Get Smarter with Data Science — Tackling Real Enterprise Challenges
The ‘Data Science Strategic Guide — Get Smarter with Data Science’is envisioned as a series of articles, which serve to be more of a strategic guide depicting essential challenges, pitfalls and principles to keep in mind when implementing and executing data science projects in the real-world. We also focus on how you can get maximum value from data science and artificial intelligence by focusing on very real perspectives and staying far away from the hype. This should enable you to drive success in the industry in your own domain! The focus here is more towards real-world projects being executed in the industry, however some of these principles are also applicable towards research.
Typically, most of my articles are hands-on oriented, targeted towards people building systems and actually doing data science. However, this guide is aimed at a broader audience including executives, business, architects, analysts, engineers and data scientists. In my opinion and experience, you need all of them for executing data science projects successfully and getting the maximum value!
If you answered yes to at least one of these questions, this guide is for you! We will be covering the following points in this guide through a series of articles:
All opinions expressed in this guide is based on distilled personal experiences and by looking at industry trends and talking to industry experts. The intent of this guide is not to spread any bias or prejudice but to just give a clear idea of the core components to examine when executing a data science project in the enterprise. I still don’t consider myself to be an expert in this domain (there is no much to learn!) but I hope this guide helps you gain some useful perspective towards effective execution of data science projects. In this particular article, we will focus on some very real challenges in the industry with regard to executing data science projects and some potential solutions.
Most data scientists (including myself) love the availability of ready to use tools, libraries and frameworks. We have our own personal preferences when working on solving different problems. Due to ad-hoc usage of these tools and methodologies coupled with our preferences, it leads to increased effort when actually trying to move towards deploying and maintaining data science project artifacts and assets.
In this section we will take a look at some of the most important challenges and pitfalls which often plague data science projects from never coming out of the proof-of-concept phase and some guidance on how we could tackle them.
The technology landscape for data science tools is huge and is getting even bigger each day. Throw in big data, artificial intelligence and several more terms and you see an even bigger landscape of tools, libraries and frameworks. In data science, thinking about a data scientist’s perspective, it’s all about using the best possible (or most easy to use and comprehend) tool for solving a problem, because methods — statistical or machine learning deep learning are all built on top of math, statisitics and optimization and as long as the implemented algorithms or methods are uniform, the specific tool or framework use doesn’t matter to them that much. I’m sure you must have heard of the saying, ‘Don’t focus on tools and frameworks. Focus on the problem to be solved!’.
This is not wrong. Typically a data scientist (like myself) might like to start looking at the data, open up R or Python and start writing code to do some analysis or build a model. We have two extremes typically based on usage patterns among folks doing data science. Some like using programming languages like R, Python, Scala or Java coupled with frameworks and libraries which enable them to do complex analyses at ease. Others like using ‘no-code, graphical drag and drop interface’ based tools like KNIME, RapidMiner, Weka and so on.
