Believe it or not, SVG is a machine-learning algorithm, and we’re here to both prove it and clarify the confusion surrounding this notion.

Some might wonder how SVG, a widely known design-based algorithm, could be related to machine learning. 

Well, hold on to your hats because we’re about to dive deep into the fascinating world of SVG, fonts, design, and machine learning.

In this post, we’ll explore the connections between these two seemingly unrelated fields, and we promise that by the end, you’ll have a whole new appreciation for SVG and its unique role in machine learning. 

Stay tuned for an exciting journey that will challenge your preconceptions and shed light on the hidden depths of SVG!

What Is SVG, and where did it come from?

The origins of Scalable Vector Graphics (SVG) can be traced back to a groundbreaking research paper that aimed to model fonts’ drawing process using sequential generative vector graphics models.

This ambitious project sought to revolutionize our understanding of vision and imagery by focusing on identifying higher-level attributes that best summarized various aspects of an object rather than exhaustively modeling every detail.

In plain English, SVG works as a machine learning algorithm using mathematical equations to create vector-based images.

Unlike raster graphics that rely on a grid of pixels to represent images, vector graphics are formed using paths defined by points, lines, and curves.

These paths can be scaled, rotated, or transformed without any loss of quality, making them highly versatile and ideal for graphic design applications.

SVG’s machine learning aspect comes into play through its ability to learn a dataset’s statistical dependencies and richness, such as an extensive collection of fonts.

By analyzing these patterns, the SVG algorithm can create new font designs or manipulate existing ones to achieve desired styles or effects.

This is made possible by exploiting the latent representation of the vector graphics, which allows for systematic manipulation and style propagation.

It also brilliantly plays off of traditional epoch training, where each new “design” can be an entire training session of the data. While formal machine learning has low expectations for some of the first outputs of a trained model, these seemingly un-trained representations can have unique designs.

SVG is a powerful tool for creating and manipulating vector graphics and a sophisticated machine-learning algorithm. 

Its applications in the design world are vast.

It continues to revolutionize the way we approach graphic design by enabling designers to create, modify, and experiment with fonts and other visual elements more efficiently and effectively than ever before.

Why The Internet Is Wrong, and SVG is a machine learning algorithm.

Despite the clear evidence provided by the research paper authored by Raphael Gontijo Lopes, David Ha, Douglas Eck, and Jonathon Shlens, a quick Google search may lead you to believe that SVG is not a machine-learning algorithm.

However, this widely circulated misconception couldn’t be further from the truth.

As stated in the paper, SVG employs a class-conditioned, convolutional variational autoencoder, which is undeniably a machine learning algorithm. Variational autoencoders (VAEs) are a type of generative model that learn to encode data into a lower-dimensional latent space and then decode it back to its original form.

In the case of SVG, this algorithm captures the essence of fonts and other vector graphics, enabling the creation and manipulation of these designs more efficiently.

The SVG algorithm is not just any ordinary machine learning algorithm; it can be considered state-of-the-art.

By harnessing the power of convolutional neural networks (CNNs) and VAEs, SVG has demonstrated remarkable capabilities in capturing intricate patterns and dependencies within large datasets of fonts and other graphics.

This makes it an invaluable tool for graphic designers and researchers, as it facilitates generating new designs and exploring creative possibilities.

So, the next time you come across information suggesting that SVG is not a machine learning algorithm, remember the groundbreaking research by Lopes, Ha, Eck, and Shlens that proves otherwise.

In fact, SVG is not only a machine learning algorithm but a state-of-the-art one with the potential to revolutionize how we approach graphic design and push the boundaries of our creative capabilities.

Link To The Paper:

https://arxiv.org/abs/1904.02632 

Why You Should Be Careful Trusting Anything You See

The misconception surrounding SVG being unrelated to machine learning is a prime example of why it’s essential to approach information on the internet with a critical eye.

While the internet is an invaluable resource for knowledge and learning, it’s also rife with misinformation and half-truths.

Before accepting anything you read or see online as fact, make sure to verify its accuracy by cross-referencing multiple sources or consulting reputable research papers and experts in the field.

Being vigilant in your quest for accurate information will help you avoid falling prey to misconceptions, form well-informed opinions, and make better decisions in other aspects of life.

While they may seem similar, they are actually fundamentally different fields. 

Choosing the right path to study can significantly impact your future career, and making the right choice can cut down the time it takes to get one of these jobs A TON.

But don’t worry; we’ve got you covered! 

In this blog post, we’ll break down the key differences between data science and machine learning and help you decide which is right FOR YOU. 

Keep reading to find out which field is best for you, why we separate these two, and some extra information so you can feel confident about your decision. 

Trust us; you won’t want to miss this!

Understanding The Career Path of a Data Scientist and a Machine Learning Engineer

Data science stems from the field of analytics and focuses on making sense of large amounts of data.

A data scientist analyzes data and finds patterns and insights to help a company make better decisions.

Data scientists typically use statistical methods, data visualization tools, and programming languages like Python and R to complete the job. 

While coding is a part of the job, it’s usually less prominent than data analytics work.

On the other hand, machine learning stems from the field of software engineering.

While machine learning engineers and data scientists both build these algorithms, machine learning engineers will be coding much more than data scientists. 

Machine learning engineers focus on implementing these algorithms and building systems that allow these algorithms to flourish.

While analytics is still a part of the job, due to the software engineering branch, machine learning engineers spend much less time analyzing data.

Are Data Science and Machine Learning The Same Thing?

While data science and machine learning might seem similar, they are actually two distinct fields.

Both fields revolve around building models and making sense of data, but the focus and approach differ.

Data science is closer to the optimization branch of mathematics, where the goal is to make slight improvements to already-built systems.

Data scientists use statistical methods and visualization tools to analyze data and find insights to help companies make better decisions.

They might also build predictive models, but the focus is on finding the best solution within the constraints of the existing system.

On the other hand, machine learning is a software engineering job focused on building the systems themselves.

Machine learning engineers use programming languages like Python and R to write code to build algorithms and systems that can foster these algorithms. 

The goal is to build models that can be used for various tasks, such as image recognition and natural language processing.

While data science and machine learning might seem similar, they are very different regarding day-to-day work.

Building a system and monitoring a system are two very different things.

As a data scientist, you will spend more time analyzing data and finding insights into pre-built systems. 

As a machine learning engineer, you’ll spend more time writing code and building these systems.

How To Pick Between Learning Data Science or Machine Learning First

When it comes to choosing between learning data science and machine learning first, the answer is pretty simple.

The most critical factor in choosing is figuring out what you enjoy doing. 

If you enjoy analyzing data and finding patterns, then data science might be your “perfect” choice. 

Also, those with strong statistical and mathematical backgrounds quickly learn data science.

While working as a senior data scientist, most of my team came from academics with Ph. Ds in physics, astronomy, and computer science.

This makes sense as you’ll use statistical methods to analyze data and find insights to help companies make better decisions – things learned in masters and Ph.D. programs.

The transition into a career as a data scientist will be much more fluid, as you already know you enjoy this type of thing, making the end goal much easier to achieve. 

If you have a passion for building things and have a system-oriented mindset, then pursuing machine learning first might be the right choice.

This is an excellent path for those from a software engineering-type role who has been writing code and feel confident in their coding abilities.

