An aspiring Full Stack Developer’s guide to quickly developing and deploying scalable web applications

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Photo by Chris Ried on Unsplash

There used to be a time not so long ago when creating web applications was the work of child prodigies the likes of Mark Zuckerberg and Elon Musk. Or alternatively, you could enrol in a fancy college, spend the best four of years of your life (and your parent’s retirement savings) learning programming and then end up making subpar 90’s style web apps. Well, we’ve come a long way since then. With the inundation of open source tools and cloud infrastructure, developing and deploying phenomenal applications has been largely democratized. …


Streamlit: democratizing app development and empowering programmers

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Visual by author.

Introduction

I have been a programmer for fourteen years now, with a good six of those having mainly revolved around Python one way or another. I have come to rely heavily on Python and religiously employ its goodness in every aspect of my work as a software developer. In that time, I have witnessed colleagues getting bottlenecked by Excel while I raced ahead with Pandas. I observed peers using lousy mouse recorders to imitate repetitive web scraping tasks while I dispatched Selenium to that end. …


A simplified approach to provisioning robust and scalable data warehouses

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Photo by Nana Smirnova on Unsplash

Introduction

With the abundance and proliferation of data in this day and age, there is an inherent need to store and reuse that wealth of information in a meaningful way. It is analogous to having a kitchen inundated with a multitude of utensils and tools to use, without having an organized way to manage them. Well, chances are that you’re going to end up opening your canned lunch with the rear end of a dipper, unless you warehouse up real fast.

Data warehousing is the ability to cache, tokenize, analyze and reuse your curated data on demand in an unparalleled manner…


Using Plotly to create a heatmap visualization of monthly and hourly data

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Visual by author.

Introduction

Anyone who has ever been exposed to the data, knows that time series data is arguably the most abundant type of datum that we deal with on a routine basis. Data that is indexed with date, time and/or both is thereby classified as a timeseries dataset. Often, it may be helpful to render our timeseries as a monthly and hourly heatmap visualization. Such powerful visualizations are supremely helpful in being able to digest data that is otherwise presented in form that may not be ingested into our highly visual selves. These renderings, will usually depict hour horizontally, month vertically, and…


Implementing a visual datetime filter for timeseries data in Python

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Visual by author.

Introduction

Perhaps the most proliferated type of data that we grapple with on a daily basis is timeseries data. Basically, anything that is indexed using date, time or both can be considered as a timeseries dataset. And more often than not, you may require to filter your timeseries data with, well, date and time themselves. Filtering your data frame based on any other form of index is a rather trivial task; the same cannot be stated about datetime however, especially when the date and time are quoted in different columns. …


Using Streamlit to develop dynamically updated data visualizations

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Photo by Stephen Dawson on Unsplash

Introduction

For those of us who have heralded all the bells and whistles brought into existence by Streamlit, from its ease of use to its ability to create powerful visualizations, we’ve largely overlooked one really handy feature. Its ability to render dynamic dashboards that can be updated in real time. I gather that most users are using Streamlit to create static dashboards or at most are utilizing the multitude of widgets available to redact dataframes that are then fed back into their visualizations. But I haven’t really seen anyone using one of the greatest inherent capabilities of Streamlit - to create…


Using PostgreSQL to create stateful, multi-page applications with Streamlit

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Photo by Mitchell Luo on Unsplash

Streamlit

Streamlit has come a long way since its inception back in October of 2019. It has empowered the software development community and has effectively democratized the way we develop and deploy apps to the cloud. However as with all new tools, there is still some way to go, and while the Streamlit team works tirelessly on addressing requests for new features, we developers ourselves can create ad hoc work arounds in the meantime.

A feature that Streamlit currently lacks, is the ability to implement programmable state for its apps. In its current form, there is no internal state that can…


The short answer is yes, but there's more to it than meets the eye

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Photo by Cookie the Pom on Unsplash

Can I take the plunge? Will my engineering background help me in making the cut? Can I jump on the data science bandwagon?

If you see yourself asking any of these questions, then you’ve probably arrived at an increasingly common junction in your STEM career. There are many of us who have been mesmerized by how impactful and ubiquitous data science has become in our lives and feel the urge of somehow adjusting our careers to it. Truth be told, I was one of those people several years ago. Having come from a engineering background myself with several years of…


A review of the University of Illinois at Urbana-Champaign’s Online Master of Computer Science in Data Science

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Photo by Myriam Jessier on Unsplash

In a post-COVID dystopia or utopia depending on which half of the cup of water you’re looking at, the relevance of an online medium through which a degree can be administered, completed and conferred cannot possibly be overstated. A once-in-a-century pandemic aside, there is a multitude of other reasons for which one could contemplate enrolling in an online program. Namely, being a working professional with limited time, being a parent with children to juggle or simply not having access to a world-class university at your doorstep. …


Artificial Intelligence, Programming

Developing an AI Fintech application to analyze and determine the financial health of banking clients

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Report generated by Bank Scan

In a world where 2–3 billion people are underbanked, including 25% of households in the United States, the need to provide intelligent banking analytics cannot possibly be overstated. For those who may not be familiar with the term ‘underbanked’, it is defined as an individual with access to a bank account but with limited financial services and insights. Especially for many of us millennials, the absence of a solid financial advisor such as the likes of Apple Pay, can see us siphon off our wealth at warp speed.

Ostensibly, not everyone has access to advanced analytics provided by their bank…

M Khorasani

Hybrid of a data scientist and an engineer. Logistician. Candid. Realpolitik. Unlearning dogma one belief at a time. www.linkedin.com/in/mkhorasani

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