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Exploring San Francisco Street Trees with Tableau

2021-06-16T01:16:47+00:00

On an afternoon when I was going through a 40-hour fast, I needed something to keep my mind busy (from thinking about food). So I decided to learn about trees in San Francisco, in particular, street trees.

Do you know…

All street trees in San Francisco are cared and maintained by San Francisco Public Works.
But, if you want to plant, remove, or prune a street tree, you can apply for a permit!
Not any type of trees could become street tree. Yes, there are competitions! Here is the list of recommended trees.
Significant and Landmark Trees: “trees on private property and within 10 feet of the public right-of-way designated as extra special and receive the same protections as all our street trees.”
At some point during the pandemic, a group of authors and tree lovers organized Social Distancing Tree Tours around the city of San Francisco. By “organized”, I mean they painted the directions and the tree names on the street. I had the honor to “attend” the Buena Vista tour.

I love trees and I’m fascinated by trees, but I didn’t know much about street trees. After this micro project, I’ve learned a lot more, not only about trees, but also about the streets of San Francisco - my favorite city.

The data

Source: Data.sfgov.org
Format: CSV file, 43MB
Rows: 195K
Columns: 18
Each row is a: Street tree

Understanding the data

There are 18 columns, but I didn’t use all of them for this micro project. Here are some of the important ones:

TreeID		Unique id of tree
qLegalStatus		Legal status: Permitted or DPW maintained
qSpecies		Species of tree
qCaretaker		Agency or person that is primary caregiver to tree. Owner of Tree
PlantDate		date tree was planted
Latitude & Longitude

I extracted the common names of the Species instead of the scientific names, and created another column for them.

The approach

Data cleaning (checking null values, replacing null with numbers or strings…)
Creating reports
Creating the dashboard

Dashboard screenshot

View the dashboard on Tableau Public.

The results

Top 5 common types of street trees in San Francisco:

Sycamore: London plane
Brisbane Box
Victorian Box
Indian Laurel Fig Tree
Red Flowering Gum

Top 3 statuses of street trees in San Francisco:

Maintained by Public Works
Undocumented (–> probably old trees that were not documented)
Permitted site

Are we planting new trees?

Yes! But since there are a lot of null values, we can’t 100% say that we planted more street trees than we did before.

Tree allocation across the city

Trees that are maintained by Public Works gather in groups in different neighborhoods.
Private trees are distributed all around the city.

Hope you enjoy this little exploration. Any feedback is highly appreciated!

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How to transition to Data Science from a business background

2021-06-09T20:52:02+00:00

“I’m currently in Consulting/ Marketing/ Finance/ BizOps…, can I become a data scientist?”

I’m sure I’m not the only one asking this question.

When I first started thinking about the data science career, I tried to talk to a lot of data scientists. I wanted to know:

Whether I’ll like this career
Given my business background, how difficult or easy it is for me to become a data scientist

Scott Czepiel was among those who agreed to share their insights with me.

Scott’s intensive career of working with data as an analyst, researcher, statistician, analytics leader, and data scientist, has enabled him to look at the “Data” field as a whole, and give clear, practical, no-biased answers to my questions. (I’m sure he’s not one of those who think Data Scientist is the sexiest job of the 21th century, but I should ask.)

If you’re thinking of a data science career, I highly recommend you read on.

Q1: How did your data science career start? Did you have technical background and transition from there?

“After grad school I got started in market research. Along with marketing analytics, this is a natural fit for sociologists because it is fundamentally about understanding human behavior. It also involves more hands-on data analysis skills that aren’t as common in marketing proper. I moved to Amazon as a business intelligence analyst, before the term ‘data science’ had become popularized. Since moving to SF, I’ve been working mostly in the tech industry.

I’ve always positioned myself as having a blend of science, engineering, and art (yeah, I know it sounds pretentious!).

‘Science’ being statistics, experimentation, and the logic of data analysis;
‘Engineering’ being the ability to move data around, reshape it, and present it in reports, and
the ‘Art’ being the ability to interpret data, combine it with domain knowledge and present findings to technical and non-technical audiences.

Whether it’s called analytics, business intelligence, or data science, I think these are the 3 cornerstones of the field.“

Q2: If you had to transition from a non-tech background, how did you do it? What did you have to learn and do in order to switch?

