NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Capstone > Data Insights on Citi Bike NYC Business

Data Insights on Citi Bike NYC Business

Alexander Tin, Jessie Wang, Michael Link and Catherine TangKimSin
Posted on Jun 28, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Business Problem

Background

Over the decade, data shows the number of bike share programs and popularity of such programs have grown drastically, with over 207 million trips taken in the U.S since 2010 [1]. Aside from being a cost efficient and environmentally friendly mode of transportation for short work commutes or excursions for fun, it also offers the benefit of exercise for the rider.

However, from a bike share program operatorโ€™s perspective, there are many factors to consider to ensure it is operating smoothly and growing. This includes taking into account  the rider experience, rider safety, bike availability, bike maintenance, bike theft/ vandalism, trip pricing, and acquisition of program sponsors.

Objective

The objective of our project is to extract insights from Citi Bike NYC (hereafter referred to as Citi Bike) business operations (and other cities taken as reference) to inform the creation of a successful bike share program in another city. By doing so, a prospective company can optimize their business model upon deployment in a new city.

Data Processing and Validation

The main dataset used in this analysis was Citi Bike Trip Histories [2]. Each row/observation represented a single trip. Variables included trip duration, start station, end station, user type, etc. In total there were 142 csv files organized by year, month, and state (NYC or NJ). Analyzing all 99 million observations within Jupyter notebook was deemed computationally infeasible due to the memory limitations of most personal computers. To overcome this limitation, we created two resources. 

First Resource

The first resource was a 5% random subsample of the entire dataset. This subsample allowed us to discover high-level trends throughout program history. For instance, with this resource we were able to answer the following: โ€œWhich hour of the day, day of the week, and month of the year are most popular for Citi Bike rides?โ€ 

Second Resource

The second resource was a SQL database. By connecting SQL with Jupyter Notebook, we were able to make targeted queries that extracted information from all 99 million observations. For instance, with a query we were able to answer the question โ€œIn the history of Citi Bike, how many people have ridden from station A to station B?โ€ In contrast, if we were only using the 5% random subsample we would have only been able to answer โ€œIn the history of Citi Bike, what proportion of rides have gone from station A to station B?โ€

The 5% random subsample was validated through comparing the sampled values of imbalanced features against the true values of imbalanced features. For example, let's assume that from a SQL query we determine 12% of all rides start from the Times Square docking station.

In our random subsample we find that 11.8% of all rides start from Times Square. In the next random subsample we find that 12.5% of all rides start from Times Square. This process was repeated 40 times, and the results for each station were plotted on a histogram to see whether the random sample (11.8%, 12.5%, โ€ฆ) accurately depicted the true value of the unbalanced feature (12%). Upon experimentation with multiple subsample percentages (1%, 5%, 10%), we found the 5% subsample to be representative of the full dataset.

Exploratory Data Analysis 

Below are some key points about the Citi Bike NYC program and our takeaways. In certain cases, we were also interested to see how Citi Bike NYC compared with other large city bike share programs. We compared them with the bike share programs in San Francisco and Washington DC (hereafter referred to as SF and DC respectively). By determining consistent trends among multiple cities, bike share program creators can have added confidence in their marketing and operational strategies.

Subscriber %

Citi Bike divides its customers into two segments. Subscribers are users who buy  for Citi Bike annual membership, whereas Customers are users who pay for a Single Ride, Day Pass or 3-Day Pass. 

Footnote: More information on their pricing can be found here. 

As we visualize below, Citi Bike and the bike share programs in SF and DC, SFMTA Bikeshare and Capital Bikeshare respectively, are all mainly composed of rides from subscribers, accounting for around 80% of total rides taken. Variations in subscriber-customer ratios could be due to differing population densities in NYC, SF, and DC (27K, 17K, and 10K people/square-mile respectively). A city with higher population density may lend itself more towards bike and foot traffic than a sparsely populated city which necessitates automotive transportation. Variations aside, the majority of bike rides come from subscribers.