Machine learning engineers build algorithms that allow computers to learn from data and the skills you’ve previously learned while coding will directly apply to your work.

You’ll use programming languages like Python and R to write code and build models that can be used for various tasks, such as image recognition and natural language processing.

What Would I Do If I Have No Experience In Either?

If you have no experience in either data science or machine learning, it might be a good idea to start by targeting a career in data science.

This approach has been successful for many people who have transitioned into the field.

By teaching yourself to code and securing a data science role, you’ll gain valuable experience and build a foundation that you can use to transition into a machine learning role later on.

We suggest starting with data science first because you can get a job in about half the time it takes to get a machine learning engineer role. 

While it might take 18 months or more to gain the necessary experience and skills to get a machine learning engineer role, you can get to work as a data scientist in as little as nine months. 

This allows you to start your career and earn money sooner while you continue to build your skills and gain experience.

Once you have gained confidence in your coding abilities and built a strong data science foundation, you can leverage that experience to transition into a machine learning engineer role.

By starting with data science, you’ll gain a deeper understanding of the field and be better equipped to make the transition later on.

Should I Just Learn Both?

While it may seem like a good idea to learn data science and machine learning, it’s better to focus on one area and become an expert in it.

Careers are better with expertise, and by focusing on one area, you can develop a deep understanding of the field and become an expert in it.

You may have to learn about both of them initially to figure out which one you enjoy more, but once you’ve decided, diving deep and focusing on one area is essential. 

By doing so, you’ll develop a deeper understanding of the field and be better equipped to make a real impact.

And honestly, people pay more $$$ for expertise and experience.

High accuracy is achievable in some situations, while a seemingly modest score could be outstanding in others.

Many times, good accuracy is defined by the end goal of the machine learning algorithm. Is the algorithm good enough to achieve its initial goal?

If so, chasing higher accuracy may not even benefit you or your clients compared to chasing other things like ethical bias and improving infrastructure.

A Deeper Relationship With Accuracy Scoring

For instance, in the world of quantitative trading, or being a quant, a 51% accuracy rate over some extended period of time would lead to significant profits for you and your clients. 

This is because even a slight edge in predicting stock movements can translate into substantial gains over time. With enough capital behind you, you’d be the richest guy on Wall Street!

While chasing a higher accuracy score would obviously be beneficial here, even with a modest 51% accuracy, working on latency and infrastructure of your trading platform may end up being more fruitful and something that should be taken into account before spending money on trying to achieve a higher scoring metric.

While sometimes, as machine learning engineers, we quickly fall in love with the first score we see pop out from our algorithm, On your path to a good accuracy score, you should ensure that your modeling techniques are appropriate, logical, and well-tuned. 

Simply testing a few different approaches may not be enough to maximize the potential accuracy of your current business situation. 

This is why it’s important to thoroughly explore various techniques and fine-tune your model based on the specifics of your problem.

For example, if you’re using something like a gradient-boosted tree, hyperparameter tuning has proven time and time again to be beneficial to achieving a more accurate model.

Even after doing all of these things, it’s still sometimes hard to know if your model is any good and if you can be happy with your model’s performance.

Something that I do when working with a new machine learning algorithm and dataset is consult academic research and papers for relevant scoring metrics and benchmark scores.

This is highly beneficial and something that I’m constantly doing in my day-to-day work, since you will quickly know if your model’s performance is any good.

This will provide you with a baseline to gauge your model’s performance and help you identify areas for improvement. 

Additionally, it is essential to consider other performance metrics, such as precision, recall, F1-score, and area under the curve (AUC), as accuracy alone may not provide a comprehensive understanding of your model’s performance.

There is no one-size-fits-all answer to what constitutes a good accuracy score in machine learning. The appropriate score depends on the problem, dataset, and context.

By thoroughly researching and fine-tuning your modeling techniques and considering other performance metrics, you can work towards achieving the best possible outcome for your specific use case.

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• Can Machine Learning Models Give An Accuracy Of 100
• High Accuracy Low Precision In Machine Learning
• Data Science Accuracy vs. Precision
• Machine Learning: High Training Accuracy And Low Test Accuracy
• Machine Learning: Validation Accuracy
• How can Data Science Improve The Accuracy Of A Simulation?

It involves using mathematical and statistical methods to extract insights and knowledge from (you guessed it) data. 

A couple of key concepts in data science are accuracy and precision, and understanding the difference between these two metrics is crucial for achieving successful results during your modeling. 

In general, if one class is more important than the others (like sick compared to healthy), precision and recall become more relevant metrics, as they’ll focus on the actionable events in the dataset. However, if all classes are equally important (classifying which type of car), accuracy is a good metric to focus on. 

This article will dive deeper into exploring the meaning of accuracy and precision in data science and review scenarios where you should prioritize accuracy and others where you should prioritize precision. 

While we understand that these topics can be overwhelming initially, you’ll be fully equipped with two new metrics in your toolbox to help YOU be a better data scientist.

You won’t want to miss this one!

Why Do Data Scientists Need Accuracy And Precision?

As data scientists, our primary goal is to build machine learning models that can predict outcomes, with some level of certainty, based on past data. 

These models can be used for various tasks, such as classification, regression, and clustering.

To determine the success of our models, we need to evaluate them using various metrics.

And as you guessed, many different metrics are used to evaluate machine learning models, and two of the most well-known accuracy measures for classification problems are accuracy and precision.

Accuracy measures how well our model can correctly predict the class labels of our data set. This means it doesn’t care what it’s predicting, as long as it’s predicting it right (All data points are equal here). 

Mathematically, It is calculated by dividing the number of correct predictions by the total number of predictions made.

On the other hand, precision measures the number of accurate positive predictions made by the model out of all positive predictions. It simply answers the question, what proportion of positive classifications are actually correct?

A model that can achieve a precision score of 1.0 produces no false positives, and a model that achieves a score of 0 has all false positives.

While they seem similar, accuracy and precision are essential metrics for data scientists to consider, as they help us answer different questions about the performance of our models.

For example, accuracy can give us an overall idea of how well our model performs, while precision can help us identify how our algorithm is doing on the “relevant” data. 

While many believe a balance between accuracy and precision should be chased, this is only sometimes correct.

In the next section, we’ll review why sometimes these metrics can be misleading and how you can sometimes look at each of these individually to find the story that answers your business question.

Reference:

https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall

Which Is Better, Accuracy or Precision?

When it comes to evaluating the performance of a machine learning model, the question of which metric is better, accuracy or precision, is asked all the time. 

The answer, however, is more complex.

Neither metric is always better, as the relevance of each will depend on the specific business problem you are trying to solve.

For example, if you’re classifying things into four different categories with equal importance, accuracy might be a better metric for you.

This is because accuracy will give you an overall sense of how well you’re doing with your data and with classifying these objects into their respective categories. 

In this scenario, a high accuracy score would indicate that your model correctly categorizes most of your data.

On the other hand, consider a scenario where you’re predicting medical disease from health data. 

In this case, making too many positive claims when they’re untrue would be disastrous, as telling someone they have a disease when they do not is a dangerous and expensive event. Here, you would want to make sure your precision is in check. 

In a situation like this, precision is more important than accuracy because it’s crucial to minimize false positive predictions.

It’s interesting to note that in some cases, even with very poor precision, the accuracy of a model can still be very high. 

