My talk at General Assembly, Boston, on introduction to data analytics and how to get started in this field. https://generalassemb.ly/education/data-analytics-lets-break-it-down/boston/17448

1. DATA ANALYTICS
Let’s Break it Down
Talk at General Assembly, Boston on October 19, 2015
Twitter: @ArpitGupta
https://www.linkedin.com/in/TheArpitGupta

2. 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: @ArpitGupta
https://www.linkedin.com/in/TheArpitGupta

3. What’s on your mind?
Why are you here?

4. 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

5. 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

6. How is Data Analytics used?
▸Transportation
▸Fashion
▸Healthcare
▸Non-profit | Social Good | Fundraising
▸Marketing | Advertising
▸Content Strategy | Buzzfeed?
▸Finance
▸Education
▸Food

10. What data does Uber have?
What questions does Uber want to answer?

11. ▸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

12. 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

13. Data Transformation
Transactional
Data
Aggregated
Data

14. 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

15. 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)

16. 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

17. Source: http://mbtaviz.github.io/

18. Source: http://mwpdigitalmedia.com/blog/without-a-video-your-kickstarter-project-will-probably-fail/

19. 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

20. $1M Grand Prize - Inactive
Source: http://www.netflixprize.com/

21. 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

22. What’s on your mind?
Twitter: @ArpitGupta
https://www.linkedin.com/in/TheArpitGupta