Data Science vs Machine Learning vs AI: How They Work

Data Science vs Machine Learning vs AI: How They Work

Hanna Kleinings


Customer Operations Manager

November 16, 2022

Year over year, Process Automation is becoming increasingly popular in business operations. In fact, according to a McKinsey study, in 2020, 31% of businesses have fully automated at least one internal process, with at least 66% of organizations piloting it at their organization.

Three areas that are witnessing particular interest – Artificial Intelligence (AI), Machine Learning (ML), and Data Science. Each of these disciplines requires a specific range of skills, which is why it’s essential to understand the exact capabilities you need to hire for.

In this piece, we discuss what AI, ML, and Data Science are, how they overlap, and what common misconceptions you may probably have. All to make sure you hire for the right process - or find the right toolset for your business.

Data Science vs ML vs AI – definitions

Before we discuss the skillsets for each role, let us start by explaining the definitions and common misconceptions around AI and Data Science, as well as Machine Learning.

Artificial Intelligence (AI)

Artificial Intelligence (AI) is the umbrella term used to describe technological advancements that aim to simulate human intelligence and reasoning by machines. AI technology has several key characteristics; namely, it can display abilities such as logical thinking, skills refinement, and learning.

That being said, when people discuss AI, they often mix up two types – General AI and Narrow AI.

General AI is as of now unattainable - it relates to the level of reasoning and thinking equal to that of humans - and machines are only as smart as humans train them to be. Respectively, Narrow AI is the type of Artificial Intelligence we’re already seeing in use, and it revolves around training the machine to work on specific tasks - think RPA or any workflow tool you've come across.

Nevertheless, more often than not, it requires a highly experienced team. It’s also important to realize that AI often requires building a human-like neural network, and this translates into a lot of computing power and data storage - also known as costs to your business.

Some common misconceptions:

Data Science

Data Science is the term used to describe technology that derives insights from data. It is commonly used by analysts to bring awareness to businesses and drive growth. From a techie point of view, it leverages both Machine Learning and Artificial Intelligence, which also means it requires expertise in multiple domains, such as programming, statistics, and mathematics.

Stephan Kolassa's Data Scientist Venn Diagram
Stephan Kolassa's Data Scientist Venn Diagram Challenging Conway. Also, check out this delightful commentary on it.

Let us debunk a few common myths around Data Science below:

  • Only large organizations can take advantage of Data Science. In reality, small and large businesses alike can benefit from using Data Science. Unlike what many people think, it’s not necessary to have a large infrastructure or complex processes in place to process large datasets and collect valuable insights. You just need data.
  • If you want to be a Data Scientist you need a Ph.D. in Statistics. While you need to be proficient in math you don’t need to hold a Ph.D. or a Master’s degree in Statistics to become a Data Scientist.
  • AI vs Data Science – the assumption that Data Science will soon be substituted by Artificial Intelligence. Chances are, you’ve seen questions like “What is more in demand, a career in Data Science or AI” and other similar queries online. In fact, there is a discussion around whether it still makes sense to hire for Data Science skills in the first place. This comes from a presumption that the rise of Data Science automation means it will eventually be replaced by AI. However, we must remember that the latter still requires human instructions and logic, and this guarantees that Data Scientists won’t be replaced by machines.
  • ML and Data Science are basically the same. As mentioned above, Data Science certainly leverages Machine Learning algorithms, but it also uses them to develop AI capabilities. Therefore, Data Science casts a much wider net.

Machine Learning (ML)

Machine Learning uses data and algorithms to emulate human learning; the more data it gathers and analyzes, the more accurate it becomes. ML is part of Data Science: by applying statistics, algorithms are able to make classifications and predictions, which helps in identifying patterns and revealing important insights within data.

Machine Learning... well, learns. It's all about breaking down data and finding patterns - and applying this to real-life problems. We could go on and on about this - but we already have! Find out more about Machine Learning here.

But you may have heard people saying this:

  • ML and AI are here to replace humans. This is probably one of the most common misconceptions that we hear, almost daily. Machine Learning is supposed to improve the effectiveness of our work, not replace us. It gives us time to focus on more critical aspects of our jobs like strategy or creativity.
  • To learn, machines need experience. For Machine Learning mechanisms to work, they need to be fed with data. They learn from it and create algorithms that can be applied to solve real-life problems.
  • Machine Learning and AI are the same. These terms are related, but they’re not synonymous. Machine Learning is a subset of the AI family.
Machine Learning is a subset of AI and deep learning is a branch of machine learning
Machine Learning is a subset of AI.