Data Insights on Citi Bike NYC Business

Takeaway:

The majority of NYC, SF, and DC bike share users are city residents who anticipate/believe that they will bike on a consistent basis.

Hourly demand Data

Based on the hour of day, we see that the hours with highest ridership is between 7-9 in the morning, while the peak hours in the evening are from 5-6pm. Upon further analysis, we found that AM ridership is concentrated in the Financial District / Midtown area, and a large increase in demand can be observed in Central Park between 4-8pm in the evening.

Data Insights on Citi Bike NYC Business

Additionally, there is a difference between weekday and weekends, where the peak ridership is in the afternoons during the weekends, as opposed to weekdays, which peak during commuter rush hours.

Data Insights on Citi Bike NYC Business

Takeaway:

We can deduce that the increase in ridership during weekdays is due to the bikesโ€™ use by commuters, while for the weekends bikes are used quite evenly throughout the afternoon.

Monthly demand Data

Throughout the years, there is a consistent trend where we see an increase in the usage of Citi Bike between April and October, which corresponds to the seasonality of weather in New York. Thereโ€™s a positive correlation with temperatures, aspeople are less apt to bike  during the cold months of winter from December through March than in spring and summer.

Looking at the breakdown of users by month, we also see a similar trend in the users of the Citi Bike program. In particular, the number of Customers (defined above) increases as New York starts seeing the warmer weather between April and October.

Takeaway:

There is a seasonality inherent to the bike share operations, in that during the warmer months, riding bikes is safer and more enjoyable, and thus more users (both Subscribers and Customers) utilize the bike share program.

The increase of Customers during the warmer months is likely due to tourists who visit New York and utilize Citi Bike during their stay.

Temperature vs. Fleet Size

Supplementing the seasonality of demand for usage of Citi Bike, we see that there is a positive correlation between temperature and Citi Bike fleet size (i.e. both variables moving in tandem). Citi Bike fleet size was defined as the total number of unique bike IDs in a given month divided by the maximum number of unique bike IDs in the prior 3 years. As temperature drops, bike operators have historically moved bikes from the streets to storage, in order to reduce damage (such as rust, or vandalism) and idling of unutilized bikes.

Takeaway:

In NYC, Citi Bike has historically moved ~15% of their bikes into storage each winter.

Trip duration

Comparing and looking at the density of bike trip durations in NY, SF and DC, we see that it is very right-skewed, with the majority of the trips under 20 minutes. SF has a higher concentration of short trips than DC. This could be due to the difference in terrain. SF is rated as the 6th most hilly city in the nation.

In contrast, DC and NYC are the 49th and 58th most hilly cities respectively [3]. More likely, though, the differences in SF are due to the small-scale nature of the governmental bike share program. Initially there were only 350 bikes and 35 stations, and a visual inspection shows many of these stations are in close proximity to each other. The trip durations in SF may be less indicative of local users/terrain and more indicative of the scale of the business.

Takeaway:

Given the rider behavior, the takeaway for operators is to keep the max distance between stations to be around a 10-15 minute ride.

Rebalancing

Bike rebalancing is a process by which bikes are moved from docking stations with surplus to docking stations with shortages to meet anticipated bike demand. Looking at the total bike rides over the years compared to the rebalanced number of bikes, rebalancing is on average around 7% of total bike rides. In other words, for every 100 bike rides that occur, 7 bikes are rebalanced to an alternate docking station.

Through our data exploration, we found that 97.7% of unique rides do not end at the same station. Additionally, the top 10% most popular docking stations accounted for 45% of all bike trips.

Takeaway:

Rebalancing is one of the major operational considerations in setting up a bike share program.

Click into our Shiny App here to visualize the bike availability throughout different times of the day.

Revenue Data

Citi Bike has five different sources of revenue, with annual membership, sponsorship, and casual membership being the three most important. Together these three categories made up over 85% of Citi Bikeโ€™s total revenue in 2019.

