Is AI Hard To Learn? A Guide To Getting Started in 2024

Whether it’s recommending shows on Netflix or used in the healthcare sector, artificial intelligence is slowly seeping into all avenues of our lives. In fact, the global AI market is estimated to value $190.61 billion in 2025. At the moment, the expected restriction in the industry is a lack of experienced and trained staff.

Is AI hard to learn? Is that why there’s a lack of professional staff in the field? That’s true to some extent. But that doesn’t mean getting into AI without prior education or knowledge is impossible.

In this guide, you’ll learn how to get into AI and machine learning, along with what makes the profession challenging to get into.

What Is Artificial Intelligence?

Artificial intelligence is the process of programming a computer to make decisions for itself. This could be anything from recognizing objects in pictures to responding to customer queries on behalf of a company.

Machine learning is a subset of AI that teaches computers how to learn from data. This is done by feeding the computer many examples and letting it figure out patterns by itself.

What Makes AI Hard To Learn?

Is AI hard to learn? Yes, it can be, and it’s so hard that  93% of automation technologists themselves don’t feel sufficiently prepared for upcoming challenges in the world of smart machine technologies.

Companies face many challenges when implementing artificial intelligence. Among them, the top challenge is lack of staff skills, which is an issue faced by  56% of the companies. It’s understandable why most enterprises feel this way, considering AI is inherently complex.

Some things that make it hard to learn include:

• Extensive Programming: AI requires intensive programming. You need to learn coding to program computers to make decisions for themselves.
• Data Proficiency: Machines need a lot of data to learn from to become proficient at a task. This can be difficult to obtain, especially if you’re starting out.
• Complexity: AI is complex and involves understanding a variety of fields such as computer science, statistics, calculus, and more.
• Lack of Adequate Tools: Most existing tools and processes in artificial intelligence were developed for traditional software. People entering the field often have to spend time and resources developing new tools, which can be difficult and time-consuming.

However, those statistics don’t mean that there aren’t any entry-level jobs in the field of AI and ML. There are many such job openings out there you can prepare for.

How Long Does It Take To Learn AI?

Although learning artificial intelligence is almost a never-ending process, it takes about five to six months to understand foundational concepts, such as data science, Artificial Neural Networks, TensorFlow frameworks, and NLP applications.

It may take longer to learn advanced concepts, such as deep learning, reinforcement learning, and unsupervised machine learning. Since most people learn artificial intelligence through a certification or course, the duration of the program also determines how long you’ll take to learn the skill.

How To Get Started With AI

Getting started with AI can be daunting since knowing where to begin is hard. Follow the tips below to start honing your AI skills.

Build Your Foundation

Regardless of the AI specialty you’re interested in, it’s important to have a solid foundation in the field as a whole. Some basic skills to learn include:

• Python: This is the most common language used in AI. It’s useful for simple tasks, so you don’t have to upgrade all your code when moving to more advanced applications.
• Machine Learning Basics: You should also learn the basics of Machine Learning to start taking steps towards understanding AI algorithms.
• Statistics & Probability Theory: A basic understanding of statistics and probability theory will come in handy when dealing with large data sets.
• Calculus: Calculus is used in various AI applications, so it’s important to understand the basics. You should also familiarize yourself with linear algebra. 

Other skills to learn are:

• Natural language processing.
• Solving a real-world problem.
• Debugging.
• Working with new data or data structures.

Additionally, you should understand three basic cognitive skills used in AI. These are 

1. Learning: Focuses on data acquisition and rule creation for turning data into an actionable format. 
2. Reasoning: Aids in decision-making and logical inferences. 
3. Self-correction: Handles information correction and changes to improve results.

Once you have your foundation in place, start working on a simple AI project. This will give you hands-on experience and help you learn new concepts.

Utilize Free Resources

If you’re new to artificial intelligence, you don’t necessarily have to spend hundreds of dollars upfront. Instead, you can develop useful skills through free resources, such as YouTube videos and ebooks.

You can also use online tutorials and GitHub to learn programming languages that come in handy in AI careers. Google also has a  host of free resources for learning artificial intelligence, and these can be helpful to beginners and seasoned machine learning practitioners alike. Once you think you’re ready for the next step, go ahead and take a paid course to get advanced knowledge of artificial intelligence.

Take a Course

Take a  machine learning certification or course to gain a stronger foundation in AI. This will give you the skills you need for more advanced projects and accelerate your learning process.

Many institutes offer courses in artificial intelligence, both online and offline. Make sure the course you pick covers all the bases, offers mentorship availability, and teaches you how to deal with real-world projects.