This is often because the amount of important events in the dataset is usually tiny. 

Think about it this way, in the example above, your dataset would have a few positive medical diagnoses “1s” and many healthy individuals “0s”.

If your dataset was 95% “0s” and 5% “1s”, and your algorithm just predicted “0s” the whole time, it would achieve a 95% accuracy. However, this algorithm is not only useless – but dangerous to patients, as we would not be diagnosing the disease.

Be careful with blindly trusting any metric, as choosing the right one can actually be dangerous.

How to know when to use Accuracy, Precision, or Both?

Knowing when to use accuracy, precision, or both is an essential consideration for data scientists.

First, to get this out of the way, Precision and Recall are only used for classification algorithms, while accuracy can be used both for regression and classification.

As a quick review, recall measures the number of true positive predictions made by the model out of all actual positive instances. It is used in conjunction with precision to provide a complete picture of a model’s performance. (We’ll go over this more in another article).

In general, if one class is more important than the others, precision and recall become more relevant metrics, as they’ll focus on the actionable events in the dataset.

However, if all classes are equally important, accuracy is a good metric to focus on. 

This is because accuracy provides an overall sense of the model’s performance, regardless of class label distribution.

In scenarios that fall somewhere between these two extremes, accuracy and precision should both be used to get a complete picture of the business scenario. 

By considering both metrics, data scientists can build effective and reliable models while also considering the unique requirements of each business problem.

In Data Science, Can You Have High Accuracy But Low Precision?

It is possible to have a high accuracy score but low precision in data science.

This scenario is quite common, especially when working with unbalanced datasets that have a low number of important events (1s vs. 0s)

In such cases, focusing solely on accuracy can be dangerous and lead to misleading results.

This is because a high accuracy score can give a false impression that the model is performing well when it may be missing many important events.

If you find yourself in this scenario, it’s important to stop focusing on accuracy and instead focus on precision and recall.

By doing so, you can build a more relevant model to the goal you’re trying to achieve.

Precision and recall will give you a complete picture of the model’s performance (in this scenario) and help you identify areas for improvement.

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• Can Machine Learning Models Give An Accuracy Of 100
• How Can Data Science Improve The Accuracy Of A Simulation?
• Machine Learning Validation Accuracy
• High Accuracy Low Precision In Machine Learning
• What Is a Good Accuracy Score In Machine Learning?
• Machine Learning: High Training Accuracy And Low Test Accuracy

Don’t worry; we’re here to help.

In this article, we’ll be breaking down the process of selecting the perfect algorithm for your project in a simple but effective easy-to-understand way.

We’ll start by taking a high-level look at the world of machine learning algorithms and what to consider before you even touch that keyboard. 

Then, we’ll review critical considerations and KPIs to help you know you’ve made the right choice.

By the end of this article, you’ll have a solid understanding of what to look for when choosing a machine learning algorithm and feel confident in your ability to make the best choice for your project.

If you want a future in this field, this is a MUST-READ.

The Two Main Pillars of Machine Learning

Regarding machine learning, there are two main pillars:

Unsupervised learning and Supervised learning. Understanding these two distinct pillars is critical in choosing the right algorithm for your project.

Unsupervised learning is a type of machine learning where the algorithm is trained on a dataset without any specific target variable.

The algorithm must then find patterns and relationships within the data on its own.

This approach is used when you don’t have a target variable or are interested in clusters and groups within your data that aren’t extremely obvious.

For example, an unsupervised approach is excellent when looking for marketing groups and segments within a customer base to increase sales.

Conversely, supervised learning is a type of machine learning where the algorithm is trained on a labeled dataset with a particular target variable. 

This means the algorithm knows what it’s trying to both predict and improve on, allowing our algorithm a path to convergence.

Supervised learning is often preferred over unsupervised learning simply due to the information gain.

Let’s run through an example.

Say you have four columns of data and a “target variable.” Since our unsupervised algorithm does not use this target variable, it will take advantage of the four columns.

On the inverse, our supervised algorithm will have four columns of data plus the target variable. 

This means our supervised algorithm will have nearly 25% more data to work with!

It’s important to note that your dataset and problem usually dictate which machine learning pillar you should use. 

Remember, it’s best to utilize supervised algorithms whenever possible, as they provide more information and can help you achieve better results.

In summary, the two main pillars of machine learning are unsupervised and supervised learning.

While unsupervised learning helps uncover hidden patterns in data, supervised learning is preferred because it can converge on a target variable and provide the underlying algorithms with more information.

One Pillar Has Two Categories; The Other Has None

Under the umbrella of supervised learning, there are two main categories: regression and classification.

Regression is a type of supervised learning where the target variable is continuous, meaning it can take on any value within a range (Note, that range can be 0 to infinity)

The algorithm is trained to predict the target variable’s value based on the input variables’ values.

For example, using historical data on housing prices and their respective features, a regression algorithm can predict the price of a future house based on its features.

On the other hand, classification is a type of supervised learning where the target variable is categorical, meaning it can only take on a limited number of values or categories. 

The algorithm is trained to predict the target variable’s category based on the input variables’ values. 

For example, one of the most classical machine learning problems is when using data on flower species and their respective features; a classification algorithm can predict the species of a flower based on its features.

It’s worth noting that these two categories only exist in supervised learning, as we have a target variable to learn from and optimize for.

This allows us to predict future values or groups based on the information we’ve learned from the target variable.

In unsupervised learning, we don’t have a target variable to tell us if we’re doing a good job with our predictions.

Our algorithms have nothing to optimize for; they only find patterns and relationships within the data.

This means unsupervised learning differs from supervised learning, requiring an almost different philosophical approach to choosing an algorithm.

What To Do Before You Start Coding Your Algorithm

Before you start coding your machine learning algorithm, sit down and ensure you understand your business problem and are being realistic with your data.

This will help you choose the correct algorithm for your project and ensure you get the best possible results.

When it comes to understanding your business problem, it’s essential to determine whether you’re trying to optimize toward a target (supervised learning) or looking for a new way to look at your data (unsupervised learning). 

For example, if you’re trying to predict future sales or which group a new member would belong to, you’ll need a target variable, and supervised learning would be the best approach.

On the other hand, unsupervised learning would be the better option if you’re looking to build up groups and clusters without guiding the algorithm.

Be realistic with your data. 

Supervised algorithms are immediately not an option if you don’t have a target variable. 

In this case, unsupervised learning is the only option available.

In summary, before you start coding your machine learning algorithm, understand your business problem and be realistic with your data.

Use your data as a guiding light, and make sure you choose the right approach based on your specific needs and the information available.

Quick Guide To Choosing The Right Machine Learning Algorithm

Here’s a quick mental map that I use to choose the right algorithm.

Understand your business problem: What are you trying to solve?

Understanding your business problem is the first step in choosing the right algorithm.

Before exploring different algorithms, you need to understand what you’re trying to achieve.

Explore your data: What columns and data do you have that’s usable?

You need to have a good understanding of the data you have available to you.

This will help you choose an algorithm that is well-suited to your specific needs and can take advantage of the data you have.

Determine if it’s a supervised or unsupervised problem: Once you have explored your data, you need to figure out if you’re dealing with a supervised or unsupervised problem.

This will help you narrow your options and choose the right approach for your problem.

