Data Science vs. AI: How I Would Choose My Path

William Imoh · Improve this Guide

Choosing between specializing in data science and AI comes down to what you like and the problems you want to solve. If you enjoy analyzing data, uncovering insights, and making data-driven decisions, data science might be for you. But artificial intelligence is the way to go if you’re into creating systems that can learn, adapt, and make decisions on their own.

When I first got into tech, I wasn’t sure which path to take. I spent late nights researching, feeling both excited and overwhelmed. Data science and AI seemed so similar that telling them apart felt impossible, especially since many jobs required skills in both. But as I dug deeper, I realized they have distinct focuses despite their overlap.

Data science focuses on extracting meaningful insights from data. As a data scientist, you’ll be responsible for designing datasets, building models to analyze data, identifying patterns, and solving complex problems to help businesses make better decisions. You’ll do this using statistical analysis and several machine learning techniques like supervised and unsupervised learning.

In contrast, artificial intelligence is about developing intelligent systems that can perform tasks that typically require human intelligence. These tasks include pattern recognition, predictive analysis, and language translation. You’ll design algorithms to learn and solve problems without constant human intervention.

If you don’t know much about data science and AI, don’t worry about it. Check out our beginner’s guide on data science and AI to get a good overview of both fields. In this guide, I’ll show you what each field is about, the skills you need, and the opportunities they offer. By the end, you’ll know which career path suits you best based on your interests, skills, and goals.

Data science vs. AI: What are the key differences?

When discussing data science and AI, many people often mix them up. While they share similarities, their purposes, tools, and techniques differ. The table below summarizes the key differences between data science and AI:

Characteristics	Data science	Artificial intelligence
Primary focus	Extracting insights from data	Building intelligent systems that mimic human intelligence to perform tasks
Responsibilities	Data collection, data cleaning, data analysis, data modeling, data visualization, and building predictive models	Building and training machine learning models, machine learning algorithms, and artificial intelligence applications
Tools	Python, R, SQL, Pandas, Hadoop, TensorFlow, Tableau	Java, Python, PyTorch, OpenCV, spaCy, GPUs, AWS and Keras
Educational path	Bachelor’s in computer science, statistics, or mathematics; master’s in data science	Bachelor’s in computer science, mathematics, or engineering; master’s in AI or related field
Career paths	Data analyst, data scientist, business analyst, machine learning engineer	AI engineer, machine learning engineer, AI product manager, NLP enginner, AI research scientist
Applications	Predictive analytics, fraud detection, customer segmentation, healthcare analytics and more	Self-driving cars, chatbots, robotics, predictive maintenance, medical imaging analysis and more

Primary focus

One big difference between data science and AI is what they focus on the most. Data science focuses on uncovering insights and solving problems through data analytics. It uses structured data like customer databases to identify patterns and solve complex problems. You’ll use advanced tools and machine learning techniques like computer vision to work with unstructured data like videos and images.

If you choose data science, you’ll work on extracting valuable insights from data. For example, a data scientist working in finance will use anomaly detection to analyze transactional data. This helps with fraud detection and risk assessment, e.g., unusual login attempts.

In contrast, if you choose artificial intelligence, you’ll focus on building systems that mimic human intelligence, like learning, reasoning, and solving problems. It uses structured and unstructured data to enable machines to perform tasks like pattern recognition in images and speech and natural language processing. For example, AI virtual assistants like Siri can listen and reply to what you’re saying. They use natural language processing models to recognize and understand your voice (human language). It makes it easy for you to ask and get answers to questions just by talking.

Daily responsibilities

Data scientists and AI engineers have different responsibilities that show their focus areas. As a data scientist, your daily duties will include:

• Data collection: This is often the first thing you do when extracting insights from data. Your job will be to collect large volumes of data from several sources, such as databases. For example, a healthcare data scientist will collect and analyze patient data to create models to help in diagnosing diseases. It’ll make it easier to predict patient outcomes and make treatment plans better.
• Data cleaning: After you collect all the data, the next step is to clean the data. Doing this means removing errors from the raw data to prepare it for data analysis.
• Data analysis: After cleaning, you’ll analyze data to identify patterns and trends. You’ll do this using statistical analysis and machine learning techniques like clustering.
• Building models: You’ll create predictive models to make future predictions based on historical data. A company like Netflix, for example, uses predictive models to suggest TV shows and movies to you. The data scientist will analyze your past data to see what shows you’ve watched, how you rated them, and more. Then, they will use this data to build a model that suggests similar shows you’ll like.
• Data visualization: Visualizing data is about presenting complex data in a visual format. As a data scientist, you’ll use data visualization tools like Tableau and Power BI to present data in a way that every team member will understand.