Springboard’s Machine Learning Engineering Career Track course teaches you how to build and deploy machine learning algorithms. Plus, you’ll build a professional portfolio throughout the program, which will further help you bag a job.

Another benefit of taking an online course is that you can meet like-minded people who’re on the same path as you. Most courses also have community forums or collaboration opportunities, allowing you to network with other people who’re also learning artificial intelligence.

Ask for Help

As we’ve already established, AI is hard. So you shouldn’t hesitate to ask for help when you need it. There are several online communities to tap into when you have questions about AI, such as Facebook groups and Reddit forums.

You can also ask for help offline via meetups in your local community. If there’s no meetup available in your area yet, you can always start one yourself. If you’re taking an online course, you can get help from your mentor or other people who’re also taking the same course as you.

How To Get Better at AI?

Now that you know how to get into AI and machine learning, let’s discuss how to get better in the field.

Get a Mentor

An artificial intelligence mentor can help guide you and answer your questions. They can also help you learn new things and give you feedback on your projects. If you don’t know anyone who can act as a mentor, consider finding one online.

Practice on Your Own Time

Practice artificial intelligence by working on projects in your spare time. This will help you get better at the concepts you’re learning and improve your skills.

You can attempt to write a code that solves a problem. For instance, write a code for suggesting products to consumers based on their purchase history. If you enroll in a course, you’ll likely be given real-life problems to solve through AI as practice exercises.

Participate in Competitions

Another way to improve your AI skills is by participating in competitions, such as  Kaggle competitions. These competitions usually have a data set and a problem to solve. The best part is that you can use any tool or language to solve the problem. You can also join online communities of people who compete in these competitions.

Attend Events and Presentations

Every year, several events are organized in which the current prospects and future of artificial intelligence are discussed. Some examples are:

• Re-Work Women in AI Virtual Summit
• World Summit AI: Montreal
• International Conference on Machine Learning (ICML)
• AI & Big Data Expo Global
• AI World Executive Summit: The Future of AI
• The European Conference on Computer Vision

You can find industry experts, Data Scientists and professionals at these conferences who can give you an idea of what to expect in the coming years.

About AI as a Career

Before you enter the field, it’s imperative to know whether AI is a promising career and how much you can earn every year.

What Are the Requirements To Get Into AI?

Here are some requirements to get a job in artificial intelligence:

• A college degree in computer science, mathematics, or statistics
• Proficiency in Python, Java, MATLAB, C++, or R
• Proficiency in machine learning and deep learning algorithms
• Knowledge of big data

The job requirements may also depend on the individual job description.

Is AI a Stable Career?

In 2015, only 10% of organizations reported using artificial intelligence or planned to use it in the future. By 2019, the number had risen to 37%.

Thus, it’s safe to say that AI is here to stay and will only become more popular in the years to come.

Does AI Pay Well?

Artificial intelligence professionals make an average salary of $106,823 per year in the US. Professionals working in higher positions can earn up to $177,000 per year.

Is There a Difference Between AI and Machine Learning?

The main difference between machine learning and artificial intelligence is that the former is a subset of the latter. To put it simply, AI encompasses all machine learning but not all artificial intelligence practices are associated with machine learning.

Summing up, artificial intelligence may be hard to learn, but if you have the right resources, you can make a place for yourself in the field. 

Start by building your foundation, and keep honing your skills with advanced online courses because the field of AI is ever-changing. Keeping up with the industry will ensure your competitiveness in the job market. 

Companies are no longer just collecting data. They’re seeking to use it to outpace competitors, especially with the rise of AI and advanced analytics techniques. Between organizations and these techniques are the data scientists – the experts who crunch numbers and translate them into actionable strategies. The future, it seems, belongs to those who can decipher the story hidden within the data, making the role of data scientists more important than ever.

In this article, we’ll look at 13 careers in data science, analyzing the roles and responsibilities and how to land that specific job in the best way. Whether you’re more drawn out to the creative side or interested in the strategy planning part of data architecture, there’s a niche for you. 

Is Data Science A Good Career?

Yes. Besides being a field that comes with competitive salaries, the demand for data scientists continues to increase as they have an enormous impact on their organizations. It’s an interdisciplinary field that keeps the work varied and interesting.

10 Data Science Careers To Consider

Whether you want to change careers or land your first job in the field, here are 13 of the most lucrative data science careers to consider.

Data Scientist

Data scientists represent the foundation of the data science department. At the core of their role is the ability to analyze and interpret complex digital data, such as usage statistics, sales figures, logistics, or market research – all depending on the field they operate in.

