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The 5 Data Science Skills You Can’t Ignore in 2024

Boost your career with these essential data science skills

Source: DALL·E
Source: DALL·E

Storytime

My Internship Experience

Back in 2022, I was a Data Scientist intern at a bustling startup in the heart of Berlin.

My days were filled with the challenge of developing and implementing Natural Language Processing (NLP) models using BERT and Computer Vision models with Faster-RCNN. My task was to increase the accuracy of the company’s existing models, and I was eager to experiment with these models.

A New Perspective

During informal meetings discussing my growth as a Data Scientist, my supervisor started emphasizing something that, at the time, felt a bit perplexing.

He kept stressing the importance of focusing on the production side of model development, not just the experimentation side.

His words left me puzzled.

As far as I knew, the role of a data scientist was to understand business requirements, perform statistical analysis, and find the best model. What more was there?

Bridging the Gap to Production

This was a bit of an eye-opener for me. The concept of "putting the model into production" was new to me. I hadn’t studied this at university, and it certainly wasn’t a part of my skill set in 2022.

As the weeks went by, the picture started to become clearer.

I began to grasp what my supervisor meant and why it was crucial to upgrade my Data Science skills.

It wasn’t just about finding the best model or performing statistical analysis; it was about making sure that these models could be effectively integrated into the company’s systems and workflows.

This was the key to turning my work from mere experiments into real-world solutions that could drive business value.

Although my internship was dedicated solely to the experimentation part (in NLP and Computer Vision), this advice from my supervisor was very important for my career development.


Hello there!

My name is Sara Nóbrega, and I am a Data Scientist specializing in AI Engineering. I hold a Master’s degree in Physics and later transitioned into the exciting world of Data Science.

I write about data science, artificial intelligence, and Career Advice in these fields. If you want to read more, make sure you subscribe and follow me!


Photo by Dom Fou on Unsplash
Photo by Dom Fou on Unsplash

The truth is, while universities and formal education provide some essential skills, they often do not prepare students with the practical know-how needed in companies!

Last month, in May 2024, I helped someone choose courses for their Data Science master’s program and I noticed a lack of courses dedicated to model production or even an introduction to the topic.

Putting the model into production is part of a broad topic called Machine Learning Operations (MLOps). But MLOps is just one example; there are many other critical skills and knowledge areas that formal education does not cover, that I will outline in this article.

For example, knowledge of cloud computing is becoming increasingly important. With more companies moving their operations to the cloud, being proficient in cloud platforms like AWS, Azure, or Google Cloud can significantly enhance your ability to deploy and scale Machine Learning models.

Learning how to leverage these platforms’ various tools and services can streamline your workflow and reduce infrastructure costs.

Of course, you won’t need to master all of the skills! That will depend on your goals and your preferences.

But by expanding your skill set beyond what is traditionally taught, you can become a more versatile and valuable data scientist.

In this article, I will talk about those skills and more, providing you with a comprehensive guide to the skills you need to stay ahead in 2024 and beyond!


Why Should You Care?

Evolving Skills

Several years ago, the top skills for a Data Scientist included Python/R, machine learning, SQL, data visualization, and statistics. But Data Science is an ever-evolving field!

The skills required in 2017 differ significantly from those needed in 2024, which is a natural progression due to technological advancements and shifts in the job market.

Impact of AI

For instance, the recent boom in Generative Artificial Intelligence (GenAI) and large language models (LLMs) highlights the increasing demand for AI skills.

Does that mean AI skills will be in higher demand? I would say so.

While deep expertise in NLP or LLMs (important subsets of AI) might not be necessary for a data science position, a good understanding of AI systems, their business impacts, and their potential to drive value is becoming essential for any data-driven career.

AI Learning | Source: DALL·E
AI Learning | Source: DALL·E

The Expanding Role of Data Scientists

Overlap of Roles

But I do not want to be a Machine Learning Engineer, so does this apply to me?

It’s a fair question. However, many current job listings for Data Scientists include requirements that overlap with Machine Learning engineering skills.

While some companies clearly differentiate between Data Scientists, ML Engineers, and MLOps engineers, there is often an overlap.

And it makes sense! It is beneficial for companies to have employees who understand a bit of everything, ensuring that communication and the collaboration between different roles is effective.

Example of a Job Posting

In the image below, you can see a job posting for a current Data Scientist position, that I screenshot from LinkedIn (May 2024). This role is heavily focused on NLP and GenAI, despite being a Data Science position.

In this article, I will discuss some of the key skills mentioned in the job post.