On the flip side, AI involves various responsibilities like designing, training, and testing AI models. As an AI engineer, your daily responsibilities will include:

• Data preparation: You’ll collect, clean, analyze, and work with large amounts of data to train and test AI models (i.e., machine learning models). Yes, as an AI engineer, you’ll also analyze data, although it is the primary duty of the data scientists. Doing this lets you check if the data is good for the AI model and spot any problems that could mess up the training.
• Model training: You’ll be responsible for developing algorithms that enable machines to learn from data using machine learning techniques like computer vision and neural networks. For example, automotive industries use artificial intelligence to create self-driving cars. They use AI algorithms to analyze sensor data from cameras, radar, and lasers to see what’s around them.
• Review models: You’ll test and make sure that the models provide reliable results. Imagine you’ve developed a model to identify diseases from analyzing medical images. You’ll use images the model hasn’t seen before to test its accuracy at spotting diseases. If it misses something, that’s your cue to tweak it until it gets better.
• Implementation: Once you create a model, you need to make sure it works well with other systems. It’s like having a chatbot on a website to handle customer inquiries, using a fraud detection system in a bank to analyze transactions, or incorporating GPS functionality in self-driving cars for navigation. Additionally, you’ll monitor and maintain its performance and update it when needed.

The tools you’ll use

One important thing to consider when choosing between data science and AI is the tools you’ll use. In data science, you’ll work with programming languages like Python, R, and SQL to extract insights and analyze data. You’ll use Python libraries like Pandas to manipulate data and NumPy to make math calculations.

As a data scientist, you’ll also use big data technologies like Hadoop to work with large volumes of data. Machine learning libraries like TensorFlow will help you develop advanced machine learning models. Also, you’ll use data visualization tools like Tableau and Power BI when sharing your results.

On the flip side, artificial intelligence needs more advanced tools to work its magic. You’ll use programming languages like Java and Python, like in data science, but you’ll focus on AI frameworks like PyTorch to train deep learning models, especially neural networks. These frameworks can do various machine learning tasks, such as supervised learning (e.g., image classification), unsupervised learning (e.g., anomaly detection), and reinforcement learning (e.g., game playing) when needed.

Tools like OpenCV will help with computer vision tasks, and libraries like NLTK and spaCy will help with natural language processing. As an AI engineer, you’ll also work with specialized hardware like graphics processing units (GPUs) and tensor processing units (TPUs). These tools provide the computational power to train large neural networks well. Finally, you’ll work with cloud platforms like AWS and Google Cloud AI Platform to deploy and manage your AI models.

How do the skills you need differ?

Now that you know the differences between data science and artificial intelligence, let’s look at the skills you need. As a data scientist, it’s important to build a solid understanding in the following areas:

• Proficient in programming skills like Python and SQL for data analysis and manipulation.
• Strong understanding of statistics and mathematics to analyze and interpret data.
• Good in data wrangling for cleaning and organizing datasets before analysis.
• Proficient in data mining to extract useful patterns and insights from large datasets.
• Strong data visualization techniques to present valuable insights through tools such as Matplotlib or Tableau.
• Basic understanding of machine learning principles and algorithms to apply predictive analytics and solve business problems.
• Good critical thinking skills to analyze data and provide solutions to business problems using data.

In contrast, as an AI engineer, you’ll create complex AI systems using the following skills:

• In-depth knowledge of advanced machine learning concepts, including neural networks, to create models for tasks like image recognition or natural language processing.
• Proficiency in programming skills (Python, C++, Java) for creating complex algorithms.
• Understanding mathematics, e.g., linear algebra and calculus for algorithm development.
• Experience with frameworks like TensorFlow and PyTorch for building and deploying AI models.
• Knowledge of computer vision and natural language processing to create intelligent systems that understand and interact with data.

