With the increasing amount of data and the increasing awareness of data-driven culture, global businesses strive to adopt a data science approach. Undoubtedly, data-driven intelligence has become the highest parameter to succeed in the digital world. However, Covid changed the world overnight. Most data science models became useless--at least for some time. Everyone raced to retrain and redeploy their existing data science models.

Britannica describes artificial intelligence as the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The trouble in this definition is the term intelligent. How do you define intelligence? How do you measure intelligence? You can say something is intelligent if it can do descriptive, diagnostic, predictive, and prescriptive analytics at the same time or within a short timeframe.

When looking at data scientist salaries and data science roles, it became obvious that there are different, more specific facets within data science. These facets relate to unique job positions, specifically, machine learning operations, NLP, data engineering, and data science itself. Of course, there are even more specific positions than these, but these can give you a general summary of what to expect if you land a job in one of these positions. I wanted to pick these four roles, too, because they can be separated well, almost as if it was there was a clustering algorithm that found jobs that were the most different between one another but that were also in the same population. Below, I will be discussing the average base pay with a low and high range, as well as respective seniority levels, the number of estimates used to determine these numbers, and expected skills and experiences for each role.

While working on numerous data science projects, I have seen that that most data scientists adopt a haphazard approach when they work on a data science problem. While it is understandable that Data Science is both art as well as science, it is important to have some method to the madness. Most people I see just take the data and start throwing algorithms, hoping that they would achieve success through brute force. In most cases, this does not result into a favorable outcome. The business users also get frustrated and then it would appear that data science is nothing more than a fad.

Much has been written about the power of our massive data collections to enable the 360 view of our customers, our business, our employees, and our processes. When our numerous disparate heterogeneous data collections are aggregated and joined in our data lake or data cloud or data fabric or wherever, with appropriate data tagging, data discovery and data integration tools in place, then we can reach for that ideal: the 360 view of our domain! But is the "360 view" really the right goal? It is definitely a good target and we should incentivize productive work toward that ambition, but should we go all the way to achieving that full 360 view in all projects, at all times? Most of us have probably learned by now the truth in the statement "the perfect is the enemy of good enough."

Text data is one of the largest forms of unstructured data and is ever-growing. At Reorg, I work with large amounts of financial text data every day. One challenge of working with text data is that you need a large training data set to build robust models. You also need good, organic training data, which will be described in further detail in this article. Machine learning (ML) models are only as good as the data used to train them.

As the world of Data Science progresses, more engineers and professionals need to deploy their work. This can be due to testing, obtaining user input, demonstrating model capabilities, or deploying a model to production. Due to this, we need to understand and know how to take a Data Science model and deploy it to a Web App and API using some of the most in-demand and popular libraries, including Flask, NodeJS, and ReactJS. Being able to deploy models will make a DS more versatile and in-demand, but it will also benefit the development and ops teams within the company. In this course, we will take a DS model and learn how to deploy it in a practical and hands-on manner, allowing us to simulate a real-world scenario that can be applied to industry practices.

Overlap between these two popular tech roles is sure to happen, so let's dive deep into what skills are required for both roles and what makes them different. In general, data scientists can expect to work on the modeling side more, while machine learning engineers tend to focus on the deployment of that same model. Data scientists focus on the ins and outs of the algorithms, while machine learning engineers work to ship the model into a production environment that will interact with its users. I will be describing these top skills by personal experience in 2021. I have seen a lot of articles communicate other skills and tools that data scientists use, but I want to describe the ones that most people I know, including myself, use daily.

I am often asked about the state of data science and where we sit now from a maturity perspective. The answer is pretty interesting, especially now that it's been more than a year since COVID-19 rendered most data science models useless -- at least for a time. COVID forced companies to make a full model jump to match the dramatic shift in daily life. Models had to be rapidly retrained and redeployed to try to make sense of a world that changed overnight. Many organizations ran into a wall, but others were able to create new data science processes that could be put into production much faster and easier than what they had before.

It's highly unlikely that business owners are going to read this and begin to change their perspectives on how we define Data Science. Not because I doubt my influence or anything, but since I'm aware that the majority of my readers are at the beginning of their Data Science journey -- I really dislike the term "aspiring" -- but here is what I wish to tell you all… Stop trying to be good at everything in Data Science, and pick 1 (max 2) area's you want to specialize in and get really good at it! Let's face it... Breaking into Data Science is difficult for a number of reasons. However, I've come to a realization recently that much of the difficulty lies in the fact that the term "Data Scientist" encompasses so many different technical qualities that make it virtually impossible for one individual to meet all these criteria and stay up to date in each area -- and that's okay! I've been listening and speaking to Vin Vashishta, Chief Data Scientist and LinkedIn Top Voice 2019, and he believes that for roles to be defined better then more specialization amongst practitioners must occur.