Example of a Data Scientist job post on LinkedIn (May 2024) | Image by author.
Example of a Data Scientist job post on LinkedIn (May 2024) | Image by author.

And there is more.

I have noticed a growing trend where more and more ML Engineer and AI Engineer positions are increasing. In fact, it is estimated that the demand for AI and ML roles will increase by 40% from 2023 to 2027!

This shift is evident in the current job market, where companies are leveraging recent AI breakthroughs by recruiting more ML/AI Engineers.

People with Data Science backgrounds, who enjoy learning new skills, can adapt to any shifts in the job market!


In this article, I will outline five key skills you need to stay relevant in today’s job market. These skills will not only put you ahead but also provide flexibility to access more job opportunities!

For each skill, I will explain:

  • Why is the skill necessary;
  • How the skill is used daily by Data Scientists;
  • Provide real-world examples on how Data Scientists employ the skill;
  • Suggest nice ways to learn it 😉

Let’s begin!

Photo by Jan Tinneberg on Unsplash
Photo by Jan Tinneberg on Unsplash

Deep Learning

Why You Need this Skill Now

Deep learning has revolutionized fields like image and speech recognition, autonomous driving, and predictive analytics.

Its ability to process large amounts of data and uncover intricate patterns makes it indispensable for modern data scientists.

Moreover, deep learning is at the forefront of many cutting-edge technologies, such as generative adversarial networks (GANs) and reinforcement learning, which are pushing the boundaries of what’s possible in artificial intelligence.

These advancements are not only transforming existing industries but also creating entirely new ones, from creative arts to autonomous systems.

As industries continue to adopt deep learning technologies, the demand for professionals who can design and implement these complex models will grow exponentially​.

Deep Learning | Image by author.
Deep Learning | Image by author.

Studying deep learning is a crucial step towards acquiring the next set of skills outlined in this article.

Grasping the basics of deep learning builds a strong foundation essential for understanding and implementing advanced ML and AI techniques.

How do Data Scientists Apply this Skill?

In many ways. Data scientists can apply deep learning techniques to develop sophisticated models for tasks such as image classification, forecasting, natural language processing, and anomaly detection.

Real-World Example

Let’s say you work for a financial company.

Deep learning models are used to detect fraudulent transactions in real-time. By analyzing transaction patterns and identifying anomalies, these models help financial institutions prevent fraud and protect customer assets.

How to Learn this Skill?

Before diving into deep learning, ensure you have a basic understanding of mathematical foundations (linear algebra, calculus, probability and statistics).

Then, begin with the core principles of deep learning: neural networks and backpropagation.

Get hands-on experience with the leading deep learning frameworks (TensorFlow, PyTorch).

Then, move on to more complex models (CNNs and their architectures).

After, understand models designed for sequence data (RNNs, LSTMs..)

Once you’re comfortable with CNNs and RNNs, you can explore more advanced topics: Generative Adversarial Networks (GANs) and Reinforcement Learning.

AI and Machine Learning Exploration

Why You Need this Skill Now

AI and machine learning are reshaping industries right now.

The surge in AI technologies, particularly NLP, LLMs, and GenAI, has made AI skills increasingly valuable.

Understanding how these technologies work and their potential applications is crucial for maximizing their impact in business and tech.

AI is and will continue to manifest itself in the fields of predictive analytics, automation, anomaly detection, chatbots and intelligent systems.

Companies are investing heavily in AI technologies to find new ways to provide value to their costumers, so no wonder there is a surge in demand for professionals skilled in these areas​!

How do Data Scientists Apply this Skill?

There are so many ways Data Scientists can use AI nowadays!

In 2024, Data Scientists employ AI to enhance various aspects of their work.

Automated Machine Learning (AutoML) streamlines the model development process, while advanced NLP and computer vision applications provide deeper insights into text and image data and automate several tasks.

Regarding time-series analysis, advanced AI algorithms are applied to predict future trends, improving accuracy in forecasting.

AI-driven data preprocessing and integration improve data quality and accessibility.

Explainable AI (XAI) enhances model transparency and helps people understand how decisions are made.

AI models are also used to detect unusual patterns (anomalies) that do not conform to expected behavior, useful in fraud detection, network security, and predictive maintenance.

Chatbot development | Image by author.
Chatbot development | Image by author.

Real-World Examples

One of the most visible applications of AI and machine learning that I see since the AI boom is in the development of chatbots and virtual assistants.

These AI-driven tools use NLP to understand and respond to customer inquiries, providing support and information round-the-clock.

For instance, many companies have integrated AI chatbots into their customer service operations to help answer routine questions.