Annual Membership

The largest source of revenue in recent years has been the annual membership: of the $46.7 million generated by these three sources (in 2019), annual memberships accounted for $24.7 million, or 53%. While this source of revenue has climbed upwards from 2015 to 2017, it has plateaued for the past three years.

Sponsorship Money

The second most important source is sponsorship money. Citi Bike has a total of 9 sponsors, and, as indicated by the name,  Citibank is  the largest. Over the years, sponsors have, on average, contributed $12.1 million per year to Citi Bikeโ€™s annual revenue. In 2019, the revenue decreased due to a dip in sponsorship income.

Casual Membership

Finally, there is casual membership, which has grown by 29% from 2018 and 2019, and is approaching the average annual value of sponsorship. Even though casual membership makes up a small portion of total Citi Bike rides, this is a very profitable part of their business considering the high revenue per ride. With greater demand for casual membership, especially in the summer months, this is a part of Citi Bikeโ€™s business model that should not be overlooked.

Takeaways:

Annual membership makes up the majority of Citi Bikeโ€™s revenue, although in recent years its growth has begun to plateau.

 

Data Modeling and Results

Time Series Forecasting

Next we forecasted Citi Bikeโ€™s revenue and ridership, using an ARIMA model, in order to benchmark Citi Bikeโ€™s performance during the COVID-19 lockdown. As you can see in the graphs below, both the ridership (top left), and revenue (top right) follow a seasonal trend. Every year, demand peaks in the summer and early fall, drops steeply in the winter, and picks back up in the spring. Each year this cycle repeats itself.

What is interesting about the daily ridership is that aside from the larger more visible annual seasonality, there is also a smaller seasonal trend per week (observed through ACF and PACF plots). Citi Bike demand is higher on weekdays than weekends. However, it was difficult to fit a good model to take into account the larger seasonality along with the smaller seasonality, so we decided to aggregate daily ridership by month to build a monthly-ridership seasonal ARIMA model for our analytical purposes.

Because of the COVID-19 disruption to business, we excluded ridership data starting from March 2020 as the lockdown began mid March, and shows an immediate decrease in bike usage due to the pandemic. We only excluded April 2020 from the revenue, as there is an apparent lag in revenue reporting.

Next, we split the data for training and testing, the last 12 months of each set were used as testing data, and all data prior was used for training. We were able to fit two time series models that gave a 13.9% and 10% MAPE, for revenue and monthly rides, respectively. The two plots on the second row above illustrate the projected revenue and monthly ridership for 12 months afterwards with a confidence interval of 80% and 95%.

Projected Revenue Data

Next we look at 2020 projected revenue, in comparison to 2019 revenue, and actual 2020 revenue observed for the month of April. Based on our ARIMA model, we were expecting Citi Bike to receive revenue comparable to last year, and growth in revenue starting in June. In reality, we see that actual revenue is about half a million dollars less than we expect from observing our model forecast.

It is important to keep in mind that April 2020โ€™s $2.8 million is still comparable to Citi Bikeโ€™s revenue April in 2018, just two years ago, when there was not a global pandemic. Considering the entire city of New York was under lockdown and that many were no longer commuting to work, this is actually very impressive. It is fair to conclude that Citi Bike is still able to remain a fully operating business, despite the city wide lockdown.

Changes from April to May 2020

While we donโ€™t have May data to confirm that Citi Bike is on its way back to pre-pandemic business levels, we can observe May ridership data. Again, looking at the projections, actual ridership for both March, April, and May fall short of what we projected.

When we look at the change in demand from April to May 2020, however, we see that demand has more than doubled (number of rides increased by 117%), in contrast to an 8.5% increase in ridership from April to May in2019. Even though April and May ridership in 2020 still falls short compared to last year, demand is rapidly growing to meet pre-pandemic levels. Because of this, we believe that Citi Bike is on its way to returning to business as expected. Based on the evidence, we can conclude that COVID-19 has not had a significant impact on Citi Bike and its current business model thus far.