They combine their computer science, statistics, and mathematics expertise to process and model data, then interpret the outcomes to create actionable plans for companies. 

General Requirements

A data scientist’s career starts with a solid mathematical foundation, whether it’s interpreting the results of an A/B test or optimizing a marketing campaign. Data scientists should have programming expertise (primarily in Python and R) and strong data manipulation skills. 

Although a university degree is not always required beyond their on-the-job experience, data scientists need a bunch of data science courses and certifications that demonstrate their expertise and willingness to learn.

Average Salary

The average salary of a data scientist in the US is $156,363 per year.

Data Analyst

A data analyst explores the nitty-gritty of data to uncover patterns, trends, and insights that are not always immediately apparent. They collect, process, and perform statistical analysis on large datasets and translate numbers and data to inform business decisions.

A typical day in their life can involve using tools like Excel or SQL and more advanced reporting tools like Power BI or Tableau to create dashboards and reports or visualize data for stakeholders. With that in mind, they have a unique skill set that allows them to act as a bridge between an organization’s technical and business sides.

General Requirements

To become a data analyst, you should have basic programming skills and proficiency in several data analysis tools. A lot of data analysts turn to specialized courses or data science bootcamps to acquire these skills. 

For example, Coursera offers courses like Google’s Data Analytics Professional Certificate or IBM’s Data Analyst Professional Certificate, which are well-regarded in the industry. A bachelor’s degree in fields like computer science, statistics, or economics is standard, but many data analysts also come from diverse backgrounds like business, finance, or even social sciences.

Average Salary

The average base salary of a data analyst is $76,892 per year.

Business Analyst

Business analysts often have an essential role in an organization, driving change and improvement. That’s because their main role is to understand business challenges and needs and translate them into solutions through data analysis, process improvement, or resource allocation. 

A typical day as a business analyst involves conducting market analysis, assessing business processes, or developing strategies to address areas of improvement. They use a variety of tools and methodologies, like SWOT analysis, to evaluate business models and their integration with technology.

General Requirements

Business analysts often have related degrees, such as BAs in Business Administration, Computer Science, or IT. Some roles might require or favor a master’s degree, especially in more complex industries or corporate environments.

Employers also value a business analyst’s knowledge of project management principles like Agile or Scrum and the ability to think critically and make well-informed decisions.

Average Salary

A business analyst can earn an average of $84,435 per year.

Database Administrator

The role of a database administrator is multifaceted. Their responsibilities include managing an organization’s database servers and application tools. 

A DBA manages, backs up, and secures the data, making sure the database is available to all the necessary users and is performing correctly. They are also responsible for setting up user accounts and regulating access to the database. DBAs need to stay updated with the latest trends in database management and seek ways to improve database performance and capacity. As such, they collaborate closely with IT and database programmers.

General Requirements

Becoming a database administrator typically requires a solid educational foundation, such as a BA degree in data science-related fields. Nonetheless, it’s not all about the degree because real-world skills matter a lot. Aspiring database administrators should learn database languages, with SQL being the key player. They should also get their hands dirty with popular database systems like Oracle and Microsoft SQL Server. 

Average Salary

Database administrators earn an average salary of $77,391 annually.

Data Engineer

Successful data engineers construct and maintain the infrastructure that allows the data to flow seamlessly. Besides understanding data ecosystems on the day-to-day, they build and oversee the pipelines that gather data from various sources so as to make data more accessible for those who need to analyze it (e.g., data analysts).

General Requirements

Data engineering is a role that demands not just technical expertise in tools like SQL, Python, and Hadoop but also a creative problem-solving approach to tackle the complex challenges of managing massive amounts of data efficiently. 

Usually, employers look for credentials like university degrees or advanced data science courses and bootcamps.

Average Salary

Data engineers earn a whooping average salary of $125,180 per year.

Database Architect

A database architect’s main responsibility involves designing the entire blueprint of a data management system, much like an architect who sketches the plan for a building. They lay down the groundwork for an efficient and scalable data infrastructure. 

Their day-to-day work is a fascinating mix of big-picture thinking and intricate detail management. They decide how to store, consume, integrate, and manage data by different business systems.

General Requirements

If you’re aiming to excel as a database architect but don’t necessarily want to pursue a degree, you could start honing your technical skills. Become proficient in database systems like MySQL or Oracle, and learn data modeling tools like ERwin. Don’t forget programming languages – SQL, Python, or Java. 

If you want to take it one step further, pursue a credential like the Certified Data Management Professional (CDMP) or the Data Science Bootcamp by Springboard.

