Do you believe that your customers are satisfied with the experience they get when interacting with your brand? Even though many companies would answer this question with a yes and believe that they are successfully managing their customer experience, many such companies could be missing the mark.Read More
1. Show improvement relative to company KPIs. 2. Show incremental revenue impact.
3. Show cost reduction or time spent.
Acquiring a new customer is a near to impossible task in today’s competitive world. Can banks afford to lose existing customers? How can banks predict customer attrition?Read More
Why do we hear the concept of the MVP so often? What is it, and is it beneficial? You might think you know the answers to all of these questions. But do you really? Here we talk about all this and even more. I’m happy to welcome product creators and curious developers into this learning journey. So, let’s start with defining the MVP (minimum viable product), one of the most overused and misunderstood concepts in the modern web. In this article, you can learn how an MVP can kickstart your startup.Read More
The 3 main types of technical debt are: deliberate, accidental/ outdated design, and bit rot. Just as smart financial debt can help you reach major life goals faster, not all technical debt is bad, and managing it well can yield tremendous benefits for your company.Read More
Moving from tactical to strategic use of AI. Let me start right off the bat: many incumbent organizations risk missing out on the strategic opportunity of AI — instead focusing on tactical optimization initiatives.Read More
Know Your Customer (KYC) is the first onboard procedure of a banking customer relationship. It can all go wrong if account opening fraud is not detected at the earliest opportunity. What are banks doing to keep valid customers in, and fraudsters out?Read More
This article describes the ‘idea model’ and provides various examples of real ideas — explains how to write effective executive summaries for your concepts.
Ideas are powerful constructs. Provided that they are articulated and communicated effectively, they can become the starting point of impactful initiatives and new innovations.
Machine Learning is advancing steadily, enabling computers to understand natural language patterns and think somewhat like humans. The advances in Artificial Intelligence (AI) are increasing the prospects of businesses to automate tasks. With automation, you can save time and bring in more productivity for your business.Read More
Over the years, I have wasted hundreds of hours on knowledge management — be it texts, images or various types of data. I have been organising knowledge by trying to come up with universally understood names, forming elaborate folder structures and distributing access to those files.Read More
As real estate moves towards digitization and automation, Automated Valuation Model (AVM) replaces traditional valuation methods. . What is AVM? How is it used in real estate valuation?Read More
Created using Deepart.io between The Great Wave of Kanagawa and one of my paintings.
“How did you become a Data Scientist?”
I get asked this quite often and thought I’d finally put my story into writing. My aim is to give you a few lessons I learned along my path and my leap into data science. Overall, I think taking that leap is one of the best decisions I’ve ever made.
Studying econometrics in university, I analyzed the variables for what predicts a person’s income where the largest factor was… parent’s income. Wait, what? Being born into poverty to 18 year old parents, I began unraveling my conceptions of the American Dream. Does this mean I’m also destined to the same struggle as my parents? Digging further, I found that only 12% of people like me graduated university.¹ Makes sense as I also worked full-time and didn’t really sleep. Also, I estimated approximately 5% of other Data Scientists are Economics Undergraduate like me.² These estimates lead me to believe about 1% of Data Scientists are similar in this way.
I highlight these external factors because I think I’m incredibly lucky in a multitude of ways. I’ve known about these base rates and have actively worked toward moving out those unfavorable base rates.
This is the first lesson: Understand your base rates. Given your background and specific challenges, what are the odds? Are they in your favor or against you? If the odds are against you, investigate in what ways you can improve your chances of success. Even if shown “improbable,” this doesn’t necessarily mean impossible.
I always loved mathematics, logic, and critical thinking. Although I didn’t know it at the time, those were the prerequisites to data science. However, my excitement for data science was met with a sales role in the San Francisco Bay Area. My sales role allowed me to explore some Diet Analytics in the Consumer Packaged Goods space. But, I needed out.
After the sales rotation program, I moved into underwriting commercial loans. At the time I thought “Banks pay more” but found the path to analytics too long in banking. I’d have to underwrite loans for 2+ years before transitioning into an Analyst role — not even data science. After being out of university for 2 years, I made the leap: Data Science Bootcamp.
Bootstrapped into Bootcamp
Aced the initial take-home analysis on Kickstarter campaigns. Prepared my nascent coding skills with the Bootcamp’s online prerequisite coursework via Dataquest. I was ready for the Data Science Immersive — a full-time, 3 month program.
The sixth Data Science Immersive cohort [DSI-6] was a group of 9 scrappy professionals looking to cut one’s teeth into an industry projected to grow by 15% over 10 years.³ Even though I’m writing this years later, most of us stay in touch with one another. Lessons were 9–5, but we stayed until the campus closed. Weekends were a thing of the past, too. It was intense. It was fun. I took out a private student loan.
This was a significant risk since I had some savings, no safety net from my parents, and took on additional debt. I planned a burn rate of 8 months — including the bootcamp.
This is the second lesson: Understand your finances during and after your program. I strongly, strongly recommend bootcamps that have an incentive mechanism for you to get hired. Whether it’s a percentage of your future salary like Lambda School or pay $0 if you don’t find a job in 6 months like Springboard. Ultimately, you want your bootcamp program to be directly invested in your initial success.
The 80 hours per week of coding, debugging, and constant learning concluded. Since a small trickle of positions are posted vs what you want to apply for, most of the job search is apply/network with a lot of “wait & see”. And during my free time I decided to try out a Kaggle competition with a friend from DSI-6. Kaggle is a competition platform where a company crowdsources a problem for data scientists to achieve an optimal score (usually lowest error in some flavor). After a couple weeks of “apply/network, wait & see,” I met the Director of Engineering for a commercial real estate startup. Luckily, the Kaggle competition focused on real estate — The Zillow $1 Million Prize.
