Benchmarking is the process of measuring a script's performance in terms of the time it takes to run and memory it requires. It is an essential part of programming and it's particularly useful for developing scalable code. Usually, it involves a more detailed analysis of the code, such as profiling, so we know exactly which parts of the script are run more often and what proportion of the overall time they take. As a result, we know how we can optimize the script using this information, making it lighter and more useful.

Benchmarking is great as it allows us to optimize our scripts but what does this mean for us as data scientists? From a practical perspective, it enables us to work with larger data samples and save time. This extra time we can use for more high-level thinking, refining our work. Also, being able to develop high-performance code can make us more independent as professionals, something that has numerous advantages, especially when dealing with large scale projects. Finally, benchmarking allows us to assess the methods we use (e.g. our heuristics) and thereby make better decisions regarding them.

In Julia, in particular, there is a useful package for benchmarking, which I discovered recently through a fellow Julia user. It’s called Benchmarking Tools and it has a number of useful functions you can use for accurately measuring the performance of any script (e.g. the @btime and @benchmark macros which provide essential performance statistics). With these measures as a guide, you can easily improve the performance of a Julia script, making it more scalable. Give it a try when you get the chance.

Note that benchmarking may not be a sufficient condition for improving a script, by the way. Unless you take action to change the script, perhaps even rewrite it using a different algorithm, benchmarking can't do much. After all, the latter is more like an objective function that you try to optimize. How it changes is really up to you! This illustrates that benchmarking is really just one part of the whole editing process. 

What’s more, note that benchmarking needs to be done on scripts that are free of bugs. Otherwise, it wouldn’t be possible to assess the performance of the script since it wouldn’t run to its completion. Still, you can evaluate parts of it independently, something that a functional approach to the program would enable.

Finally, it’s always good to remember this powerful methodology for script optimization. Its value in data science is beyond doubt, plus it can make programming more enjoyable. After all, for those who can appreciate elegance in a script, a piece of code can be a work of art, one that is truly valuable.

Cloud Computing is the use of external computing resources for storage and computing tasks via the internet. As for the cloud itself, it's a collection of servers dedicated to this task and are made available, usually through some paid licensing, to anyone with an internet connection. Naturally, these servers are scalable, so you can always increase the storage and computing power you lease from a cloud provider. Although the cloud was initially used for storing data, e.g., for back-up purposes, it's used for various tasks, including data science work.

There are various kinds of machines used for cloud computing, depending on the tasks outsourced to them. For starters, there are the conventional (CPU) servers, used for storing and light computation. Most websites use this cloud computing option, and it's the cheapest alternative for cases where more specialized servers are utilized. However, for small-scale data science projects, especially those employing basic data models, these servers work well.

Additionally, there are the GPU servers that are more affordable for the computational resources they provide. Although GPUs are geared towards graphics-related work (e.g., the rendering of a video), they are well-suited for AI-based models. The latter make use of a lot of computations for the training phase of their function. As more and more data becomes available, this computational cost can only increase. So, having a scalable cloud computing solution that uses this type of server is the optimal strategy for deploying such a model.

Finally, there are also servers with large amounts of RAM, like the regular servers, but with plenty of extra RAM. Such servers are ideal for any use case where lots of data is involved, and it needs to be processed in large chunks. Many data science models fall into this application category since RAM is a valuable resource when large datasets are involved. Multimedia data, in particular, requires lots of memory to be processed at a reasonable speed, even for models that don't need any training.

Cloud computing has started to dominate the data science word lately. This phenomenon is partly due to the use of all-in-one frameworks, which take care of various tasks. These frameworks usually run on the cloud since they require many resources due to the models they build and train. As a result, unless there is a reason for data science work to be undertaken in-house, it is usually outsourced on the cloud. After all, most cloud computing providers ensure high-level encryption throughout the pipeline. The presence of cybersecurity mitigates the risk of data leaks or the compromise of personally identifiable information (PII) that often exists in datasets these days.

A great place that offers cloud computing options for data science is Hostkey. Apart from the conventional servers most hosting companies offer, this one provides GPU servers too. What's more, everything is at a very affordable price tag, making this an ideal solution for the medium- and the long-term. Check out the company's website for more information. Cheers!