The educational pathway to data science and artificial intelligence career

A very good educational foundation is necessary to start a career as a data scientist. The first step is getting your bachelor’s degree in computer science, data science, or artificial intelligence. These fields cover topics like algorithms, machine learning, and software development.

On the other hand, artificial intelligence often requires higher educational qualifications, such as a bachelor’s, master’s, or Ph.D., due to its technically complex and research-oriented nature. You’ll first need a bachelor’s degree in computer science, data science, or artificial intelligence to start a career in this field. This will give you a good understanding of machine learning, mathematics, and statistics. You’ll go into detail on topics like algebra and calculus and learn languages like Python and Java. Some AI engineers may pursue a master’s degree if they aim for senior or research positions, but it’s not always necessary.

Also, you don’t always have to attend a university to learn. Some people who want to become data scientists and AI engineers go to boot camps, take online courses, or get certifications instead. Platforms like roadmap.sh offer detailed data science and AI courses to help you learn. The platforms are more flexible and focus on practical projects, so you can create a portfolio to showcase your skills.

Which field offers better career prospects?

Data science and artificial intelligence both look promising in terms of job opportunities. Both fields are in high demand, so there are plenty of job opportunities out there for you. However, artificial intelligence jobs usually pay better because they need more advanced skills and technical knowledge in machine learning and algorithm development.

Data science is a recognized field with many uses in industries like finance, healthcare, and marketing. If you’re interested in data science, you could work as a data analyst, business analyst, or data scientist. According to Indeed, the average salary for a data scientist in the United States (USA) across all industries is $125,495.

On the other hand, artificial intelligence is in high demand in industries like robotics, automotive, and tech startups. If you’re interested in artificial intelligence, you can work as a machine learning or AI engineer. According to Indeed, engineers working in machine learning in the USA make an average of $162,735 annually.

While salary is important when choosing between data science and artificial intelligence, it shouldn’t be your only factor. For me, it’s less about which field pays more and more about what excites you daily. You’ll enjoy success and work satisfaction when you choose a job that matches your interests.

Do data science and artificial intelligence overlap?

Yes, data science and artificial intelligence overlap. Data science is like the backbone of AI. It supplies the data and insights needed to train and improve artificial intelligence models.

Many skills overlap between data science and artificial intelligence. For instance, being good at coding in Python, R, and SQL is important in both fields. Both fields also need machine learning, statistics, and data visualization knowledge. Switching from data science to AI or vice versa is straightforward because they share many similarities in skills and tools.

Will AI replace data science?

Will AI replace data science? This is a common concern among many data scientists since there’s been a lot of buzz about AI. The simple answer is no: AI is not replacing data science.

Artificial intelligence tools can make it easier for data scientists to analyze data and predict trends faster. But we humans make sense of all that data, put it into context, and use it to make decisions for the company. Instead of seeing AI as a rival, think of it as a helpful tool that can help you get better at data science.

Next steps to find your fit

If you’re still trying to figure out which path to take, here are a few steps to help you find what feels right for you:

Know your strengths

To find out which path is right for you, consider what you’re good at and what excites you. Data science might be a good fit if you love analyzing data and finding out hidden stories behind numbers.

On the other hand, artificial intelligence might be better if you’re into technology, algorithms, and creating systems that imitate human intelligence.

Connect with others

Talk to some data science and artificial intelligence professionals and get their advice. Start by attending conferences or joining online communities to connect with others.

You can ask questions, share your thoughts, and learn from others as a community member. When you chat with other professionals, ask them what they enjoy about their work, what their challenges are, and if they have any tips for newcomers. Listening to their stories can give you a better idea of what each job is really like.

Don’t be afraid to try

Don’t be afraid to try new things and step out of your comfort zone. You might think you’re into data science, but after taking some classes or working on projects, you could realize that artificial intelligence is where your true passion lies. It is completely normal and is part of the learning journey.

What next?

Artificial intelligence and data science have changed how businesses work and make decisions. Both fields have great job opportunities, but the skills needed for each are different. Understanding the basics of these fields will help you pick the right path for you.

It’s not about figuring out which field is better than the other—it’s about choosing the one that fits your goals, interests, and skills. So relax and take some time to figure out what you’re really into before making any big decisions.

For continuous learning, visit the roadmap.sh data science and artificial intelligence guides. Both guides have all the resources you need to start your journey as a data scientist or AI engineer.