1) Generative AI & Prompt Engineering

Generative AI has taken the industry by storm in 2023. According to IoT Analytics’ Generative AI Market Report, the market for generative AI software and services reached $6.2 billion in 2023.¹  By 2032, it's expected to reach a market size of 208.8 billion dollars.²

Generative AI won't go away anytime soon, so developers need to learn how to work with Generative AI to stay competitive. This can mean a few different things. 

For some, it means you'll be working with AI models themselves. This would require a deep knowledge of data science and machine learning (DSML) concepts. But this is a complex specialization, and we don't all need to be machine learning experts to leverage AI in the future.

In fact, for most developers, the growth in AI tools means you'll need to learn how to build apps on top of those tools. In order to do that, you'll want to know the basics of how Large Language Models (LLMs) work. You'll also want to learn how to use a framework that helps you leverage LLMs for app development (one popular choice is LangChain).

A less technical subskill for Generative AI is prompt engineering. This is the practice of writing prompts that generate effective outputs for a LLM tool, and is a new skill we all need to master.

Learning Tip: Educative offers hundreds of hands-on Data Science and Machine Learning courses, projects, and assessments if you want to dive deep into the technology at the heart of the AI revolution. And if you want a more beginner-friendly intro into the power of Generative AI, I recommend the curated Skill Path: Prompt Engineering Essentials

2) Cloud Computing

Cloud computing will continue to gain traction in the industry in 2024. This is both because more companies (in the tech industry and beyond) are moving toward the cloud, and because the Generative AI boom is transforming the cloud landscape. 

AI's continuing integration into the cloud will cause massive changes for how we handle cloud systems. For one, the prevalence of Generative AI will make data accessibility and security larger priorities than ever before. To add to that, developers will have to rethink and rebuild certain approaches to cloud infrastructure. Our cloud infrastructure simply isn't built to sustain the heavy workloads that AI models require, and yet, AI-optimized chips and hardware are limited in supply. In other words, we're dealing with constraints — which means we need skilled developers to get creative to find solutions around them. 

As such, we'll see a higher demand for developers who have a deep understanding of cloud services and how they work. 

There are various different cloud platforms out there, but AWS is by far the dominant one. As the figure below shows, AWS far outranked Azure and Google Cloud in 2023's Stack Overflow survey.

If you've yet to pick a cloud platform to start learning with, I'd recommend AWS. Given current trends, chances are AWS adoption will continue, and you’ll be likely to encounter AWS at work. However, even if you need to use a different platform, the good news is that the skills you learn on one platform will be transferable among the others.

Learning Tip: Educative Cloud Labs get you hands-on with AWS cloud services through 110+ immersive labs — without any of the setup or cleanup.

3) Python

It’s probably not news that Python remains one of the most in-demand programming languages out there. It continues to top various developer surveys and industry evaluations — in fact, it's currently (as of December 2023) ranked #1 on the TIOBE index.

In general, Python is seeing more and more adoption given current trends in the industry. Its many use cases for AI and ML have a huge part to do with this. Python has various libraries, tools, and frameworks for machine learning, including NumPy, TensorFlow, PyTorch, and Keras.

But Python's use cases extend far beyond AI and ML. It's used in various industries for game development, web development, DevOps, and much more. Just a few of the companies using Python include Dropbox, Spotify, Amazon, Reddit, and Stripe.

If you don’t know Python, chances are that you will encounter Python more in your daily job. We can only expect to see an increasing number of open source libraries and tools built around Python. So learning to build in Python will grant you more opportunities to contribute to future development trends.
Finally, if you're an aspiring or beginner developer, Python is a great choice for your first programming language. You can use it for various specializations later in your career, but it's still beginner-friendly.

Learning Tip: Educative offers several hundreds Python Resources for all skill levels and career specializations (including nearly 100 interactive projects, where you can get hands-on with dozens of popular libraries and frameworks through real-world, full-featured applications).

4) Trending Languages: C#, Go, Rust

Certain languages have skyrocketed in adoption over the last year. Targeting these languages for your toolbelt will make you a valuable asset for teams.

One of those languages is C#, originally developed by Microsoft. For the second year in a row, the TIOBE Index is predicting that C# will be their 2023 “Language of the Year.” While C++ unexpectedly took that title last year, C# seems well-positioned to win this time — at least according to CEO of TIOBE Software, Paul Jansen.

Why is C# so popular? In short: it's versatile. C# supports various programming paradigms, as well as cross-platform development through the .NET framework. Its use cases span game development to web development. And it's popular for enterprise applications because of its scalability and compatibility with Microsoft technologies. 

Meanwhile, Go is also rising in popularity. Go (or Golang) was developed by Google, but is used in various companies ranging from Uber to Twitch. Go is a great choice for scalable, performant software. Among other benefits, Go's built-in concurrency support makes it a popular choice for networking, cloud computing, and distributed systems. Go applications also have the benefit of going hand-in-hand with containerization and orchestration tools like Docker and Kubernetes. 

On the back end, we're seeing a lot more adoption in Rust too. Rust was developed through Mozilla, but is used in many companies today, including Amazon, Atlassian, Discord, and Microsoft. Rust is very effective when performance and safety are priorities in your application. This makes Rust a popular choice for performance-sensitive software like operating systems and embedded systems. Since it allows low-level control, it's also commonly used for device drivers and concurrent systems.

Learning Tip: Educative offers dozens of interactive courses and real-world projects in all 3 languages: C#, Go, and Rust.

5) SQL

If you're working with data and still thinking about whether you should learn SQL (Structured Query Language), now is the time to learn it. 

In the last Stack Overflow Developer Survey, SQL was among the top 4 popular technologies. (In fact, SQL was third in 2022's survey, until Python took its place this year).

SQL is a programming language for managing and querying relational databases. If it hasn't already, SQL will probably become a part of your job soon. 

Compared to other tools like MongoDB, SQL is a strong fit for structured data and complex queries involving relationships between entities. Incidentally, most of the world's data is structured and relational. Therefore, SQL finds itself applied to various use cases, from data analysis to reporting.

SQL is a tech agnostic language, so it can be utilized with various database tools. And by supporting ACID properties, SQL databases ensure data integrity, predictability, and reliability.

Knowing how to work with SQL is among many valuable skills in data science. DSML skills are crucial and won't be losing importance anytime soon. If SQL doesn't match with your goals, be sure to set another learning goal to grow your DSML skillset.