A functional language is a programming language that is based on the functional paradigm of coding, whereby every process of a program is a function. This allows for greater speed and mitigates the risk of bugs since it's much easier to figure out what's happening in a program as everything in it is modular. In the case of such a program, each module corresponds to a function, having its own variable space. Naturally, this helps conserve memory and make any methods developed this way more scalable.

Functional languages are very important nowadays as people are realizing that their advantages make them ideal in many performance-critical cases. Also, in cases where development speed is a factor, functional languages are preferred. It's important to remember though that many people still favor object-oriented programming (OOP) languages so the latter aren't going to go away any time soon. That's why there are lots of hybrid languages that combine elements of OOP and functional programming.

So far there have been a couple of functional languages that are relevant in data science projects. Namely, there is Scala (where Spark was developed on) and Julia, with the latter gaining popularity as more and more data science packages become available in it. Interestingly, ever since these languages have been shown to provide a performance edge (just like any other functional language), their value in data science has been undeniable, even if many data scientists prefer to use more traditional languages, such as Python.

What about the future of functional programming? Well, it seems quite promising, especially considering how many new programming languages of this paradigm exist nowadays. Also, the fact that there are new ones coming about goes to show that this way of programming is here to stay. Also, since the OOP paradigm has its advantages, it seems quite likely that newer functional languages are bound to be hybrid, to lure more practitioners who are already accustomed (and to some extent vested) in the OOP way of programming. Moreover, functional languages are bound to become more specialized since there are enough of them now to need a niche in order to stand out. In fact, some of them, as for example Julia, appear to have done just that.

If you wish to learn more about the Julia functional language and its application on data science, I have authored two books about it through the Technics Publications publishing house. Feel free to check them out here and learn more about this fascinating functional language. Cheers!

The world of data professionals is sophisticated and diverse, especially nowadays. In involves professionals whose expertise ranges from the design of data flows to databases, data analytics models, machine learning systems, and APIs that connect the users to a cloud-based solution. It's not a simple matter, while the variety and depth of all these roles leave people bewildered and uncertain about what this ecosystem is and what it can do for an organization.

We can attempt to gain an understanding of this world by reviewing the various professionals found in it. First of all, we have the data architects (aka data modelers) responsible for designing data/information flows, facilitating communication among the people in an organization, and developing the infrastructures for all movement and storage of the organization's data. They are often involved in database solutions as well as ETL processes and the creation of glossaries. Data architects are essential in an organization, mainly when there is plenty of data involved, or the data plays a vital role in the organization's workflow. Most modern organizations are like that, while the abundance of data makes these professionals necessary.

Beyond this role, there are also data analytics professionals, particularly data scientists. This sort of professionals is involved in deriving values from the available data, usually through discovering insights. Data scientists are more geared towards messy (e.g., unstructured or highly noisy) data and more advanced models. All data analytics professionals work with databases through focused querying of them, while the creation of visuals based on the data is an essential part of their pipeline. Naturally, this role involves some programming (more so in the case of data scientists) and communication with each project's stakeholders. The creation of dashboards is a typical deliverable in this role, though other kinds of data products are sometimes developed instead.

Data engineers are also an essential kind of professionals in this ecosystem. This role entails data governance, particularly when big data is involved as well as various ETL processes that facilitate data analytics work. Managing containers in the cloud and specialized software like Spark is part of these professionals' job descriptions. Data engineers are heavy on programming and often deal with computer clusters, be it physical or virtual. Their communication with the project stakeholders is relatively limited, although they liaise with data scientists quite a bit. Some data engineers are well-versed in data science methods, particularly the development and deployment of predictive models.

Finally, business intelligence (BI) folks also have a role to play in the data world. This kind of professionals involves liaising with the managers and other project stakeholders. BI professionals tend to be more knowledgeable regarding the inner workings of an organization. Simultaneously, their use of data is limited to basic models, useful graphics, and descriptions of the problem at hand. BI professionals are more related to data analysts, though they tend to be more involved in high-level tasks. Also, their use of programming is minimal.

If you want to learn more about the data professionals' world, I invite you to check out some great books, like those available at the Technics Publications' site. Although geared more towards data modeling, this publisher covers the subject quite well, providing practical knowledge from various professionals in the fields as mentioned earlier. If you use the coupon code DSML, you can get a 20% discount on any books purchased. Check it out when you have the chance. Cheers!