Determine if it’s regression or classification: If it’s a supervised problem, you need to figure out if it’s regression or classification.

Are you predicting a continuous value or putting things into predetermined categories?

Find a group of algorithms to test: Use what you now know about your problem to find a group of algorithms within your group (such as supervised regression or unsupervised NLP problems).

This will help you narrow your options and find the right algorithm for your needs.

Note: As you’ve noticed, we say to find the group independently, as we have yet to recommend any specific data science algorithms. 

Finding the right machine-learning model is an iterative process.

Anyone suggesting “regression trees are best when doing X” does not understand machine learning and how algorithms work.

Assess each algorithm in the group: Test each algorithm in the group and assess its performance.

This will help you determine which algorithm performed the best and is the best choice for your specific problem.

Select the machine learning algorithm: Based on your results, select the machine learning algorithm that best suits your business problem.

This will be the algorithm you use to solve your problem and achieve your goals.

What To Watch Out For When Choosing Your Algorithm

When choosing a machine learning algorithm, there are several things to remember when picking out that perfect algorithm.

First, don’t fall in love with an approach before it’s tested. 

Even if a particular algorithm looks good on paper or has worked well for others, it may not work the same for you.

It’s important to test multiple algorithms and compare their results to find the best one for your business needs.

Second, remember that your data and problem choose the algorithm, not you. 

You may have a favorite algorithm you’re excited to use, but it’s not the right choice if it doesn’t fit your data and problem well. 

Make sure to choose an algorithm that is well-suited to accomplish your goals!

Third, be aware that all algorithms seem good before they’re tested. 

Only after testing will you know how well an algorithm will perform on your problem. 

Don’t be swayed by an algorithm’s hype or popularity- test it and compare its results to other algorithms.

Fourth, don’t assume that a higher accuracy means a better algorithm. 

While accuracy is important, it’s not the only factor to consider.

Other factors such as speed, interpretability, and scalability also play a role in determining the best algorithm for your needs.

Fifth, ensure your data source is “tapped,” meaning you can’t get any more data. 

If you can obtain additional data, you can improve the performance of your algorithm or choose an altogether different algorithm that could perform much better (remember our unsupervised vs. supervised talk above).

Finally, remember that sometimes the best answer is the most straightforward answer. 

Don’t get caught up in using complex algorithms just to use a complex algorithm.

The simplest solution is often the best, especially if it provides the desired results with a lower risk of overfitting or over-complication.

How To Know You’ve Picked you’ve chosen the right learning model for your problem.

Ultimately, the best way to know if you’ve picked the right machine learning algorithm for your problem is if you’ve successfully solved the problem you initially set out to solve.

If your algorithm provides the desired results and you can achieve your goals, you’ve likely made the right choice.

On the other hand, if your algorithm is not providing the results you need, it’s time to go back and reassess.

It’s important to remember that machine learning algorithms are not one-size-fits-all solutions.

What works well for one problem may not work well for another.

This is why it’s important to test multiple algorithms and choose the best fit for your needs.

On the one hand, technology has made our lives easier and more convenient by opening us up to new technologies like machine learning and data science. 

On the other hand, it has also made us more vulnerable to things like hacking and cyber attacks. 

That’s why cyber security and data science are two fields that are becoming more and more influential to businesses every single day. 

In this exciting blog post, we’ll dive into the world of cyber security and data science and discover the limitless possibilities they hold for the future while also covering how the scope of both data science and cyber security will change over the next ten years.

Get ready to be blown away by these fields’ potential impact on our world! 

So, buckle up and prepare for an eye-opening ride through the exciting world of technology. 

And at the bottom, I’ll give you a tip to help you understand why the scope is changing.

Understanding Scope In Tech

The world of technology and the scope of its roles are constantly changing and evolving, and the fields of data science and cyber security are no exception.

In recent years, we have seen a shift in the overall scope of these two fields, with data science becoming more specialized and cyber security expanding to meet the growing demands of our increasingly security-hazarded world.

If you ask most CEOs what their top concern is, they will usually tell you that security is their number one priority. And trust me, you’ll never hear “optimization” as their primary focus (sorry, data scientists).

In today’s digital age, data breaches and cyber attacks are becoming more frequent and sophisticated.

Companies know that they must take steps to protect their sensitive information, intellectual property, and assets. 

This is where the field of cyber security comes in, with experts working to identify and prevent cyber threats, ensuring the security of digital systems and data.

On the other hand, the scope of data science has become more focused (shrinking), with data scientists specializing in specific areas such as analytics, machine learning, and data visualization. 

Data science aims to extract insights and knowledge from data, but as the field becomes more specialized, the scope of what can be achieved with a single data scientist is becoming more limited.

Cyber Security vs. Data Science: Scope Creep

Cyber security and data science are two fields that are evolving at a rapid pace.

In recent years, we have seen a shift in the scope of these two fields, with cybersecurity expanding to encompass more areas and data science becoming more specialized.

Cyber security has seen a growing scope as the threat of cyber-attacks and data breaches continues to increase.

Companies and individuals are becoming more aware of the importance of protecting their digital systems and data, and cyber security experts are rising to meet this demand.

As a result, the scope of cyber security is expanding to encompass a broader range of areas, including network security, cloud security, and mobile security.

On the other hand, the scope of data science is slowly shrinking.

In the past, data scientists were often viewed as a “swiss army knife,” capable of handling a wide range of tasks due to their mathematical prowess.

However, with the rise of autoML and cloud platforms, many of the routine tasks of data science are being automated (think about things like AWS SageMaker), leading companies to focus on hiring data scientists with a highly specific scope. 

This has decreased the overall number of data scientists as the field has become less broad and specialized.

Cyber Security vs. Data Science: Job Outlook

The job outlook for cyber security and data science fields is constantly changing, and it’s crucial to stay up-to-date on the latest trends in the job market. 

Recently, we have seen a significant increase in demand for cybersecurity professionals.

Many companies seek individuals with the skills and experience to protect their digital systems and data.

Cybersecurity is experiencing a job market boom, much like data science did ten years ago when Harvard called it the sexiest job of the 21st century (I linked the reference at the bottom).

With the growing threat of cyber-attacks and data breaches, companies are willing to pay top dollar for experts in the field.

As a result, many individuals are picking up certifications such as CompTIA and securing highly lucrative jobs in cyber security, even without any college degrees.

On the other hand, the scope of data science is becoming more specialized, leading to an increase in the level of education and experience required to secure a job in the field.

Data scientists are now required to deeply understand specific areas, such as machine learning or data visualization, making it more challenging to break into the field without the right skills, education, and experience.

I’ll give it to you straight:

If you’re starting in the tech industry, you might want to consider pursuing a career in cyber security. 

With its growing scope and size, cyber security is an exciting and innovative field that is poised for EXPLOSIVE growth in the coming years.

However, as with any career decision, it’s important to consider your personal interests and skills before making a choice.

(Choose cyber security, I’m saying this, and I even work in ML & DS).

Reference:

https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century 

Will data science exist in 10 years?

The field of data science is constantly evolving, and it’s difficult to predict what the future will hold for this dynamic and growing field.

However, some experts believe that data science may undergo a transformation similar to what happened to manufacturing during the industrial revolution.

