Are you curious about artificial intelligence (AI) and machine learning? If so, consider pursuing a career in data science. It’s common to assume that a profession with “science” mentioned in the title will require technical expertise. But how much of those technical skills are based on mathematics?
Do you need to be a mathematics wiz to work in AI and machine learning?
If you’ve done your homework and looked through AI and machine learning curriculums then you know there’s always a maths component. However, the depth of maths knowledge needed for the field of data science depends on your career path.
In this article, we’ll clarify how mathematics is applied to AI and machine learning and how you can stay ahead in the field of data science.
The difference between maths and statistics, and how they relate to AI and machine learning in data science.
Maths and statistics are fundamental to data science and analytics. They do differ, despite many definitions linking them together.
Mathematics has a concrete structure that follows a set of arguments to deliver one final solution. It is a numerical method of expressing ideas. In essence, mathematics is really about searching for the truth using logic. There are no uncertain results.
Statistics uses maths abstractly because it deals with uncertainty. The results from statistical models are always open-ended- the solution is never exact.
Statistical knowledge enhances judgement skills. When analysing data, it helps to know which questions to ask. Data scientists ask questions to inform learnings, and statistics provide the perfect tools to forecast outcomes when working with insufficient data.
Both concepts help inform AI and machine learning to deliver solutions.
How are maths and statistics used in AI and machine learning?
The simplest way to define AI and machine learning is from experience. Machine learning goes a step further and applies algorithms to its experience – the data it encounters – to make its own decisions. Businesses use repeatable predictions to prevent and troubleshoot problems in their network.
The applications of mathematics and statistics underpin machine coding. In developing a conceptual understanding of them, you will be able to produce high-quality work in AI and machine learning.
The applications of maths and statistics in data science
Maths and statistics play a central role in data science, AI and machine learning. A data scientist who cannot grasp maths is similar to a musician who cannot play a musical instrument. You can only go so far with limited skills. Here are some fundamental maths functions that we must consider:
- Calculus – lowers the error of AI and machine learning predictions
- Linear algebra – helps interpret the data collected
- Mathematical models and algorithms – equations and functions predict potential data and decide how to make the best use of the data
- Optimisation – formulates the best outcome or performance
- Probability – continues developing AI’s ability to make decisions
- Statistics – underpins machine learning
Entry-level data science roles don’t require as much maths expertise. But as you work your way up, the more demanding tasks require an understanding of essential concepts. Your career prospects will increase with greater maths skills. The more experience and exposure you have, the more confident you will be to make assessments.
How can I learn the maths needed for a career in data science?
A good way to learn the maths needed for data science is to break down subjects into topics and study the essential concepts. A conceptual understanding of mathematics is what you will be applying in data science. Your determination to study maths is what will help you to learn.
Completing an industry-level course that teaches you the practical maths required for a career change to data science will be beneficial. This will relieve the stress associated with the pressure of self-learning.
Upskill to a career in data science with your existing maths skills
A number of professionals already have the maths required to upskill in data science, AI and machine learning.
Accountants have the mathematics and statistics skills required for a career change to data science. Their skills in logic and problem-solving will definitely be helpful in understanding the underlying concepts of AI and machine learning.
Data science is transforming the insurance industry and how actuaries predict financial risks. Actuaries collate the raw statistical data to present quantitative data.
Similar to actuaries, insurance underwriters have the level of maths required to upskill quickly to a career in data science. Underwriters use maths constantly in their work to generate accurate rates for risk, manage the capacity levels and risk-loss ratios of individual risk.
Financial analysts solve problems with tools used in data science, such as mathematical models. They have many transferrable skills for understanding the essentials of AI and machine learning.
Statistics is a fundamental component of AI and machine learning. Statisticians can easily upskill with their existing knowledge. Also, data scientists work with statistics to optimise the performance of machines to interpret data.
Daily data tasks that require computational mathematics in an AI and machine learning role
Computational maths, the practical application of maths to computer science, is applied to daily tasks that involve codifying maths into computers to address business needs. A conceptual understanding of maths is essential to manage and draw conclusions from messy datasets and algorithms.
For example, a weather forecast combines mathematics, statistical models, and data collected by meteorologists. Data science roles draw from a knowledge of statistics for causal inference.
Algebra and calculus can be seen in data visualisation, AI and machine learning. With training in computational maths, you will be able to conduct computer algorithms, cryptography, programming languages and software development.
Industry-standard tools that a data scientist uses for computational maths-related tasks
Data science curricula begin with a maths and programming course. Maths is intertwined with programming because the algorithms are the instructions given when you are coding.
Programming languages are usually created with mathematical notation. When coding in Python, maths is used to instruct computers to solve advanced problems. Python can be used in combination with computational maths to transform and expose insights in massive datasets.
Statistics is also one of the most important tools when developing AI and machine learning. Data scientists analyse statistical models with Python when performing risk-management tasks.
Computational mathematics is a requirement for data science and analytics
You do not need to be a mathematician or statistician to develop AI, machine learning, or become a data scientist. But you do need to have computational math skills and the confidence to apply them. If you have the desire to be a data scientist and your math skills need an upgrade, there are many courses available to improve your skills.
Build your confidence up with a career objective in mind and you will work your way through one of the many career paths to become a data scientist. The future outlook is promising because every sector works with data. Data science employment prospects are well paid so the sky is the limit… in an idiomatic, non-computational way, of course.
Take the next step to upskill your qualifications and implement data-driven solutions. Learn more about AI and machine learning to stand out in the job market. Click here to learn more about becoming an industry-certified data professional or schedule a call here.