Additionally, AI is being used in new ways such as personalized recommendation systems, predictive maintenance in manufacturing, and smart healthcare systems that can analyze patient data to suggest treatments.

How to Learn this Skill?

It can be very overwhelming to start learning LLMs and GenAI.

I would recommend for you to keep it simple.

You don’t need to master all models, frameworks, or skills.

If you want to specialize in one of them (e.g., becoming an NLP Engineer), go ahead.

But if your goal is to upgrade your skills to stay relevant in the job market or to satisfy your curiosity, having enough knowledge to get the job done should be more than enough in the first phase!

To begin your journey in AI and advanced machine learning, you can start with foundational courses that cover basic concepts and then build up your knowledge.

Assuming you have a solid knowledge on the common ML models (Linear and Logistic Regression, Random Forests, clustering algorithms etc), you can dive to more complex topics.

Explore NLP, a critical component of AI focused on the interaction between computers and humans. Start learning about text processing, text classification and word embeddings.

Have a good solid understanding on deep learning (that’s why I outlined it as the first skill in this article).

Then, you can deep dive into LLMs. Understand transformers, BERT and GPTs, and learn about training and fine-tuning them.

Since the AI boom, the amount of free content available online about this topic has also rocketed!

Cloud Computing Basics

Why You Need this Skill Now

Knowing how to work with cloud platforms lets data scientists use powerful infrastructure, which cuts down on the cost and the difficulty of storing and processing data.

Cloud computing offers scalable and flexible resources that are perfect for dealing with big datasets and complex calculations.

Additionally, it facilitates collaboration and speeds up model deployment, allowing you to focus more on analysis and less on managing hardware.

How do Data Scientists Apply this Skill?

Data scientists use cloud platforms to:

  • Store data: Keep all the data in one place without worrying about storage limits.
  • Process data: Use powerful tools to quickly clean and analyze your data.
  • Experiment with models: Test out different machine learning models easily.
  • Track experiments: Keep a record of all experiments and their outcomes.
  • Deploy models: Launch models into real-world applications seamlessly.
  • Collaborate: Work together with a team on the same data and models.
  • Automate workflows: Set up automatic processes for repetitive tasks.

Cloud services facilitate collaboration, scalability, and efficient resource management, making them indispensable in modern data science projects!

Real-World Example

A data scientist at a healthcare startup can use a cloud platform to build and deploy a machine learning model that analyzes medical imaging data to detect early signs of diabetes, facilitating faster and more accurate diagnosis for patients.

How to Learn this Skill?

Start by learning one cloud platform. You don’t need to know how to work with all of them!

From my experience, once you know how to use one platform, you can easily switch to another. Companies care more about hands-on experience with any single platform.

Personally, I started learning Microsoft Azure. The 3 most famous ones are Amazon Web Services AWS, Azure and Google Cloud Platform (GCP).

Machine Learning Model Deployment

Source: DALL·E
Source: DALL·E

Why You Need this Skill Now

Developing a machine learning model is just the first step. Deploying these models in production environments is crucial to provide real-time predictions and derive business value.

Proficiency in deployment ensures that models can scale, integrate seamlessly with existing systems, and operate efficiently under various conditions.

Is this skill needed for all data science roles? No. But as we have discussed before, there is often an overlap with machine learning engineer skills, and it is useful to know the basics to ensure efficient communication with other engineers.

Additionally, if data scientists understand the requirements for deployment and the available resources, it helps them choose the best model for the project at hand!

How do Data Scientists Apply this Skill?

Data Scientists often collaborate closely with machine learning engineers to package, optimize, and integrate models into production systems.

They ensure that models are scalable, secure, and maintainable by setting up APIs, monitoring systems, and CI/CD pipelines.

It is important for Data Scientists to have machine learning model deployment skills because it allows them to translate their models into actionable and real-world applications.

It is important that their insights and predictions can be actually used to drive business value and decision-making.

Real-World Example

You may be familiar with e-commerce platforms.

Companies deploy recommendation engines to suggest products to customers based on their browsing and purchasing history.

These models are integrated into the website’s backend to provide real-time recommendations, making sure it improves the user’s shopping experience.

How to Learn this Skill?

After grasping the fundamentals of machine learning, you can start to familiarize yourself with cloud platforms like AWS, GCP, or Azure.

You should also study some deployment tools like Docker or Kubernetes (start with Docker, as it’s more beginner-friendly and widely used).

It’s also important to study and understand good MLOps practices to manage the lifecycle of machine learning models​ (continuous integration, continuous deployment (CI/CD), and monitoring).