Just like other pieces of hardware, graphic cards continuously evolve, becoming more efficient and more powerful. This constant evolution is partly due to the need for more computing power, whatever the form it can take. Graphic cards are no exception, and lately, NVIDIA has come to dominate this technology scene. This month, a new set of graphic cards by this company (the GeForce RTX 30 series) is making its debut. Naturally, this is bound to have ripple effects in data science, among other fields. In this article, we'll look at just that and see how you can benefit from this new development.

Graphic cards have been used in cloud computing successfully and other machines (e.g., regular PCs). The idea is to use their GPUs for crunching the numbers, instead of just standard CPUs, like those found on a typical computer. Although a GPU is part of a graphics card and designed to handle graphics data, it can be leveraged to handle complex mathematical calculations in various data science models. Particularly AI-related models, such as deep learning networks, have a lot to benefit from GPUs for various reasons. Not only are they fast and efficient (i.e., not consuming much power), but also they are inexpensive and can setups using them scale up very well. So, if you want to train that deep learning network quickly and without costing the moon, GPUs are your best option.

The new GPU servers based on the new NVIDIA graphics card can do this task even better. Featuring speeds up to twice as high as those of the previous generation cards (i.e., RTX 20), they are genuinely efficient. Additionally, they feature more memory (up to 24 GB GDDR6X, which is more than twice as much as the previous generation) and a different architecture altogether (Ampere vs. Turing previously). All this translates into a better experience for the user, particularly if that user has high graphics card demands. As more and more people use graphics cards in their AI-related work, these cards' manufacturers try to address this requirement in their new products. Of course, not all of them succeed, but those that do are big successes. Perhaps that's why NVIDIA has become a name you'd hear not only among gamers but also among data scientists and AI professionals.

Hostkey is one of those companies that have figured out the edge such state-of-the-art graphics cards can offer in cloud computing. That's why it boasts such GPU-powered servers among the various services it offers. Geared mostly towards data scientists, Hostkey has various packages available, many of which involve GPU servers. What's more, lately, it has started to offer servers with one of the latest NVIDIA cards on them (GeForce RTX 3080), which we expect to see released next week. Not only that, but it has a raffle for a free one-month subscription to this service, involving such a GPU server. Check out the company's website for more information. Cheers!

As privacy matters gain more value these days, transparency also gains a lot of value in data science work. This is to be expected for another reason, which I hope it has become more obvious if you have been following this blog: transparent models are easier to explain to others. Beyond these advantages, there are other ones too (such as transparent models are easier to tweak and optimize), which I'm not going to elaborate on right now. Instead, I'm going to look at the various data models used in data science and where they fall on the transparency spectrum.

On the one extreme of this spectrum lie the most transparent data models. These are usually Stats-based since they can provide exact proportions of each feature's contribution. Also, you know exactly what's going on with the decisions involved in the predictions they yield. Even if you know nothing about data science you can still make sense of these models and understand the predictions they yield. The main disadvantage of these models is that they are not as accurate, partly because of the overly simple processes they use.

On the other extreme of the spectrum, you can find the most opaque data models. These are usually AI-based and are referred to as black boxes. Not only do they not tell us anything about feature importance, but trying to explain their inner workings is a futile task. However, they tend to have an edge in performance when it comes to accuracy, plus they require very little prep work for the data they use (data engineering).

Somewhere in the middle of the spectrum lie all the other models, mostly under the machine learning category. These include random forests and boosted trees (some transparency), k nearest neighbor (very little transparency), support vector machines (no transparency), and fuzzy logic systems (pretty decent transparency). That’s a category of models most people forget since they tend to think of transparency as a binary attribute.

Finally, it’s good to remember that transparency is usually linked to a business requirement. Also, sometimes the performance you obtain from the black box models is a good trade-off since some projects require high accuracy in the predictions involved. So, transparency is not always a necessity even if it can facilitate the communication of these models to the project stakeholders. As a result, it's always good to think about whether you need this extra transparency that a statistical model may offer you if you can achieve better performance with a less transparent model.

For more information about transparency and other aspects of data science models (particularly machine learning related), you can check out my latest book, Julia for Machine Learning. It is a very hands-on kind of book, which doesn't neglect to provide a lot of information needed to build the right mindset when it comes to data science work. Also, it includes lots of examples and links to useful resources that can help you understand all the concepts involved.