Average Salary

Data architecture is a very lucrative career. A database architect can earn an average of $165,383 per year.

Machine Learning Engineer

A machine learning engineer experiments with various machine learning models and algorithms, fine-tuning them for specific tasks like image recognition, natural language processing, or predictive analytics. Machine learning engineers also collaborate closely with data scientists and analysts to understand the requirements and limitations of data and translate these insights into solutions. 

General Requirements

As a rule of thumb, machine learning engineers must be proficient in programming languages like Python or Java, and be familiar with machine learning frameworks like TensorFlow or PyTorch. To successfully pursue this career, you can either choose to undergo a degree or enroll in courses and follow a self-study approach.

Average Salary

Depending heavily on the company’s size, machine learning engineers can earn between $125K and $187K per year, one of the highest-paying AI careers.

Quantitative Analyst

Qualitative analysts are essential for financial institutions, where they apply mathematical and statistical methods to analyze financial markets and assess risks. They are the brains behind complex models that predict market trends, evaluate investment strategies, and assist in making informed financial decisions. 

They often deal with derivatives pricing, algorithmic trading, and risk management strategies, requiring a deep understanding of both finance and mathematics.

General Requirements

This data science role demands strong analytical skills, proficiency in mathematics and statistics, and a good grasp of financial theory. It always helps if you come from a finance-related background. 

Average Salary

A quantitative analyst earns an average of $173,307 per year.

Data Mining Specialist

A data mining specialist uses their statistics and machine learning expertise to reveal patterns and insights that can solve problems. They swift through huge amounts of data, applying algorithms and data mining techniques to identify correlations and anomalies. In addition to these, data mining specialists are also essential for organizations to predict future trends and behaviors.

General Requirements

If you want to land a career in data mining, you should possess a degree or have a solid background in computer science, statistics, or a related field. 

Average Salary

Data mining specialists earn $109,023 per year.

Data Visualisation Engineer

Data visualisation engineers specialize in transforming data into visually appealing graphical representations, much like a data storyteller. A big part of their day involves working with data analysts and business teams to understand the data’s context. 

General Requirements

Data visualization engineers need a strong foundation in data analysis and be proficient in programming languages often used in data visualization, such as JavaScript, Python, or R. A valuable addition to their already-existing experience is a bit of expertise in design principles to allow them to create visualizations.

Average Salary

The average annual pay of a data visualization engineer is $103,031.

Resources To Find Data Science Jobs

The key to finding a good data science job is knowing where to look without procrastinating. To make sure you leverage the right platforms, read on.

Job Boards

When hunting for data science jobs, both niche job boards and general ones can be treasure troves of opportunity. 

Niche boards are created specifically for data science and related fields, offering listings that cut through the noise of broader job markets. Meanwhile, general job boards can have hidden gems and opportunities.

Online Communities

Spend time on platforms like Slack, Discord, GitHub, or IndieHackers, as they are a space to share knowledge, collaborate on projects, and find job openings posted by community members.

Network And LinkedIn

Don’t forget about socials like LinkedIn or Twitter. The LinkedIn Jobs section, in particular, is a useful resource, offering a wide range of opportunities and the ability to directly reach out to hiring managers or apply for positions. Just make sure not to apply through the “Easy Apply” options, as you’ll be competing with thousands of applicants who bring nothing unique to the table.

FAQs about Data Science Careers

We answer your most frequently asked questions.

Do I Need A Degree For Data Science?

A degree is not a set-in-stone requirement to become a data scientist. It’s true many data scientists hold a BA’s or MA’s degree, but these just provide foundational knowledge. It’s up to you to pursue further education through courses or bootcamps or work on projects that enhance your expertise. What matters most is your ability to demonstrate proficiency in data science concepts and tools.

Does Data Science Need Coding?

Yes. Coding is essential for data manipulation and analysis, especially knowledge of programming languages like Python and R.

Is Data Science A Lot Of Math?

It depends on the career you want to pursue. Data science involves quite a lot of math, particularly in areas like statistics, probability, and linear algebra.

What Skills Do You Need To Land an Entry-Level Data Science Position?

To land an entry-level job in data science, you should be proficient in several areas. As mentioned above, knowledge of programming languages is essential, and you should also have a good understanding of statistical analysis and machine learning. Soft skills are equally valuable, so make sure you’re acing problem-solving, critical thinking, and effective communication.

Since you’re here…Are you interested in this career track? Investigate with our free guide to what a data professional actually does. When you’re ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp which will help you land a job or your tuition back!