During the industrial revolution, many tasks once performed by a single person (like building a whole car) became automated, split up, and specialized. 

For example, one person might focus on building the wheels of a car, while another might focus on the hood. 

Ultimately, all of these pieces would come together to build a car.

Similarly, data science may become more specialized in the coming years, with individuals focusing on specific subsets of the field, such as data visualization or regression analysis.

This shift towards specialization would eliminate the role of the data scientist, as the umbrella term would no longer cover what you’re actually doing.

We would see experts in things like data sourcing, regression, classification, etc.

This form of optimization of the field would decrease the total number of people within the field; as any good data scientist knows, optimization usually leads to cheaper outcomes for the business.

However, as data scientists and machine learning engineers, we always strive for greater accuracy and better performance from our models. 

But what happens when we see an accuracy of 100%? Is it truly a sign of a perfect model, or should it raise red flags about potential problems?

In this blog post, we will explore the concept of accuracy in machine learning and the factors that influence it.

We will also discuss why a 100% accuracy rate may not always be what it seems and what to look out for when evaluating the performance of your models. 

This post aims to provide a deeper understanding of the limitations and challenges of machine learning and to help you make informed decisions about your models.

Why Even Evaluate Model Performance with Metrics?

As machine learning models become increasingly important in a wide range of applications, it is crucial to have a way to evaluate their performance.

But why is it necessary to evaluate the performance of a machine-learning model in the first place? 

Simply because what else would be the alternative?

Without a metric, it would be difficult to determine whether the model is making accurate predictions or needs improvement.

For example, if a model is used for medical diagnosis, it is crucial to know whether it accurately identifies whatever disease you’re chasing after. 

Patients may receive incorrect diagnoses and treatments if the model performs poorly, which could have serious consequences.

Also, if someone asked you, would you rather have your medical diagnosis be performed by a model that scores 30% or achieves 80%?

Accuracy is one of the most commonly used metrics for evaluating the performance of machine learning models.

It measures the proportion of correct predictions made by the model compared to the total number of predictions.

For example, if our machine learning model gets 3 out of 10 correct, we can confidently say that our model has 30% accuracy.

Accuracy is not the only metric to evaluate machine learning models’ performance. 

Many other metrics, such as precision, recall, F1 score, AUC, and ROC, provide different perspectives on the model’s performance.

Why is 100% Accuracy (Or Any Other Metric) Concerning?

Pursuing high accuracy and KPIs is a common goal in machine learning, but achieving 100% accuracy, or any other metric, as we’ve stated earlier, can be concerning for several reasons. 

Let’s take a closer look at some of the reasons why 100% accuracy, or any other metric, can be a cause for alarm.

Insufficient data

One of the most common reasons for 100% accuracy is insufficient data to evaluate the algorithm accurately.

If you only test the algorithm on anything less than 50 samples, you could have “easy” data.

What we mean by this is that as datasets grow, many nuances in the data are captured. The unique situations or hard-to-guess situations within your dataset are represented.

If you have low amounts of data, these situations are never captured, and your algorithm is never tested against them.

While this does not necessarily mean that the model will NOT perform well on unseen data, if you were to double or triple the amount of data you have, you would quickly see your accuracy plummet during testing.

It is vital to have a large enough dataset to evaluate the model’s performance accurately.

Training set accuracy

Another reason 100% accuracy can be concerning, and something that we see all the time with our fellow machine learning engineers, is that the accuracy is being measured on the training rather than the testing set.

The training set is used to train the model, while the testing set is used to evaluate the model’s performance on new, unseen data.

If the model achieves 100% accuracy on the training set but has poor accuracy on the testing set, it may be overfitting the training data.

We do not like overfitting here at EML and always take the model’s accuracy from the out-of-sample results.

Coding error

Simply, 100% accuracy can also indicate a coding error. 

For example, the model may be predicting the same class for all examples, or it may be making predictions based on the order of the data rather than the actual features.

You could simply be miscalculating accuracy or comparing predicted results to predicted results.

Another thing that could have gone wrong is you could have “leaked.” You’ll want to venture into the dense topic of data leakage, where you’ve allowed your algorithm to cheat during training and testing.

It is important to carefully check the code and ensure the model makes predictions based on the correct factors.

God-like model

Finally, and something I’ve never personally done, maybe you just “got it right.”

You may have created a god-like model that can perform so well that not even the testing set can beat it.

While I highly doubt this is the case, maybe you’ve pulled off a miracle and created an unbeatable machine-learning model.

(I doubt it, though)

What Does Good Accuracy Look Like During Machine Learning Modeling?

When it comes to evaluating the performance of a machine learning model, it can be challenging to determine what constitutes good accuracy.

The answer is not always straightforward, as it depends on the specific task, the industry, and the data.

However, there are a few things to remember when evaluating your models’ accuracy.

Accuracy is an iterative process:

The accuracy of a machine learning model can change as the model is improved and fine-tuned.

For example, a model that starts with an accuracy of 50% may increase to 60% after the first round of improvements and continue to improve with each iteration.

The goal is to achieve the highest accuracy possible for your specific problem, not the highest accuracy possible.

Different industries have different standards:

The standards for good accuracy can vary widely depending on the industry.

For example, in some industries, a model with 60% accuracy may be considered highly accurate, while in others, anything below 90% may be regarded as unacceptable.

It is important to check the academic literature and the results of others in your industry to understand what constitutes good accuracy in your field.

Clients may have different standards:

Finally, it’s important to consider the needs and expectations of your clients.

Some clients may be happy with an accuracy of 60%, while others may have something in the 90s in their heads for the specific problem. 

It is essential to understand your client’s specific needs and requirements and strive to meet or exceed those standards.

Other Articles In Our Accuracy Series:

Accuracy is used EVERYWHERE, which is fine because we wrote these articles below to help you understand it

• High Accuracy Low Precision Machine Learning
• Data Science Accuracy vs. Precision
• Can Machine Learning Models Give An Accuracy Of 100
• What Is a Good Accuracy Score In Machine Learning?
• Machine Learning Validation Accuracy
• How Can Data Science Improve The Accuracy Of A Simulation?

The key difference between programming and machine learning is that programming relies on instructions from a programmer to perform tasks, while machine learning uses algorithms to allow the machine to identify patterns within data. Your machine will use these patterns to decide how to best proceed. 

While that’s just the high-level difference, this article will dive deep into different scenarios, more examples, and some things that many need to consider when they think about these two.

Not a read you can skip!

What Is Machine Learning?

With how widespread machine learning has become, it’s almost easier to answer what machine learning is NOT.

It is the foundation behind some of the most innovative software currently in development, and many predict that machine learning will become an integral part of human life. 

At its core, machine learning is the ability of computers to learn from experiences (data) without being explicitly told what to learn. 

This means that instead of a computer being programmed to complete a task, it can learn by itself through observation, repetition, and patterns that we have no chance of recognizing. 

Understanding machine learning and its benefits make it easy to see why it has become so popular in recent years. 

As data sets grow, machines can independently identify patterns and draw conclusions about them without any external input.

Is machine learning the same as coding?

Whether machine learning is the same as coding has been constantly debated in recent years (all over the internet) – and it’s an important one to consider.

If we think of coding as “writing code,” these two are basically the same thing – machine learning engineers write code just like everyone else in the tech realm.