Data analytics is the field of analyzing data and using any insights you've discovered to facilitate an organization's workflow. It has a very hands-on approach to things and focuses on describing a problem accurately and liaising with the stakeholders to drive decisions based on the data analyzed. Data science is akin to that, while it employs the scientific method to go deeper into the data and develop more sophisticated strategies to drive those decisions.

Nowadays, the roles of the data analyst and the data scientist are somewhat mixed since the latter is still relatively new. The fact that its evangelists haven't publicized it accurately makes it even more challenging to understand how it fills a somewhat different niche as a role. A data analyst is more wide-spread as a role and ties to a large variety of tasks, including marketing analytics (e.g., SEO) and business intelligence (BI) work. An organization usually leverages a data scientist in cases with more unstructured data (e.g., text) or data from various forms, making data wrangling a necessary part of the analysis. Also, in scenarios where the objectives are not as clear-cut (e.g., predict the sales of the next quarter), a data scientist is usually preferred. However, it's worth pointing out that many data scientists start their careers as data analysts and that both roles are necessary. After all, they both work with data to produce insights, even if they often go about it differently.

Beyond the differences mentioned previously, another critical difference between the two roles is the models used. Namely, the data analyst is more geared towards describing the data and understanding the problem it represents. The data scientist goes a step further and also makes predictions (through mathematical models, usually based on machine learning), digging deeper into it. As a result, she can put together a complete solution, such as a predictive model accessible through an API. Naturally, she can also create a dashboard, something that is among the data analyst's deliverables.

What's more, although data analysts can tackle all sorts of data, usually when it comes to text and semi-structured data, that's where they draw the line. For this sort of datasets, specialized methods are required, such as Natural Language Processing (NLP), which falls in the data scientist's domain. The use of AI is often essential in problems like this, something that a data scientist is usually required to know, but beyond the job description of a data analyst.

You can learn more about data science and the data scientist's role through a couple of my books. Specifically, the book Data Scientist: The Definite Guide to Becoming a Data Scientist, illustrates the ins and outs of this role and some practical advice as to how you can pursue a career in this field. Additionally, the Data Science Mindset, Methodologies, and Misconceptions book showcases the field overall and its defining aspects as well as the essential techniques used. Both books together offer a birds-eye view of data science and how you can build your career in it. Check them out when you have a chance. Cheers!

Statistics is a very interesting field that has some relevance to data science work. Perhaps not as much as some people claim, but it's definitely a useful toolset, particularly in the data exploration part of the pipeline. But how can Stats improve, particularly when it comes to new technologies like A.I.?

Statistics can benefits from such technologies in various ways. The most important is the mindset of the AI-based approach, which is empirical and pragmatic. Instead of imagining complex theories for explaining the data, AI looks at it as it is and works with it accordingly (the data-driven approach).

Conventional Statistics on the other hand tries to fit the data into this or the other mathematical model for describing it and then it processes the data with some arbitrary metrics that are mediocre at best. However, the math is elegant, so we go with it anyway. So many textbooks can’t be wrong, right? Well, in data science there is no right or wrong, just models that work well and others that don’t work as well. Since there is usually money on the line, we prefer to go with the former models, which coincidentally tend to be machine learning related, particularly AI-based. So, there is surely room for improvement for Statistics if it were to adopt the same mindset.

What's more, Statistics can benefit from A.I. through additional heuristics (or statistics, as they are often referred to in that context). The existing heuristics may work well, but they are very narrowly defined, making them overly specialized. AI-based heuristics are broader and tend to be more applicable in a variety of data sets. If Statistics were to adopt a similar approach to heuristics, it would for sure benefit greatly and become more widely applicable.

Finally, Statistics can benefit from A.I. by embracing a different approach to describing the data. This is a more fundamental change and probably most fans of the field will disregard it as impractical. However, it is feasible and even efficient, with a bit of clever programming (based on heuristics and a geometrical approach). The latter is something Statistics seems to be divorced from, which is another area of improvement.

It's worth noting that although developments in Statistics are bound to be beneficial to anyone applying it in data analytics projects, the practitioner also needs to evolve. There is no point in advancing this field if its practitioners remain in their old ways, limited and rigid. Perhaps that's one of the reasons machine learning and A.I. have advanced so much; their practitioners are more open to changes and willing to adapt. No wonder these fields now dominate the data science world. Something to think about...

PS - This article was supposed to be published yesterday. However, there was an issue with the scheduler, hence the delay. Normally, I'll have new material every Monday and sometimes on Thursdays too. Cheers!