“All my programming skills are self-taught, but even after 20 years I still run into the ridiculous bias that I can’t possibly know how to write code because I don’t have a CS degree. At the same time, the programming that I’ve focused on is not the same that engineers specialize in. I pick up what I need in order to make data analysis easier, but there’s no need to become an expert in Python or Javascript to do that.

I actually think the tech skills are the easier part of the job. To really make a difference in an organization, to fulfill the hype or promise of what data science is supposed to be, is more about understanding the business, interpreting data in the right context to generate insights and make recommendations.

This is actually really hard to do and I know plenty of physics PhDs who know everything about algorithms but know nothing about how business works. Coming from a business background puts you in a very advantageous position because you are already familiar with how business functions, the process of decision making, product development process, business process, etc. All of these are much harder for someone with a pure tech background to pick up on their own.

The #1 tech skill to acquire first is SQL. It’s the foundation of accessing data from databases, it’s used everywhere, and getting more comfortable with it will unlock more job opportunities and set you apart from others.

After or along with SQL, try working with some visualization and reporting tools such as Tableau or Looker. At many companies this would be all you need. To go further, learn R. The best place to start is with RStudio and the excellent book, R for Data Science. This book will teach you the “tidyverse” which is a set of packages that has really modernized the R language and made it far more intuitive for data analysis than it used to be.

Data science is so broad that you don’t need all of these things to be successful. A good friend of mine who’s been VP of DS at several companies only knows Tableau. No SQL, no R or Python, just Tableau, but he’s also got a very keen insight so he’s successful because he can take Tableau reports and spin up an incredible narrative about the data.

At the end of the day, that’s what an organization needs from a data scientist: someone to look at data and understand what it means and tell the story about it.“

Q3: What do you like the most and the least about your data science job(s)?

“Most of my gripes about DS have more to do with the tech industry than about data science itself. However, the two are very much linked because a lot of the demand for data scientists is happening within tech. I’m trying to branch out to other industries (the health sector specifically), but especially here in the Bay area, jobs in tech are more common.

Like you also mentioned, I love to analyze data, and that’s always been the main draw for me. I also love the feeling of helping people understand something that they would otherwise not be able to discover because of having a different skill set.

Being a data scientist gives you time to be both an introvert and an extrovert.

I don’t know of any other job (besides HR) where you work with literally every department: engineering, product, sales, marketing, finance, customer service, IT, at some point you get to know all of them because they all need data and they all need someone to help them understand it so they can be more productive in their own domain.“

Q4: What do you think about the future of the data science job market?

“The demand for data science will continue to outpace supply for a long time. Yes, there are lots of people entering the field, but if you approach it from a “business first” perspective, there will always be a need for people to understand and interpret the data.

Data science is also very broad and means different things at different companies and to different people. You will encounter those who say data science is only about deep learning models, things like computer vision for self-driving cars, or genetic sequencing. That’s a very narrow view. Data science is about a lot more than that. Just be aware there’s a bias but mainly ignore those people. The bias is usually from genius coders with hard technical degrees who look down on everyone else. I think it’s much better to understand your strengths and weaknesses, let others do the jobs you can’t, and you do what you’re good at.

As Data Science matures, it appears to me to be specializing along 3 lines that I would roughly classify as business intelligence, product analytics, and machine learning engineering.

BI is focused on data pipelines, reporting, dashboards, analysis tools. There’s lots of work with the business side to understand high-level requirements and translate those to engineers. Also, auditing data.
Product analytics is a little bit of BI but focused more on driving product recommendations. It’s about becoming an expert on how customers use the product.
The modeling side has lots of crossover with ML engineers. This is more about building data-driven products, where data scientists will do research, build prototypes and specify high-level what the flow of data should be, but then a dedicated engineering team will implement it in production. Even this is a very broad brush, there’s lots of cross-over and lots of companies where data science is a blend of all 3 or a different mix.

In summary, I feel very fortunate that I stumbled into this field before it became a thing, when I was just doing what I enjoyed. There’s lots of room to grow, and even better is finding your niche where you build up valuable domain expertise, which again I would argue is more useful in the long run than tech skills.“

If you want to learn more about Scott and his data journey, visit his blog “Variables and Observations” where he writes about work, data, and working_with_data.

Final thoughts

Thanks to Scott, I was able to gain confidence and made the decision to pursue a data science career.

