No matter how talented you are at crunching numbers and writing code, your effectiveness as a data scientist is limited if you chase questions that don’t actually help your company, or you can’t get anyone to incorporate the results of your analyses. Similarly, how do you stay motivated and relevant in a field that’s constantly evolving?
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In the last two decades, a lot of robust methodologies and frameworks for project management have established their roots deeply in the market. And to get effective collaboration and team management in the workplace, many Industries prefer methodologies to accomplish the project. However, having several methodologies as options makes the task hard, especially when each of them is unique in one way or the other.
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Let’s start by saying that perhaps 99% of what’s called AI these days, even the most fashionable ‘deep learning’ — merely amounts to a vastly complicated statistical analysis of humongous amounts of data enabled by the unprecedented drop of the costs of storage and compute (bytes/$ and FLOPS/$ respectively).
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Building simulations are not only really cool, but quite relevant with the pandemic! Not only are Python simulations very beneficial to your coding fluency and your understanding of data science, but they are also fun and addictive to play around with.
There are a myriad of scenarios and factors you can simulate, often with less than a couple hundred lines of code. For example, I have an article about simulating a basic pandemic and predicting population control, both of which have code which you’re free to see!
This is the second part of our series on scaling ML teams! Read on if you’re a leader in an ML team that is starting to grow beyond 10 people and scale from one team to a collection of teams working on the ML pipeline. If you’re a smaller team or you haven’t seen our first post on scaling from 0–10 people, you may want to read that first.
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If you are interested in value investing, you probably know that fundamental analysis is the most effective and at the same time, the most complicated way to determine the intrinsic value of a company. But this approach takes a lot of time and effort, limiting the investor’s horizons. To solve this problem, I started the COVANN project. Today, it is a cascade of artificial intelligence models whose task…
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When I initially began as a product owner, it was unclear how to take these requests. I had not been in this position before, so it was a learning curve.
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As you begin to work on a data science project, it may be helpful to draw on an SME to better understand the data you are analyzing and how to best look at a problem. As mentioned in the previous section, if general knowledge is insufficient to conclude your analysis with your dataset, then stop.
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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.
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.
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In most DevOps settings you’ll find that there are multiple environments in the pipeline. You might have conditions that change the environment based on which branch was merged or when a branch is tagged for release. There are a number of reasons you want to have more than just a production environment, the biggest reason being testing.
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Thinking is probably one of the hardest, daunting tasks, and George Bernard Shaw had his perspective that made me think more about my way of dealing with challenges.
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When you’re wondering if something can be automated or not, a way of looking at the situation is like a puzzle.
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This is my complete guide to Depop Bot (a.k.a. Depop Sales Bot) in 2020. So if you’re interested in using a Depop Bot, you’ll love this guide. Let’s get started.
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I’ve always been a supporter of well-curated newsletters. They give me an opportunity to get a good overview of what happened in the fields I follow within a span of a day, a week or a month. However, not all the newsletters fit this category.
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The novel coronavirus (COVID-19) outbreak is causing many companies to ask their employees to work from home. Apple, Twitter, and Facebook are some high-profile names that have closed their offices in an effort to protect their workers and curb the spread of the deadly disease.
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Whether your company has been planning to be remote-based for years or the need to adapt came suddenly, remote work is an opportunity for any business to operate with more efficiency, resilience, and flexibility.
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With time, patience, and lots of learning along the way, I realized that doing more is actually doing less by focusing on things that matter and having a solid control over the process to achieve desired outcomes.
That’s when I discovered what productivity is all about.
Every day, I go through my routines. Repeat. In the world of data science, most of the work I do is exploring unknown realms. The workflow is more or less similar to every project iteration but there are 7 habits of Data Science you just should not miss.
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Hacking your motivation is a trial and error process to see what works for you. Don’t take everything on the books or even my own advice. Try it and keep what works for you. Start hacking your future now.
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