Big Data

Why You Need this Skill Now

"Nowadays, all data is big data", or so I’ve heard.

While this isn’t always true (I’m currently working on a project with just 150 rows of data… 😅 ), it generally holds.

With the exponential growth of data, having big data skills is crucial.

These skills enable you to efficiently process and analyze massive datasets, helping you extract valuable insights and make informed decisions.

Big data skills encompass a range of skills including proficiency in programming languages (like Python, Java, and SQL), understanding of data structures and algorithms, and knowledge of big data processing frameworks such as Apache Hadoop and Spark.

These skills are essential for handling and working with datasets too large for traditional data processing tools.

Photo by Markus Spiske on Unsplash
Photo by Markus Spiske on Unsplash

How do Data Scientists Apply this Skill?

Data scientists use big data skills to handle, process, and analyze large volumes of data using tools like Hadoop, Spark, and distributed databases, allowing them to uncover patterns and insights that wouldn’t be visible in smaller datasets.

Real-World Example

A data scientist at an environmental research organization uses Apache Spark to process and analyze satellite imagery for remote sensing applications.

Each day, they handle terabytes of high-resolution images from multiple satellites, using Spark to clean, preprocess, and store this vast amount of data efficiently.

They apply machine learning algorithms to detect changes in land use, monitor deforestation, and track the health of agricultural crops.

For instance, by analyzing the spectral data from the satellite images, they can identify areas affected by drought or disease.

How to Learn this Skill?

Start with learning one big data platform or tool such as Apache Spark or Hadoop. Take online courses, then work on real-world projects, and practice handling large datasets to build up your expertise.

Final Thoughts and Key Takeaways

Source: Unsplash
Source: Unsplash

Why is it important to learn new data science skills in 2024?

The answer is simple: the field of data science is rapidly evolving.

Staying current with new skills is crucial to maintaining a competitive edge and continuing to add value to organizations.

As we’ve explored throughout this article, the role of a data scientist is expanding, and so are the skills required!

The [World Economic Forum’s Future of Jobs Report 2023](http://The Future of Jobs Report 2023 | World Economic Forum (weforum.org)) highlights that AI and big data skills are among the top in-demand skills and will continue to grow in importance through 2027.

According to this report, 60% of workers will require training in these areas as businesses continue to adopt advanced technologies at a rapid pace.

This means that investing time in learning these skills now will pay off significantly in the future.


Looking back at my internship at the startup in Berlin, I realize how invaluable that experience was.

It taught me that being a data scientist isn’t just about the thrill of discovery and analysis but also about ensuring that those discoveries can be practically applied.

This lesson is one I carry with me to this day, always reminding me to balance the scales between experimentation and production.

So, I encourage you to start developing these skills right now!

Whether it’s deep learning, AI and ML, cloud computing, model deployment, or big data, each of these areas is critical.

Not only will they make you more versatile, but they’ll also open up new opportunities and allow you to make a greater impact in your field.

Remember, the journey of learning and growth never stops. Embrace these new skills and keep pushing the boundaries of what’s possible in data science. Your future self will thank you! 😉


Thank you for reading! If you want to support my work, you can buy me my favorite coffee: a cappuccino. 😊

Book a call with me, ask me a question or send me your resume here:

Other Resources:

Sara’s Data Science Free Resources


Curious about how I transitioned from Physics to Data Science? Check the article below!

How to Transition from Physics to Data Science: A Comprehensive Guide

Do you work with time-series data? Then you must check the article below!

The Ultimate Guide to Finding Outliers in Your Time-Series Data (Part 1)


My name is Sara Nóbrega and I am a Data Scientist with a background in Physics and Astrophysics. I’m an enthusiast on AI, MLOps, Smart Cities, Sustainability, Cosmology and Human Rights.


References:

The state of AI in 2023: Generative AI’s breakout year | McKinsey

Jobs In Data – Machine Learning Engineer vs Data Scientist – Salary Gap (jobs-in-data.com)

Data Scientist Job Market 2024: Analysis, Trends, Opportunities | 365 Data Science

Machine Learning Engineer Job Outlook 2023: Research on 1,000+ Job Postings | 365 Data Science

The 10 Most Important Data Science Skills in 2023 – HackerRank Blog

The AI Talent Rush: 10 In-Demand AI Jobs for 2024 (onwardsearch.com)

AI investment forecast to approach $200 billion globally by 2025 (goldmansachs.com)

The Future of Jobs Report 2023 | World Economic Forum (weforum.org)


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