data analytics - let's break it down
TRANSCRIPT
![Page 1: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/1.jpg)
DATA ANALYTICSLet’s Break it Down
Talk at General Assembly, Boston on October 19, 2015
Twitter: @ArpitGuptahttps://www.linkedin.com/in/TheArpitGupta
![Page 2: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/2.jpg)
Hi! I am Arpit Gupta▸ Senior Product Manager, Analytics @Fiksu, Mobile
applications advertising▸ Instructional team at GA’s Data Analytics Course▸ Past: Healthcare consulting for 5 years and non-
profit▸ Analytics for a long, long, time!
Twitter: @ArpitGuptahttps://www.linkedin.com/in/TheArpitGupta
![Page 3: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/3.jpg)
What’s on your mind? Why are you here?
![Page 4: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/4.jpg)
Goals▸ Define data analytics ▸ Why it’s so important▸ The stages of analyzing data▸ What tools are used▸ Recommended next steps for learning to analyze
data yourself
![Page 5: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/5.jpg)
What is Data Analytics?▸ Learn to make sense of data; tell a story; defend your
proposal ▸ We can store data points, but learning from them is an
entirely different skill.▸ Drive business value.▸ Other terms
●Business Analytics●Web Analytics ●Social Media Analytics ●Real Time Analytics●Data Science / Predictive Analytics
![Page 6: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/6.jpg)
How is Data Analytics used?
▸ Transportation▸ Fashion▸ Healthcare ▸ Non-profit | Social Good | Fundraising ▸ Marketing | Advertising▸ Content Strategy | Buzzfeed? ▸ Finance▸ Education▸ Food
![Page 7: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/7.jpg)
![Page 8: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/8.jpg)
![Page 9: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/9.jpg)
![Page 10: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/10.jpg)
What data does Uber have?
What questions does Uber want to answer?
![Page 11: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/11.jpg)
▸ User Acquisition● How many new users are signing up on the platform?● What’s the breakdown by platform, OS● Which sources are most effective in driving new users?
▸ User Retention● What’s the average time before users abandon your product? ● What’s the lifetime value of my users?
▸ Revenue● Which city generated maximum revenue in last 7 days, 30 days, etc.● What % of revenue is from recurring customers?
▸ Product● How are users using your product’s features? are people recommending? ● Has a new feature resulted in bad customer experience and a drop in
usage/revenue?
Type of questions
![Page 12: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/12.jpg)
Analytics Workflow
1. Identify the problem
2. Obtain the data
3. Understand the Data
4. Prepare the Data
5. Analyze the Data
6. Present the Results
![Page 13: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/13.jpg)
Data Transformation
TransactionalData
AggregatedData
![Page 14: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/14.jpg)
Tools for Data Analytics▸ Excel / Google Spreadsheet▸ Database - SQL ▸ R ▸ Python▸ ETL Tools - Extract, Transform, and Load▸ Data Visualization/Dashboards
● Powerpoint/Excel● Industry-specific dashboard (Healthcare, E-commerce, etc.)● Role-specific dashboard (Marketing, Finance, Sales, etc.)● Tableau ● GraphiQ https://www.graphiq.com/ , D3.Js● Create your own Dashboard
![Page 15: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/15.jpg)
Data Types▸ Categorical (also Qualitative)
●Categorical variables represent types of data which may be divided into groups. Ex: race, sex, age group, and educational level
▸ Numerical (also Quantitative)
●Values of a quantitative variable can be ordered and measured. Ex: age, height, sales, volume
●Numbers are not always numerical data. Ex: Gender (0=Male, 1=Female)
![Page 16: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/16.jpg)
Typical challenges▸ Data is stored in too many places▸ Stored in different formats.
●How many ways can you use store date?
▸ Requires engineering effort to pull or transform data
▸ Quality of data is not good▸ Data is there but need to jump hoops to get
access ▸ Delay in answering questions▸ How to interpret data
![Page 18: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/18.jpg)
Source: http://mwpdigitalmedia.com/blog/without-a-video-your-kickstarter-project-will-probably-fail/
![Page 19: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/19.jpg)
Resources - datasets, competitions▸ Datasets
● City of Boston https://data.cityofboston.gov/ ● https://www.quora.com/Where-can-I-find-large-datasets-open-to-the-public● https://github.com/thearpitgupta/data_science_resources#data-sets ● http://www.gapminder.org/ ●Your own data: Uber, Runkeeper, Mint, Fitbit, Social media,
sleep, etc.● https://www.facebook.com/help/405183566203254 ● https://www.linkedin.com/settings/data-export-page ● https://riders.uber.com
▸ Data Competitions●Social Good http://www.drivendata.org/competitions/ ●Kaggle https://www.kaggle.com/ ●Baseball hack http://www.baseballhackday.com/ ●MIT Sloan Analytics Hackathon
![Page 21: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/21.jpg)
Resources - jobs, continued learning▸ Inspiration
●MBTA http://mbtaviz.github.io/●TED Talk
https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen
●Quantified Self http://quantifiedself.com/
●538 political blog http://fivethirtyeight.com/
●Crazy Egg http://blog.crazyegg.com/category/analytics/
● Ocam Razor http://www.kaushik.net/avinash/
▸ Learn - Codeacademy, W3schools▸ Jobs - Angel.co, Venturefizz, StartupJobsBos.com ▸ General Assembly - Data Analytics & Data
Science
![Page 22: Data analytics - Let's break it down](https://reader036.vdocuments.mx/reader036/viewer/2022062821/589d53f41a28abef688b4a93/html5/thumbnails/22.jpg)
What’s on your mind?
Twitter: @ArpitGupta
https://www.linkedin.com/in/TheArpitGupta