Which is right for your organization?

If you're looking to move towards a data-led process, and have an eye out for automation, you need to hire the right team. Here are some of the skills you want to be looking out for:

Programming languages

In hiring for ML, AI, and/or Data Science, you'll probably want someone who is comfortable in multiple programming languages like Python, R, Java, SQL, C++, etc. Python is the most common and easiest programming language to create complex algorithms, for the high-end programming language, C++ speeds up the entire process. AI professionals require R to build plots and stats.

Knowledge of statistics, mathematics, algebra, and probability

You can’t master ML, DS, or AI without being proficient in mathematics. Among others, you’ll have to be able to select the right algorithms and come up with various validation strategies which just cannot be done painlessly if you don’t love numbers!

You'll also want someone who can make these numbers actionable - and who is well-versed with problem-solving and analytical skills. The knowledge of Applied Mathematics, Algebra, and Statistics is necessary to grasp the problem. ML, AI, and Data Science experts decide which algorithm is suitable to address the problems to optimize for outcomes. If you have someone on your team with sufficient knowledge of statistics & probability, they can help you utilize various models including Hidden Markov Models, Naïve Bayes, Gaussian Mixture Models, etc.

Moreover, you'll want someone comfortable with data visualization. Your team also needs a way to make sure the graphs properly convey complex concepts, such as hypothesis testing, in a comprehensible way. There are quite a few visualization tools for Ftists, including Tableau, Microsoft Power BI, and Google Data Studio. While these solutions can do the job for you and automatically create visuals, it’s still important that your team overlooks the process.

Good command of signal processing techniques

The advanced signal processing algorithms bandlets, wavelets, curvelets, shearlets, contourlets, etc are notable for processing information. To consider: someone well-versed with time-frequency analysis can implement various strategies to generate good outcomes.

Expert in Artificial Neural Network Architectures

Neural Networks is a structure made up of artificial neurons that help produce a single output from multiple input signals. It helps to observe multiple aspects of a datasheet, relate each aspect, and generate a single output. Even you can find complex patterns from a vast volume of data. ML, AI, and Data Science experts focus on multi-layer perceptrons, radial basis networks, recurrent neural networks, generative adversarial networks, and convolution neural networks.

Connecting process challenges and solutions

To solve some common business problems, you often need to leverage a combination of skills:

Detecting spam and filtering out noise

Dreaming of a world where your mailbox is smart and understands which emails you actually need to read? You'll likely need a tool or a team of ML engineers to build smart filtering into your workflow - especially if you want to help your mailbox treat emails just like you would in real life. Not only this, but you'll also need to consider the required degree of computational speed. Organizations need a cloud computing environment and high-end multiple processors to meet computational requirements. Usually, small and mid-sized organizations suffer in this situation.

screenshot showing's categorizing emails functionality
Nearly everyone I've ever talked to - ever - struggles with emails. Why not let machines do the work?

Automating customer communication through a chatbot

To create a chatbot with basic capabilities such as answering simple questions, your developers need to know how to work with NLP (natural language processing) capabilities. While such a chatbot can answer simple queries, if the person goes off-script, rule-based automation often fails. Building an advanced chatbot that understands the beautiful mess that language is, requires a team that has a mix of AI and ML/Deep Learning skills.

Predicting demand based on customer behavior/sales data

Demand prediction and forecasting also require a mix of data modeling and mathematics, widely used in ML. You would likely need to hire developers with Python skills for assessing the probability of a customer returning to your store, or model potential demand for your products on the market. This, for instance, can be useful if you produce physical goods, where shipping costs, weather, source material availability, etc., all account for significant risk factors.

In a nutshell...

There will always be a high demand for ML, AI, and Data Scientists. However, we all know it -  recruitment is a lengthy and costly process, which isn’t financially attainable for all businesses – especially SMEs.

The good news is: You can always outsource this. We wrote lengthy guides on both AI as a Service - as well as MLaaS providers. You could even try your hand at various no-code tools and build your own AI models - that solve a specific problem in your organization.

You can take a look and see if Levity may be a good fit for your idea - we'd love to hear from you and help you get started on your no-code AI automation journey.

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll probably love Levity.

Sign up

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll probably love Levity.

Sign up

Now that you're here

Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.

If you liked this blog post, you'll probably love Levity.

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