POINTS ANALYSIS We’ve acquired our data, now what do we do with it? Gmaps Javascript Library  Map Generation  Draw polylines

12 VISUALIZATION

13 JAVASCRIPT IS NIFTY! JavaScript is a scripting language similar to Python which is designed to be used with websites Executes client-side Allows neat user interactions On every website, ever

14 THE DREAM

15 LET’S MAKE A WEBSERVER JavaScript has server-side applications too  NodeJS Python is easier  Flask We can host our website on a Virtual Private Server (VPS), and anyone in the world can access it  Access to all that beautiful JavaScript  Custom UI  Unique Experience

16 PORTRAIT OF A SIMPLE WEBSITE Python-Based Web Server Client (Web Browser) Request (HTTP) Response (HTML, CSS, JS)

17 SERVER-SIDE REQUEST HANDLING

18 HTML: THE IMPORTANT PARTS

19 JAVASCRIPT: THE IMPORTANT PARTS

20 END RESULTS

21 MORE RESULTS

22 SCROLLING!

23 ORGANIZATION

24 GITHUB REPOSITORIES We used GitHub to manage all our code  Easy collaboration  Revision Control  Independent branches  Commit Messages

25 CONCLUSION Project Management is hard  Bring together a lot of conflicting ideas from a lot of different people  Split up tasks effectively  Using time effectively We suck at JavaScript  It’s hard to learn a new technology Expectation Vs. Reality Cleanairfor.me

26 QUESTIONS? Go ahead, ask me anything.


Download ppt "TEMPLE ANALYTICS MERCK CHALLENGE By Team Jeffrey Diana."

Similar presentations


Ads by Google