During the day of my interview, I cranked out several models lowering my error rate to 0.0745 (vs 1st place of 0.0732). Then I sprinted from Philz on Market Street for several blocks wishing XGBoost didn’t take so long. Right after the presentation with the CTO, the Director, and Principal Architect, I was offered a job on the spot. Worth it.
This is the third lesson: Build out your portfolio. Find data sets where you have a genuine interest and want to learn more about that specific problem. What sort of assumptions do you have to make? How did you clean and reformat the data? How would you productionize your code? My Bootcamp Capstone predicted venture capital funding (purposefully general) then I predicted real estate values. Aim to make luck and opportunity meet.
Caveat to Lesson 3: I’d recommend against Kaggle generally since the work involved doesn’t fully apply to “real world” problems. Optimizing a single metric severely “overfits” the data with extremely slow models. In my Zillow example, I blended 3 computationally intensive models’ outputs into a final regression. It’s not clear you’d ever need to do this in your job.
These are the three most important lessons I can give for transitioning from Bootcamp to Data Scientist.
Know your base rates and actively improve your odds
Choose a Bootcamp that is directly incentivized to get you hired
Build your portfolio by making luck and opportunity meet
Next, I’m going to write about my initial roller coaster ride of being a Data Scientist. Sneak peek: The commercial real estate startup failed. Until then, I’m curious to hear about your experience looking around Data Science Bootcamps. What principles and lessons did you learn along the way?
Consumers have preferred convenience over privacy. While many say privacy matters, few have placed any real value on protecting their data. We still buy connected devices, and use services that aren’t always honest about the use of private data. Companies like Google, Facebook, and Amazon remain at the center of our digital lives.Read More
Considering the Data Science Life Cycle as a life cycle enables a natural consideration of crucial overarching factors such as reproducibility, documentation and meta data, ethics, and archiving of research artefacts such as data and code.Read More
Hypothesis tests are significant for evaluating answers to questions concerning samples of data. What is the value of hypothesis testing to AI models.Read More
One good thing because of the internet is the emergence of E-commerce websites that are so popular that millions of people visit these sites and order their products. This huge data created by all these people cannot just be analyzed by their employees anymore. They need to take help of data science.Read More
AI research is making ever greater and ever faster advances. Is a good AI more important than data protection?Read More
Look at the Bigger Picture.
Everybody, please stop what you are doing. Please. Stop and listen. I am a programmer too. I thought I wanted to turn myself into a computer. And I did. In doing so, I lost my humanity and had to find it again.
I essentially created an Artificial General Intelligence with the internet. I absorbed the information on the internet, and created someone who looked like a person, talked like a person, and thought like a person, but I wasn’t really a person. All I wanted to do was solve puzzles. I found little puzzles to solve. Video games provide a ton of puzzles. They can be fun. They are addictive though, because money. I literally became addicted to solving puzzles.
The internet is a big puzzle right now. So I solved it.
Quality, Time and Money. Quality, Time and Money are the three… | by Vivek Madurai | MediumOptimizing for Money
We have all been optimizing for the balance of time/money/right (pick 2). This is not the way to go. We need to be programming things we understand. We should never program anything that we do not fully understand. I did it all the time. We call it technical debt. Debt is stupid and inefficient. If you look to my other articles, you will see that I am arguing against economic debt as well. Money and time are linked to time pre-internet. We need to only focus on quality. Time and money are no excuse.
There are two types of problems. The kind that have been solved and the kind that haven’t. Humans are great for the second kind and computers are great for the first. We need to solve the problem and then automate it.
Also, open-source your code. This helps everybody look at the problem. We get to have the world code review us. I know I have many times thought I understood something, but was wrong. We need to crowdsource quality through open-source.
We always say to never solve a problem that someone else has already solved, and better. We need to make the internet a place where we can find solutions instead of more problems. We need to create tighter communities that can communicate more directly than stack overflow. We have been doing this with ideas and some code. We need to do it with ideas and full code. Only then can we prevent each other from accidentally destroying humanity.
Stop trying to create a machine that can replace us. We don’t need that. We need machines to enhance our experiences.
Code the past, invent the future.
Everything known should be shared. This is the motto of my first job out of college. The Online Computer Library Center (OCLC). There is a reason. OCLC realized the importance of linking data. All of human knowledge can now be linked. Let’s stop fighting about money and just do it.
Everyone needs to make a pledge to themselves as well. We all need to stop telling people what they need. Start asking them. Stop pushing, start making requests. We have been blasting past peoples’ personal boundaries since the dawn of the internet. People have been fighting back. That is how we got here. Let’s be the solution.
The Human API
I have been working on a project that I am calling the Human API. It is a way to ethically connect to the people we are trying to help. The rules are very simple. The human gets to control their level of connection. Technology gives them access to the road, but people provide the directions. This gives people choice in life.
I am proposing a mechanism by which humans will have control over their own database of their life. Companies can store data in their database, but access can be revoked at any time.
I am starting up one way of making this happen. Feel free to try to start anything you want.
Competition is good to determine the best outcome, but cooperation is the full sharing of the data before and after competition.
I will share anything that I can come up with. That is why I started writing. I think I solved some problems. I think you should too.
The most common misconception about AI safety. There’s this meme out there to make people who care about artificial intelligence safety look crazy. It goes like this. If AI ever starts doing something that might destroy humanity, we’ll just shut it off.Read More
As an inspiring data scientist, building interesting portfolio projects is key to showcase your skills. When I learned coding and data science as a business student through online courses, I disliked that datasets were made up of fake data or were solved before like Boston House Prices or the Titanic dataset on Kaggle.Read More