One of the industries that AI has reshaped is data science and analytics. Generative AI platforms are capable of analyzing data much faster than humans, and professionals who want to stay current must stack AI skills on top of formal education.
In this post, I will provide you with a roadmap and resources for starting a career in data analytics in the AI era.
What Is An AI Data Analyst?
AI Data Analysts use traditional data analysis techniques and tools combined with artificial intelligence (AI) and machine learning (ML) models to collect, refine, and interpret data sets to answer specific questions and help organizations make data-driven decisions.
The demand for AI data analysts is projected to grow 36 percent in the next decade, much faster than the average for all occupations.
Data analysts with an AI skillset can work for companies in various industries, such as tech, finance, marketing, and healthcare. They earn an average annual salary of $131K.
Do I Need a Degree?
You don’t necessarily need a degree to become an AI data analyst. While formal education can be helpful as proof of your ability to work with data, it’s not a strict requirement.
It really depends on the job. Many employers do prefer candidates with a degree, but in some cases, practical experience and proven skills are even more important.
If you don’t have a degree, a good alternative is earning a professional certification and focusing on building a solid portfolio highlighting your abilities. You may also need hands-on experience in entry-level positions to demonstrate your expertise.
Roadmap For Becoming An AI Data Analyst
- Start by Learning the Fundamentals
- Build your Technical Skills
- Build Your AI Skills
- Work on Projects And Build Your Portfolio
- Apply for Entry-Level Jobs
1. Start by Learning the Fundamentals
The first step in launching your new career is learning the fundamentals of data analysis. Even if this is basic stuff that AI can easily do, you still need to know how to do it manually.
AI will not replace your job but help you carry out manual tasks faster and more efficiently.
Learning the fundamentals is also a good way to evaluate whether becoming a data analyst is the right choice. Many people are attracted to this field because of the high salaries and job opportunities, only to realize that dealing with numbers and large amounts of data is not what they want.
I recommend spending time playing around with tools and free resources.
For example, Kaggle is an open-source platform built to help beginners understand the basics of data science.
EdX and IBM have collaborated on a free course called “Data Analytics for Everyone.” This course explains what data analytics is and the key steps involved in the process.
There is also this free course on Coursera, which introduces the basics of data analysis and tools you can use.
Once you've completed the above, you'll have a good understanding of the basics and a clear idea of whether this is a job you want to explore further. If you've got the green light, the next step is to work on your technical data analytics skills.
2. Build your Technical Skills
Data analyst roles usually require specific knowledge of a particular programming language or data analytics tools.
To increase your chances of applying for various jobs, your technical skills should include things like:
- Statistical analysis
- SQL (Structured Query language)
- Data visualization
- R or Python programming
- Data cleaning and preparation
- Data visualization
Learning different programming languages and technical skills in depth can require much time and investment.
To accelerate your path to your data analyst career, look at some job listings for roles relevant to your interests. Use those listings to decide what kind of tools and languages you most need to master.
Remember, many employers will seek a combination of technical and relevant soft skills, such as problem-solving capabilities and excellent communication skills. Work on strengthening your soft skills just as much as your technical knowledge.
A good course (that comes with a recognized certificate) is the IBM Data Analyst Professional certificate. It includes 11 courses to help you build your technical skills from the ground up. It's suitable for beginners and requires no prior experience or related degree.
3. Build Your AI Skills
The next step is to build your AI skills. You should learn how Generative AI tools like ChatGPT, Gemini, and Claude work and, most importantly, how to use them to carry out data analytics tasks.
These skills will set you apart from other candidates who don't have this knowledge.
In particular, you should learn how to:
- Use AI tools to automate data cleaning, transformation, and preparation tasks.
- Leverage machine learning models for predictive analytics and anomaly detection.
- Apply natural language processing (NLP) for sentiment analysis, text classification, and extracting insights from unstructured data.
- Use AI for data visualization, such as generating dynamic dashboards or visual reports.
- Integrate AI-powered tools into your workflow to streamline repetitive tasks like data entry, tagging, or sorting large datasets.
- Fine-tune and customize AI models to suit specific data requirements and improve analysis accuracy.
It may sound like a lot, but the good news is that AI tools excel at data analysis, and in most cases, your job will be to provide them with the data and the right prompts.
One of the most efficient ways to build your analytics skills with AI is the Google Data Analytics Professional Certificate. It's one of the most popular programs for becoming a certified data analyst and includes dedicated lessons on AI.
In particular, it includes practical lessons on how to:
- Boost your data analytics skills with AI
- Clean and prepare data with help from AI
- Organize data and build formulas using AI
- Use AI to help you ask more effective questions
- Create engaging data visualizations with AI
- Improve your R code with the help of AI
It's a comprehensive course that takes less than six months to complete and can help you secure an entry-level position (as a Data Analyst, Junior Data Analyst, or Associate Data Analyst) through Google’s Employer Consortium.
For more details, read our review (it includes sample exam questions and passing tips).
4. Work on Projects And Build Your Portfolio
Employers will first ask for your portfolio when you apply for work. While a degree or professional certificate is great, you must demonstrate practical skills for the job.
If you're applying for entry-level jobs, it is normal not to have an extensive portfolio. In such cases, you should include any projects completed during your education and any data analysis work you did independently.
Various free data sets are available online, including climate data from the National Centers for Environmental Information and NASA open data, which offer excellent practice opportunities for building demo projects.
To make your portfolio more useful to employers, you should show off the relevant skills to the jobs you want to apply for. For instance, you may want to demonstrate your Python programming skills or display your knowledge of the R language.
In general, a data analyst portfolio is expected to have projects related to:
- Scraping and curating data from various sources.
- Cleaning and optimizing different kinds of data.
- Visualize findings through graphs, maps, charts, and more.
- Transforming data into actionable insights.
5. Apply for Entry-Level Jobs
Although relevant education and a good portfolio will improve your chances of getting the right job, most employers will also prioritize experience. The best way to discover what a job as a data analyst is really like is through entry-level roles.
The related job titles to look for are:
- Junior Data Analyst
- Data Scientist AI
- Business Analyst
- Research Analyst
- Data Associates
According to Glassdoor, Junior Data analysts make $69K to $107K annually in the US.
Is AI Data Analyst a Good Career?
AI data analysts are in high demand in today’s AI-transforming landscape, and their role is listed as one of the fastest-growing jobs in the US. According to The Future of Jobs Report, data analyst roles are expected to grow at around 36% by 2030, meaning there shouldn’t be a shortage of job opportunities for these experts.
AI Data analysts can contribute to business decision-making, which can lead to opportunities for promotion to high-level managerial positions. A career as a data analyst can also open the door to other employment opportunities. Some professionals who start as data analysts go on to work as chief data scientists, who can use advanced techniques to build models and tools capable of predicting future trends in a business.
Some other benefits of a data analyst career include:
- Flexibility in working remotely
- Very good starting salary, which can increase over time
- Opportunities to work both as an individual and part of a team
