When it comes to data science and artificial intelligence (AI), you’ll often find a lot of intersections between the two skill paths. AI has numerous subsets, like machine learning and deep learning, and data science utilizes these technologies to interpret and analyze data, discover patterns, make predictions, and generate insights. So, deciding between AI and data science can be tricky.
On the other hand, technologies like ML depend on robust data science practices to ensure clean, high-quality, and relevant data is training the ML algorithms and systems. Not to mention that data science is an interdisciplinary field that often incorporates the knowledge of AI and ML, and many AI careers, like an AI engineer, require data scientist skills.
So it’s easy to start wondering—where to begin? This is a particularly pressing question for those who understand that the demand for data science and AI skills is skyrocketing and want to get on board.
There’s no right or wrong answer or fundamental hierarchy. However key differences in knowledge and skills required for certain job roles will ultimately shape your journey to proficiency and your career projection.
Leading AI vs Data Science Jobs and Skill Requirements
A good place to start is deciding on the type of career that best suits you, and then trying to fulfill the corresponding needs and skillsets of that particular career path. Also, check the qualifications that a business requires for specific positions, such as the type of degree or “soft” skills such as being a great communicator.
If you are unsure of the career path you want to take—AI vs. data science—work backward, looking at the corresponding skillsets required under each path, and then decide on the career choice that best meets the skills you are best suited for or are interested in developing.
The following are three of the most sought-after job positions in the fields of AI vs. data science. By understanding the requirements of the job, and the necessary qualifications that organizations require, you can pinpoint exactly where your skills and interests fall in these particular areas to better approach your unique career path.
Machine Learning Engineer
Requires a strong understanding of programming languages, mathematics, analytical skills, data sets, and an understanding of development tools. Also, most organizations want you to have a Master’s or doctorate in computer science or mathematics.
Data Scientist
An understanding of statistical analysis, big data platforms like Hadoop, and programming languages, as well as strong communication, analytical skills, and business knowledge, is critical.
Business Intelligence Developer
Great communication and problem-solving skills, the ability to analyze complex data sets to identify market trends, knowledge of BI technologies, and certifications in data science is recommended.
Other popular career choices in these fields include research scientist, robotic scientist, data analyst, business analyst, and data architect. Research the respective job requirements and qualifications to discover the career path that’s best for you.
Considering the Stops Along the Road
When deciding between AI vs data science skills, first, you’ll need to consider what your career goals are and whether you’re looking to expand your competencies, improve your existing knowledge base, or help your business move into a new direction through digital transformation.
For those who aren’t sure where to start, both data science and AI have a similar skill and knowledge baseline: statistics, mathematics, and programming. These are solid foundational learning paths that leave the door open for either data science or an AI-focused career path.
Then start to explore some different AI and Machine Learning courses. If this interests you, start looking into more AI-related courses like programming languages, coding, data modeling, algorithms, or visualization.
If you’re finding yourself more interested in the analytics and business side of learning, then a data science educational path is your starting point. Begin shaping your skills in data mining or wrangling, data modeling, database management, and programming languages, like Python and R.
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