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

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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. …


Integrate Excel with Word to generate automated reports seamlessly

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Microsoft Excel and Word are without a shred of doubt the two most abundantly used software in the corporate and non-corporate world. They are practically synonymous with the term ‘work’ itself. Often times, not a week goes by without us firing up the combination of the two and one way or another putting their goodness to use. While for the average daily purpose automation would not be solicited, there are times when automation can be a necessity. Namely, when you have a multitude of charts, figures, tables and reports to generate, it can become an exceedingly tedious undertaking if you…


Run prescheduled Python scripts on Colab and access the results on your own drive

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Introduction

Cloud computing is quickly becoming the weapon of choice for data scientists and firms alike, for all the right reasons too. With the resources that you are afforded and the granularly small fees associated with them, it’s really just a matter of basic arithmetic to discern between cloud and local computing. Over the years platforms the likes of Amazon Web Services, Microsoft Azure, Google Cloud Platform and others have competed to offer the most sophisticated, cutting-edge and yet most affordable services to their customers. Ironically Google as one of the pioneers of the tech industry, was somewhat late to the…


And how you can avoid making the same mistakes

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Introduction

As far as I can remember, I have always wanted to have a side hustle. Something that would give my spare hours purpose and would help raise my bottom line ever so slightly higher. Something that I could run on the periphery to give me that extra bit of fiscal security, knowing that I’m inching closer towards financial independence and an earlier retirement. Mind you, I’m not overly ambitious, lofty or any way delusional, or at least not that I know of. But I was and still am convinced that not every business endeavor has to be a breakthrough. Good…


Using Plotly to render interactive radar charts in real-time

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Introduction

Perhaps one of the unsung heroes of data visualization is the benevolent and graceful radar plot. We’ve grown accustomed to a whole slew of other visualizations, choropleths, donuts and heatmaps to name a few, but radar charts are largely missing from our dazzling dashboards. Granted, there are only particular use cases for such charts, namely, visualizing wind roses, geographical data and some other types of multivariate data. But when you do use one, it is remarkably effective at visualizing outliers or commonality amongst numerical and ordinal datasets.

For this tutorial, we will be generating a dashboard with an interactive and…


Automating the repetitive tasks that you shouldn’t be wasting time over

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Perhaps we can all relate to a landmark moment in our data science careers when we’ve had to spend the good portion of a day generating a multitude of repetitive and tedious reports that could have otherwise been generated automatically. It goes without saying that your time is far more valuable than having to attend to the pesky undertaking of filling in tables, writing headers, sub-headers and figure numbers. Savor the moment by doing something ever so slightly more purposeful, like reading up on TDS or checking out the latest frameworks available for server-less computing. …


Just under a dozen stories later…

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Introduction

I still cannot forget that graceful email in my inbox — ‘UIUC Online Master of Computer Science: A personal post-mortem” has been accepted into Towards Data Science’. That was just under six months ago, and since then I have had the privilege of attributing eight other TDS articles to my name. I’m not a prolific contributing writer, but I do write from time to time whenever a Eureka moment presents itself. My writing also largely depends on my professional workload, I write more abundantly during holidays and most of my articles have been conceived on weekends (excluding this one ironically)…


Streamlit: democratizing app development and empowering programmers

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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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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

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…

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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