However, when someone refers to “coding,” they’re usually talking about software engineering.

While the reasoning behind this terminology is a little blurry, software engineering is generally assumed to be the role when someone refers to “writing code.”

If we use that ideology, these two are closely related yet have fundamentally different approaches; coding, traditionally seen as a product of software engineering, requires human instructions to make decisions, while machine learning employs algorithms that teach the computer to adapt independently. 

Differences between machine learning and traditional programming

As we’ve expressed earlier, traditional programming and machine learning have some differences when it comes to the tech realm.

While traditional programming requires creativity, foresight, abstraction, and understanding of interlinking systems, machine learning is based on optimizing existing data to reach a particular metric.

Traditional programming allows developers to create something unique and novel, while machine learning relies on already existing data sets (Leave GANs out of this).

While traditional programming may be more suitable for entering new markets or creating something never seen before, machine learning excels at making small changes that can have larger impacts in areas such as efficiency or cost-effectiveness.

Machine learning is great for optimizing an existing system, but it often arrives at outcomes that even machine learning engineers need help to backtrack.

In contrast, understanding software written by a programmer on your team is an essential skill in software engineering and a core skill in traditional programming.

So, to break it down in simple terms, the main differences between traditional programming and machine learning are:

• Traditional programming is more creative
• Machine learning is based on optimization toward a metric
• Machine learning needs historical data
• Traditional programming can create things that don’t exist
• Software engineering is about understanding the software
• Machine learning has outcomes that sometimes aren’t understood

How Much Programming Knowledge Is Required for Machine Learning and data science?

Data science and machine learning have revolutionized the world of technology, changing how we understand and interact with data.

While these new technologies bring incredible insights, they still require knowledge of programming languages to be effective. 

Data scientists and machine learning engineers must be familiar with coding techniques to design reliable models and support them in production environments.

While some tools, such as AutoML or Github CoPilot, are helping to simplify the process of building models, most of the coding involved is for setting up the Machine Learning (ML) infrastructure/operations (MLOPS).

MLOPS is continuous machine learning – where machine learning engineers and data scientists build pipelines and infrastructure to allow their models to maintain high accuracy and consistency in production environments.

So, while you can use AutoML or Github CoPilot to build a decent model quickly, you’ll need to really dive deep into programming to create a system that can support your models.

Why Is Machine Learning So Popular Now?

Machine Learning has been gaining popularity in recent years and is now a highly sought-after field, with professionals earning very high salaries. 

This is because it is a relatively new field, where we’ve unchained our computers and allowed them to make decisions for us.

However, the ideas behind machine learning have been around for a long time.

Just think back to the terminator movies – the idea of robots making decisions for us isn’t new.

What is new and what really has unlocked machine learning is that computational limits are constantly increasing – allowing more complex tasks and models to be created by our machines.

And, to top it off, while computational is being pushed to the limits – data is everywhere.

Every company worldwide has started to collect and harvest its own data – no matter the industry.

And where there’s data, there’s machine learning.

Should I Learn Machine Learning Or Software Development?

Choosing between software development and machine learning can be complicated.

Both fields involve coding and building but have different goals, processes, and approaches.

Machine learning is much more mathematically dense with its focus on algorithms, data analysis, and data storage, whereas software development is more creative in nature.

As a general guide:

If I’m more interested in solving problems and more statistically focused, I would choose machine learning. If I am creative and love to build things from scratch and see them develop, I would choose software development. 

Ultimately, it all comes down to what interests you most – I’d do a couple of projects first and figure out what aspects of that project I enjoyed – was it finding a solution or building something?

Will machine learning replace programmers?

Machine learning may replace programmers, but the ones it’ll replace aren’t the jobs you want. Historically, when automation has been introduced, it’s always targeted the repetitive tasks of that sector.

With programming, these will be the dull, minimally complex tasks that programmers didn’t want to do anyways.

This means that instead of a team of 10, you’ll have 8.

I believe in the future, all software and machine learning engineers will use some form of AI to write their code.

The days of writing software/programs/models from scratch and digging through StackOverflow to get your code to work are slowly dwindling away.

So no, machine learning will not replace programmers; it will replace the low-hanging fruit programming that programmers don’t enjoy doing anyways.

Is Machine Learning Harder Than Software Engineering?

Machine learning is a lot of the time easier than traditional software engineering. This is because the model does a lot of the work for you. Since problem-solving with machine learning mainly focuses on setting up an environment for your model to thrive, we don’t need to absorb every aspect of possibility – as our model can do it for us.

Contrasting this with software development, where every piece of the software has to be tested and re-tested, software engineers are left in a place where they have to understand the software and code they are writing at a deep level.

Do Traditional Programmers Make More Money Than Machine Learning Engineers?

As we know from above, many employers are turning to artificial intelligence and automation in today’s tech-driven economy to power their businesses.

And from this – the demand for skilled machine learning engineers is exploding. 

But do those engineers really make more money than traditional computer programmers? 

The answer is yes, by a lot.

According to glassdoor, the median salary of machine learning engineers in 2022 was $131,000 per year, whereas software developers earned an average of $90,000 per year.

With an incredible pay rate and growing job opportunities, machine learning engineering is becoming an increasingly attractive career path for people who want high-paying work and fun-to-solve problems. 

At first glance, it might appear that there’s no contest here: clearly, the higher salaries offered by machine learning engineers make them a more lucrative option for aspiring techies than traditional programming jobs.

But there are still merits to both careers; 

While both require strong coding aptitude and technical skillsets, each involves different approaches to problem-solving that can build different employment opportunities depending on which field you choose.

Sources:

https://www.glassdoor.com/Salaries/machine-learning-engineer-salary-SRCH_KO0,25.htm

https://www.glassdoor.com/Salaries/software-developer-salary-SRCH_KO0,18.htm

Which is the better career path, traditional programming or machine learning?

As a career path, if you had to choose between these two – you can’t choose wrong.

Both are incredible paths that will lead to rewarding and fulfilling job opportunities and a long-lasting career; however, there are some things you should consider before you jump in.

It’s usually a bit harder to get a machine learning job since machine learning usually requires a master’s degree in a STEM-related field.

This isn’t the same for software engineers.

Many software engineering jobs are available even while being self-taught or with a Bootcamp certification.

While it is easier to break in, we saw from above that software engineers make some pay sacrifices compared to their machine-learning friends.

Machine learning positions pay significantly higher salaries than software engineering jobs.

While there may be fewer machine learning positions than software engineering ones, both options can still provide a highly lucrative career – it just depends on your individual goals and interests.

Other This Or That Articles

We’ve written a couple of other articles that are very similar to this one; check out:

• • Machine Learning vs. Automation:
  • Full Stack Developer vs. Machine Learning Engineer
  • Data Science vs. Bioinformatics
  • Data Science vs. Economics
  • Machine Learning vs. Human Learning
  • Data Science vs. Operations Research

While being two of the top tech jobs, data science and full-stack development are two very different realms within the business ecosystem.

Data science is a relatively new name for the field that combines statistics, computer science, mathematics, and other computationally intensive disciplines to gain insight from data – both small and large.

Before being called data science, it was actually called operations research.

On the other hand, full-stack development is creating web applications (or any software) that involve all aspects of software engineering, including front-end design, back-end development, database management, and DevOps.