Sponsorship vs. Advertising

Overview

Citi Bike and others organizations have on average contributed $12.1 million per year to Citi Bike. As mentioned above, these sponsorships are the second largest revenue source for the business. Despite the tremendous value of these sponsorships, the year to year giving of donors can be quite erratic (standard deviation of $2.3 million).

From 2018 to 2019 all revenue streams grew except for sponsorship. Sponsorship decreased by $5.2 million and drove the overall business growth rate from 9.8% in 2018 to -2.8% in 2019. In short, sponsorship has provided a large amount of revenue for Citi Bike but has been irregular from year to year.

Our group wanted to investigate whether sacrificing sponsors for general advertising on the bikes (e.g. Broadway musical ads, restaurant ads, etc.) would provide more revenue and greater consistency in year to year financial reporting. To do so, we divided sponsorship by the total number of rides in a given year ($/ride). This Annual Sponsorship per Ride is a proxy for the cost of putting the Citibank logo on each bike. Throughout the years, Citibank and other sponsors have on average paid $1.03 per ride (see figure below).

Findings

Two findings emerge from the graph above. First, advertising has become cheaper for Citibank over the years. This is predominantly due to an increase in riders and the popularity of the program. Secondly, if a bike share program was going to start in another city it seems that sponsorship ranging from $0.5 - $2 per ride is the norm. Program creators could request funding from prospective sponsors accordingly.

Advertising Relative Data Comparison

How does the average Annual Sponsorship per Ride of $1.03 compare to traditional advertisements? In other words, is the advertising cost on Citi Bikes currently higher or lower than the going rate for billboards or taxi cab advertisements? One estimate of taxi cab advertising claimed that on any given day, taxi-top advertisements cost $1.75 per 1000 impressions [4].

We asked ourselves, โ€œOn a single Citi Bike ride how many people need to see the bike advertisement for the value of advertising to equate that of the taxi?โ€ The answer was โ€œOn each Citi Bike ride, 590 onlookers are necessary for sponsorship to be just as cheap as taxi cab advertising.โ€ To interpret this result let us consider two fictional scenarios.

Scenario

Reality

Fictionally Expensive

Fictionally Cheap

Sponsorship

Cost per Ride

$1.03 / ride

$1,750 / ride

$0.0175 / ride

Needed

Impressions

590

people

1,000,000

people

10

people

 

Scenarios and Recommendations

The first fictional scenario involves Citibank sponsoring an inordinate amount of money per ride ($1,750). In order for sponsorship to be just as cheap as taxi cab advertising, 1 million people would need to see the advertisement on a single ride. This is an unrealistic expectation.

If Citi Bike were receiving sponsorships of this magnitude, our recommendation would be to stay with the sponsors because that deal cannot be met on the open market. In contrast, if sponsors were giving $0.0175 per ride, only 10 people would need to see the advertisement on a single ride for the two advertising types to be of equal value. If Citi Bike were receiving sponsorships of this magnitude, our recommendation would be to switch to the open market because you will likely receive more money there. In reality, the median trip duration is 20 minutes, and it is unlikely that 590 people will view the bike during that time.

Currently, we recommend Citi Bike to stay with their sponsors because these contributions likely outcompete the market value of advertising. We recommend that Citi Bike study the number of impressions made on an average bike ride. With this value in hand, they can either switch to open market advertising or encourage current sponsors to give at a flat rate per ride so that advertising does not continue to become cheaper for the sponsors.

 

Conclusions and Business Recommendations

Our exploratory data analysis and modelling uncovered transferable business insights which can be organized into three categories; sponsorship, operations, and revenue.

Sponsorship

With respect to sponsorships, we found that Citi Bike annually generated $12 million from their sponsors. The sponsorship cost per ride has decreased from $2 to $0.5. If sponsorship costs per ride continue to decrease, we would advise Citi Bike to study the number of Citi Bike impressions made on a given ride. With this value, Citi Bike can determine when it would become more profitable to sacrifice Citi Bike sponsors for open market advertising.