My key takeaways are:

Data science is broad and badly defined. Know your niche and develop your positioning in the market.
Brush up your SQL skill!
Data science = Science (the logics) + Engineering (the actions) + Art (the communication/applications). I think of it as cooking: you need to understand how to use heat, pressure, chemical reactions… to cook a dish, then you need the hard skills to actually chop, stir, fry… the food, and finally, a good presentation on a huge plate with a flower on top to convince people that this dish is delicious.
That being said, now you just need to pick your focus, and work on the skills that need improvement. For me, it’s the Science and some of the Engineering parts.

Thanks Scott! And stay tuned for more interviews to come.

This article is also published on my Medium. Photo by Jeremy Bishop on Unsplash

Pipeline Analysis - Excel and Tableau

2021-05-07T20:23:49+00:00

Project Overview

As a Business Analyst at Konica Minolta, I was responsible for conducting financial analysis for opportunities that our European subsidiaries had in pipeline.

Each European subsidiaries had their own CRM systems, but due to the complexity of the data, an in-house advanced Excel modeling was used at the Japan Headquarter (my team).

Dataset: varied quarter by quarter
Columns:
- Time period
- Name of NOC (National Operating Company)
- Name of Customer
- Mega factor %: How much % discount of hardware will be given to NOC in order to win the deal
- Status: Lost, Won, Canceled, Pending
- Other P&L metrics: Revenue, gross margin for hardware and consumables (toners, pay-per-page, professional services…)
Tools: Excel, Tableau

Project snapshots

ask me a question

My approach

Data cleaning and reorganizing, contacted Sales rep of the subsidiaries if finding missing data.
Descriptive analysis: I explored the financial impact of the pipeline through time, and by subsidiary. I also analyzed the won/lost updates
Predictive analysis: I presented different financial scenarios and projected the financial impact in each scenario.
Prescriptive analysis: I provided suggestions in terms of how much financial support we should give to the subsidiaries, which subsidiary to pay close attention to, and whether we needed to contact the subsidiaries for more information, etc.

see other projects view my resume

Recommendations

2021-05-06T16:44:07+00:00

What other people say about me

see more on my linkedin view my resume

Flight Cancellation Prediction using ML

2021-05-04T04:57:18+00:00

Project Overview

Length: 1 month of preparation and 7 hours of Hackathon
Content: MBA students teamed up with Data Scientists from GoodData. We were given public domestic flight data and 7 hours at the Hackathon to provide any insights that we’d like.
Data set: 47 million rows, 4GB, CSV file
Tools: We used R to build prediction model, Excel to deliver insights from historical data, and Google Slides for the presentation.

Our approach

Team meeting - MBA students: Determined our analysis focus (flight delays? cancellation? others?) and developed hypothesis.
Team meetings - MBA students and Data Scientists: Discussed about our analysis focus, agreed on the tools and amount of data we would use during the Hackathon
Hackathon:
- I presented my idea of building a prediction model of flight cancellation.
- My team agreed, and GoodData members helped to clean the data and build a multiple regression prediction model.
- I came up with the creative recommendations about how flights search engines like Skyscanner could monetize from this model.
- We presented our insights in terms of “Descriptive”, “Predictive” and “Prescriptive” Analysis.

Snapshot of the data set

ask me a question

Final presentation

3_Big-Data-Hackathon-Presentation.pdf

download pdf

Customer Survey - NPS Score Analysis

2021-05-02T20:23:50+00:00

Overview

Analyzing Customer Survey (NPS score) data, giving answers to problem statements/ strategic questions, and provided insights and recommendations.

Our approach

Initial meetings: understanding problem statement, confirming project's goals and timeline
Developed hypothesis
Data cleaning: excluding missing values, fixing misplaced records
Finding insights: breaking down data into different categories (Customer type, Number of responses, NPS score levels, etc.). Conducting Text Analysis from survey responses
Data Visualization
Report and recommendations

Tools

MS Excel Spreadsheet, JMP Software

Examples of the data set

Ask me a question

Final presentation

1_Customer-Survey-NPS-score-Analysis.pdf

download pdf

Professional Background

2021-05-01T01:43:01+00:00

You can find details about my professional and educational background in my resume. If that feels too dry to you, keep reading :).

Ritsumeikan Asia Pacific University | Bachelor of BA in International Management (2010 - 2014)

I arrived at Ritsumeikan Asia Pacific University (APU) in April, 2020, after quitting 1.5 year of enrollment at Foreign Trade University in Vietnam. I knew I’d be older than my peers, but I also knew that I wanted to go to Japan. And I made the right decision. APU had everything I wanted: a bilingual academic system (English & Japanese), a diverse culture with students from all over the world, and lots of extra-curriculum activities. At APU, I built my Japanese language skills, learned about the lifestyle and business culture of different countries, developed interests in business and management, and made friends with people through student clubs and volunteers activities.