In this blog post, we’ll explore the differences between these two fields regarding job duties and the skillsets required for each.

We’ll also explore the ideas of salary, which is easier to get into, do these two ever “collide,” and much more.

Finally, we’ll discuss the pros and cons of each profession so you can decide which is best for you.

This isn’t one I’d skip.

What is Data Science?

Data science is a fascinating field that combines aspects of computer science, statistics, and mathematics to interpret any type of data.

Using analytical, business, and critical thinking skills, data scientists employ machine learning and other SOTA techniques to identify patterns in large datasets by analyzing data to make predictions about real-world events – usually to improve business KPIs.

For example, with a large enough dataset of consumer spending data, a data scientist could analyze and predict what products will be popular this season or whether interest rates on consumer loans should be dropped.

Data science can also be used for more broad and abstract tasks, such as understanding social trends, stock market fluctuations, and upcoming marketing strategies.

Ultimately, the goal of any data scientist is to take information from any part of the business realm, analyze it and then turn it into invaluable insights that can help shape positive business decisions.

What is a Full Stack Developer?

A full-stack developer is a highly skilled coder with both front-end and back-end development expertise.

If you don’t know the difference, front end development is code that a client/customer sees (think like a homepage), and backend development is the gears of the app, code that a client/customer does not see.

A full-stack developer typically works on the entire development process from start to finish, including planning, design, coding, testing, deployment, and maintenance.

Since they work on both the front-end and back-end of an application, they can work with various programming languages.

Usual Front-end stack:

• HTML5
• CSS3
• JavaScript

Usual Back-end stack:

• PHP
• SQL
• MongoDB
• Python (My Favorite!)

That’s not all they need to know – full stack developers know a ton of different frameworks, such as:

• Angular
• Vue.js
• React
• HTMX (Personal Favorite)
• JQuery

And finally, in rare-ish scenarios, some full-stack developers will make use of DevOps tools such as

• Docker
• Git (Everyone knows this)
• GitHub (Everyone knows this)
• Jenkins
• Kubernetes (Experts only!!)
• Chef

As a result, full-stack developers are in high demand due to their ability to handle all aspects of web application development.

Note: Many full-stack developers go by software engineers or software development engineers in some tech firms. They’re essentially the same thing, and the general scope of these jobs is the same. (Though software engineer sounds better, and probably pays better)

If I Didn’t Want To Do Full Stack, Could I Do Front-End or Back-End Web Dev?

Let’s say you didn’t want to pursue full-stack web development, but you fell in love with either frontend dev or backend dev.

You can definitely consider just specializing in one of the two.

I want to warn you that while this is a viable option (and many work in one or the other), I do not suggest this.

Let’s get the simple reasons out of the way:

1.) Full-stack web developers tend to be paid more than those who specialize in either one of the two areas of development alone, as there’s more role responsibility in full-stack.

2.) There are more full-stack roles available than there are for front-end and back-end developers separately.

Now for the complicated reasoning:

Essentially, and you’ll see this once you start working in these roles, full-stack roles are 90% back-end and 10% front-end development.

So, for more pay and job availability, instead of becoming a back-end engineer, you can pick up 10% front-end work (to make the app/software usable) and have more job options and a much more lucrative career.

The only time I’d say this option isn’t viable is if you really dislike backend development.

In that case, pick front-end development,

Here’s a simple chart.

I only like back-end dev -> choose full-stack

I like full-stack dev -> choose full-stack

I only like front-end dev -> choose front-end

Does Full Stack Include Data Science?

Full stack does not include data science; However, developers sometimes incorporate data science models into their applications. This mostly has to do with the scope and goals of the application that’s being built.

When this happens, this is usually set up as a microservice.

Most full-stack developers will not be present in the modeling and data exploration and will only be presented with final outputs (like a PyTorch Model)

Data science is an area of high expertise in its own right that requires a deep understanding of the subject matter and is a realm that software engineers don’t usually dip their toes into.

Although full-stack developers may be familiar with some aspects of data science, they are unlikely to have the same expertise or knowledge base as dedicated data scientists.

So no, full stack doesn’t require data science, but it may require you to work with some data scientists.

Is There Such Thing As A Full Stack Data Scientist?

Yes, there is such a thing as a full-stack data scientist. This role requires the ability to go from a dataset to a production-grade model and build a customer-ready web app around the production-grade model.

Taking on this role is not easy and usually requires tons of skill and expertise.

And therefore (as you probably guessed), it can be highly lucrative for those who are experienced in these areas.

You’ll often see this role listed as a “machine learning engineer,” though it can also be listed as other titles.

A full-stack data scientist will have expertise across many fields like coding, machine learning, analytics, system design, DevOps, and engineering, to name a few.

I’ve had a role like this in the past, and while fun; there was just too much to do. While this role taught me most of everything I know, it may have also cost me a couple of years of my life (Just kidding).

Do You Need To Know How To Code For Data Science or Full Stack Web Dev?

Knowing how to code is essential when it comes to data science or full-stack web development. There’s no way around this.

You’ll need to understand the fundamentals of coding, such as variables and functions, as well as more advanced concepts like object-oriented programming and databases.

It’s also important to be familiar with specific programming languages like Python or JavaScript, currently dominating these fields.

Sorry – coding is a must.

Which Is Faster To Learn, Data Science or Full Stack Development?

When it comes to your career, if you had to choose to learn only one of these two – you can’t choose wrong.

Both are incredible paths that will lead to rewarding and fulfilling job opportunities and a long-lasting career; however, there are some things you should consider before you pick and choose which to learn.

It’s usually a bit harder to get a data science job even if you “know it.”

Since employers want to ensure you understand complicated mathematics, gaining this trust is difficult and usually requires a master’s degree in a STEM-related field.

This isn’t the same for full-stack web development.

Many software developer jobs are available even while being self-taught or with a Bootcamp certification.

So I think both could be learned in about the same time frame, but learning full stack web development will pay off much sooner.

Who Writes More Code, Full Stack Developer or Data Scientist?

When it comes to writing code, a full stack software developer is the clear winner. A full stack developer’s job is basically entirely coding, and they will write way more code than a data scientist.

While a data scientist may have some coding involved in their work, it’s probably only around 30-50% of the time – spending the rest waiting for models to run (just kidding).

Remember, data scientists will use surveys, research, or anything else they can get their hands on to perform data analysis.

This leads to a much more “active” role than their counterparts.

With these two, it’s really not close – a data scientist will never write as much code as a software engineer, as data scientists need other tools for work, not just code.

Data Science vs. Full Stack Developer Salaries

As we know from above, many employers have too much data that needs exploring.

And from this – the demand for skilled data scientists is exploding.

But do these data scientists really make more money than full stack developers?

The answer is yes, by a lot.

According to glassdoor, a data scientist can expect to bring home around $125,000 a year, whereas full-stack developers earn an average of $86,000 annually.

With an incredible pay rate and growing job opportunities, data science is becoming an increasingly attractive career path for people who want high-paying work and fun-to-solve problems. 

At first glance, it might appear that there’s no contest here: clearly, the higher salaries offered by data science make them a more lucrative option for aspiring techies than full-stack developers…

But there are still merits to both careers; 

While both require strong coding aptitude and technical skillsets, each involves different approaches to problem-solving, and your career should be about more than just $$.