Operations

As temperature drops, Citi Bike operators have historically moved bikes from the streets to storage, in order to reduce damage (such as rust, or vandalism) and idling of unutilized bikes. From our analysis, it appears that Citi Bike moves approximately 15% of their fleet into storage each winter. Associated transportation and storage costs should be considered when implementing a bike share program in a similar climate.

Additionally, bike rebalancing is a necessary process to counteract the unsteady flow of bikes to and from various docking stations. We found that for every 100 bike rides that occur, Citi Bike operators rebalance 7. Associated labor and transportation costs should be considered when implementing a docking station bike share model in any city.

Revenue

Over the course of Citi Bikeโ€™s program history, annual subscriptions have accounted for 42% of total revenue. From 2017 - 2019 annual subscriptions have flatlined with a growth rate of approximately zero (-0.2%). Assuming that this plateau of subscriptions is nationally applicable, we recommend that bike share program managers decrease the cost of casual ridership to attract new customers into the subscription base.

Additionally, through comparative analysis of NYC, SF, and DC, we found that the majority of rides are under 20 minutes. Knowing this, program managers could offer generous subscription rules (unlimited rides less than 3 hours) while knowing that very few people will actually take advantage of these benefits.

Additionally, Citi Bike has not been entirely immune to COVID-19 financial impacts.  Despite low April 2020 revenue in comparison to projected values,  the $2.8 million observed is still comparable to Citi Bikeโ€™s April 2018 revenue. There also appears to be a nation-wide increase in demand for bikes since the start of the pandemic, as people look for socially distanced alternatives to traditional modes of public transportation [5].

In light of this, it is fair to conclude that Citi Bike is still able to remain a fully operating business, despite the state mandated lockdown. Assuming similar levels of population density, other bike share program managers can expect the continuation of a viable pandemic business model as people move from crowded public transit to comparatively safe bike transit.

 

Thank you for taking the time to read our blog post!

We are the Data Science team for Schwinn. Our Git repository can be found here. Please click on our individual blog links below to reach out or to read other work weโ€™ve done.

Contributors

Jessie Wang - www.linkedin.com/in/jwangxin

Alex Tin - www.linkedin.com/in/alexandertin

Michael Link - www.linkedin.com/in/data-science-link

Catherine Tang Kim Sin - www.linkedin.com/in/catherine-nicole-tang-kim-sin-a25a64192

 

Sources

Featured Image - Photo by Anthony Fomin on Unsplash

[1]-https://nacto.org/program/bike-share-initiative/

[2]-https://s3.amazonaws.com/tripdata/index.html

[3]- https://www.researchgate.net/publication/281672365_The_Hilliness_of_US_Cities

[4]-https://www.billboardconnectionadvertising.com/taxi-cab-advertising-boston-effective/

[5]-https://www.businessinsider.com/united-states-is-running-out-of-bikes-during-coronavirus-pandemic-2020-5

About Authors

Alexander Tin

View all posts by Alexander Tin >

Jessie Wang

Jessie is a graduate from the University of California, Santa Barbara with a degree in Actuarial Science. Upon graduation, she joined UnitedHealth Group as an actuary where she gained a wide array of experience in the healthcare industry....
View all posts by Jessie Wang >

Michael Link

Michaelโ€™s background is in ecological and water resources engineering. He began his career as a water resources engineer in a Fortune 500 consulting firm. Having worked to industry-standard analytical techniques and software, he discovered that he had a...
View all posts by Michael Link >

Catherine TangKimSin

View all posts by Catherine TangKimSin >

Leave a Comment

CITIBANK STEPPING OUTSIDE THE BOX - the marketist September 20, 2021
[โ€ฆ] day. This Branding strategy became a primary source of revenue as it grew over the years, reaching $46.7M in 2019. Their main sources of income are annual memberships (24.7M or 53%) and sponsorships (12.1M or [โ€ฆ]

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application