Konica Minolta | Business Data Analyst (2014 - 2017)

After graduating from APU, I joined Konica Minolta, Inc. (KMI, コニカミノルタ) - a Japanese multinational technology company headquartered in Marunouchi, Tokyo. It has offices in 49 countries worldwide, and I was first responsible for managing the sales subsidiaries in Europe. At KMI, I have learned so much about how to work as a professional: keeping deadlines, reporting-communicating-consulting, wearing multiple hats, thinking from different perspectives, and how to be a mentor for a new grad.

3.5 years at KMI, I also experienced working with Excel spreadsheets and other analytical tools. That’s when I fell in love with data-driven decision making. I love to analyze data, identify trends or insights, and presenting results from the analytics to improve business performance or strategies.

University of San Francisco | MBA in Business Analytics & Customer Success (2017 - 2019)

To challenge myself and expand my career in business, I decided to apply for an MBA in America. I had never been to the US before, but I believed nothing could beat my eagerness to learn and my ability to adapt to new environments. USF offered me with a small-class, flexible MBA program in which I explored my next career paths in advanced analytics, customer success, or marketing. I had the chance to work on a lot of coursework and projects that I’m still proud of. At USF, I brushed up my data analytics skills, became familiar with American working culture, and developed my interests in customer success and marketing at Tech/SaaS companies.

Digital Garage | Growth Marketing Analyst (2018 - 2019)

Digital Garage is a Japanese-based technology company. They established a co-working/event space/innovation center on Market st, San Francisco, called DG717, where I joined as a growth marketing analyst. At DG717, I helped an all-women Marketing team to develop marketing campaigns, launch and operate events, analyze data and conduct marketing performance analytics of all assets and channels.

Working at DG717 was fascinating to me, because I was able to meet with a lot of startups companies, and learn more about technologies such as Blockchain, VR/AR, Security, AI… My enthusiasm with high-tech products just became stronger and stronger.

Vantiq | Product Marketing Manager (2019 - 2021)

Vantiq is a Platform-as-a-Service company, headquartered in San Francisco Bay Area. Vantiq provides a low-code development environment for enterprises to build real-time applications. After graduating from USF, I first joined Vantiq’s partner enablement team to design and build a partner enablement portal. Later, I expanded my responsibilities to marketing analytics and product marketing sides, while managing the day-to-day operations of the portal.

At Vantiq, I truly experienced what it’s like to work at a high-tech startup, in a small team. My tasks weren’t limited by my title. I analyzed data, built buyer/user personas, worked with cross-functional teams to create sales & marketing resources, led product release campaigns, implemented marketing automation programs, designed and executed lead-gen and lead nurturing programs, and more. I also enjoyed supervising and supporting marketing activities for Vantiq Japan and used my Japanese language skills.

Data Science For All | Data Science Fellow (2021-2022)

I knew I wanted a new challenge. I wanted to do more with data! I understand the limitation of analytical tools such as Excel Spreadsheet when it comes to working with large datasets. I decided to explore Data Science. I started to message people on LinkedIn about becoming a data scientist. One of them told me about the DS4A fellowship. I applied and was accepted! Free training? I’m gonna take it!

Data Science for All (DS4A)/ Empowerment is a virtual data training and jobs program, made available for free to qualifying students and professionals from underrepresented communities.

I worked with 3 other team members to learn about (head-scratching) math concepts, machine learning and statistical modeling. It was challenging to learn advanced math/statistics at the age of 30s (haha!), but we did it!

What’s next?

I’m looking to write the next story in my career. Would you like to be a part of it?

book a time to speak with me view my resume

Resume

2021-04-30T22:11:04+00:00

(Coming soon)

Meanwhile, let’s connect on Linkedin.

Contact me

2019-11-18T09:26:37+00:00

contact me about anything

Beside serious stuff like how to build a career in a new country, or how to change your habits to lose weight, connect with me on anything. I’m INFJ, Enneagram number 7, Cancer, non-binary. I enjoy walking, hiking, drinking tea and hot cacao, reading books about body, brain, and psychology, dancing, playing with my ukulele sometimes, cooking healthy food, doing yoga & core training… Oh! And, of course, writing.

direct message

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