Sources:

https://www.glassdoor.com/Salaries/data-scientist-salary-SRCH_KO0,14.htm

https://www.glassdoor.com/Salaries/full-stack-developer-salary-SRCH_KO0,20.htm

Which is a better career, Data Science or Full Stack Web Dev?

Deciding which career would be best for you is an extremely difficult and personal decision.

When considering Data Science and Full Stack Web Development, it is important to remember that the best career is the one you will enjoy most.

While money and job security are both important considerations, it is better to be happy in the long run than paid well but disappointed.

It all comes down to what works best for you.

Consider your skill set and interests and what kind of atmosphere you would like to work in.

Research job descriptions and talk to people already working in those fields to get an idea of what career would be best for you.

This is a decision only you can make.

And we hope we’ve helped.

Other This Or That Articles

We’ve written a couple of other articles that are very similar to this one; check out:

• • Machine Learning vs. Automation:
  • Full Stack Developer vs. Machine Learning Engineer
  • Data Science vs. Bioinformatics
  • Data Science vs. Economics
  • Machine Learning vs. Human Learning
  • Data Science vs. Operations Research
  • Machine Learning vs. Programming

We’re not going to do that.

Regarding the best IDE for data science (especially learning data science), look no further: Spyder is the best IDE for data science.

While there are about ten more reasons Spyder should be your only choice – we’ll get into all of that below.

Looking for an IDE switch? or just getting into data science?

This article was made for you.

Why Spyder is the best IDE For Data Science

The majority of data science roles are closer to analyst roles than they are to programming roles.

From my experience as a data scientist, I spent way more time in analytical software like Excel than I did in things like Git and Docker.

And there’s a reason for that – I was building models, not software.

And you will be too.

This idea that you should have a set-up that a full-stack developer or a software engineer uses is just silly.

You will be getting paid to find optimizations and build models.

Why then wouldn’t you optimize your tech stack to benefit this goal?

Now that we got that out of the way (I’m looking at you VS Coders!!) let me tell you why Spyder is the best IDE for data scientists.

Well, first and foremost, it was explicitly designed for data scientists. This is why it’s offered in the Anaconda download package, the most popular data science platform.

When you start working as a data scientist, everything is datasets.

You’ll be trading datasets, modeling off new ones, and looking for old ones. Everything you touch will be influenced by some dataset.

And this makes sense; the title is literally “data scientist.”

And if you know anything about a data scientist’s actual work, you spend most of your time munging, fixing, editing, and correcting these datasets to prepare them for modeling.

You’ll often take a dataset from ingestion (you just received the dataset) to modeling, which will not resemble the original dataset in any way.

So, from going from point A to point Z, wouldn’t it be nice to have an IDE that saved values in variables?

Since you’re moving so much data from point A to point Z, having an IDE that saves the “state” of your variables is a lifesaver.

This sole reason (and many more, but this is the main one) is why Spyder is a home run for data scientists.

You can use its Variable Explorer to easily view and update variables, detect errors from their values, or create code plots with a few clicks.

Check out the image below, which saves every single variable we’re using and allows us to explore it later.

Now, imagine that you were coding with something like VS Code or PyCharm; you’d be in a scenario where if something goes wrong or you wanted to see how variables developed over time, you’d have to add print statements to see the output.

This gets insanely confusing, adds bloat to the code, and doesn’t help nearly as much as you think it would.

The second and “slightly smaller” reason Spyder is the best IDE for data science is because of the box it comes in.

There’s nothing worse than having to install a thousand different software programs on your computer to get things up and running.

With the anaconda download, you’ll be gifted everything that you need to get started with data science quickly.

And yes, you guessed it – Spyder sits right inside that download.

Once you’ve started coding and worked on a couple of projects, the idea behind creating virtual environments becomes incredibly important.

Basically, without diving too deep into it, you create baskets on your computer where you install specific modules. These modules will only exist in that basket and will allow you to run certain pieces of code once you’re inside the basket.

Now I bet you’re wondering where you create these baskets.

Well, you’re in for luck – because once you download the anaconda package, you’ll have the Conda environment on your laptop.

This allows you to create those baskets to install any modules and packages you want so that you can run any code you wish to.

What else could you want from one simple download?

So, to bring it all back home, Spyder is the best IDE for data science because it was made for data science. 

The anaconda download has everything you will ever need to be successful as a data scientist, from variable tracking to baskets to install modules into.

Why VS Code Is NOT The Best IDE For Data Science

VS Code may be a popular choice among software engineers, but it is far from the best IDE for data science.

Many of its features and plugins are wasted on data scientists since they are more tailored to developers than analysts.

VS Code also falls short with its lack of variable exploration and dataset-based tools.

Without these key features, working with and analyzing large amounts of data in VS Code can become tedious, complex, and time-consuming.

Therefore, while it is an excellent tool for coding, it isn’t as efficient or practical when dealing with datasets that play an integral part in data science.

Now I will say (and many have emailed me) if your only option at work is VS Code, then use VS Code – but if given the option, choose Spyder.

What VS Code is Good For

VS Code is an excellent choice for software engineering tasks within a team.

It has an innovative and powerful autocompletion feature that helps speed up the coding process and a super simple folder architecture that takes zero time to learn.

The git and docker integration allows developers to compare branches, stage changes, manage containers and commit code easily.

Heavy terminal use is also fluid by default, with full support for everyday command line operations like running code, stopping code, and restarting your programs.

VS Code works so well with the terminal that one is actually integrated into the IDE.

And to top it off, it offers various development tools (made by the community) for working with docker containers and general microservices architecture.

This makes it super easy for software engineers to debug and see how APIs work between their microservices.

Should I Spend Money on an IDE For Data Science?

It really is sad – everyone on the internet has something to sell.

If you’re wondering if you should ever pay for an IDE for data science, the truth is the answer is no.

With open-source software such as Spyder and Visual Studio Code, you can access powerful tools without spending a single dime.

Both provide smart code completion, real-time analysis, debugging capabilities, and many other integrations with various packages – all powered by the community.

Plus, the interface is well-designed and user-friendly, so even beginners can get up and running quickly.

Therefore it’s hard to justify spending money on an IDE for data science or software engineering.

While I’m sure there are scenarios out there where you would need to pay for an IDE, like a custom-built software package for some niche of coding, if you’re breaking into this game, you probably don’t need it.

When Should I Use Jupyter Notebook vs. Spyder?

Jupyter Notebook and Spyder are two of the most popular choices regarding data science applications.

And luckily for you, they both come in the Anaconda download.

While both are useful for writing code and doing data science, some key differences between them should be considered when deciding which one to use.

I prefer Jupyter Notebook for hyperparameter tuning and final touch-up modeling due to its enhanced usability over Spyder. Jupyter Notebook is well suited for making small changes and updating scripts quickly to get the desired results.

I sometimes bring over code from Spyder into Jupyter Notebook (shamelessly) if I have difficulty debugging it in Spyder.

One downside to using Jupyter Notebook is that sharing it with other data scientists can be clunky or awkward compared to using conventional software like an IDE.

In these cases, using Spyder may be a better choice.

Understanding when to use Jupyter Notebook versus Spyder can help streamline your workflow and make tasks more efficient.

Generally, I’m in Spyder about 70% of the time, using jupyter notebook to primarily clean up my code.