Master the essential mathematical foundations essential for careers in engineering, data science and artificial intelligence. The Mathematics for Engineering specialisation builds systematic competencies in calculus, probability, linear algebra, discrete mathematics, and formal logic, preparing you to solve complex technical problems and develop cutting-edge algorithms. Whether you're beginning your quantitative journey or strengthening existing skills, this specialisation provides the essential mathematical literacy required for advanced applications in engineering, data science, algorithm design, and artificial intelligence across various technical domains.

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Mathematics for Engineering Specialization
Mathematical Foundations for an Engineering Career. Master core mathematical skills essential for advanced careers in technical domains.

Instructor: BITS Pilani Instructors Group
Included with
Recommended experience
Recommended experience
What you'll learn
Master calculus, trigonometry, matrices, differential equations, and algebraic techniques essential for engineering and data science.
Apply probability theory, statistical distributions, hypothesis testing, and confidence intervals to analyze data and validate findings.
Develop logical thinking through mathematical proofs, discrete structures, graph theory, and formal verification techniques.
Solve optimization problems using linear algebra, eigenvalue analysis, and mathematical modeling for machine learning applications.
Overview
Skills you'll gain
- Engineering Calculations
- Computational Logic
- Deductive Reasoning
- Bayesian Statistics
- Artificial Intelligence and Machine Learning (AI/ML)
- Descriptive Analytics
- Engineering Analysis
- Calculus
- Logical Reasoning
- Analytics
- Differential Equations
- Statistical Modeling
- Data Analysis
- Linear Algebra
- Statistical Analysis
- Programming Principles
- Statistical Inference
- Probability & Statistics
- Theoretical Computer Science
- Trigonometry
What’s included

Add to your LinkedIn profile
August 2025
Advance your subject-matter expertise
- Learn in-demand skills from university and industry experts
- Master a subject or tool with hands-on projects
- Develop a deep understanding of key concepts
- Earn a career certificate from Birla Institute of Technology & Science, Pilani

Specialization - 5 course series
What you'll learn
Integrate trigonometric concepts to analyse and solve complex real-world engineering problems.
Critically evaluate and solve intricate systems of linear equations using matrix operations.
Synthesise the principles of differentiation and integration to develop and optimise engineering models.
Formulate and solve first-order and first-degree differential equations to effectively model a variety of engineering processes.
Skills you'll gain
What you'll learn
Evaluate and interpret complex data sets with probabilistic models, applying Bayes’ theorem and Chebyshev’s inequality to solve real-world problems.
Design hypothesis tests, including t-tests, z-tests, and chi-square tests, to validate data-driven hypotheses in various professional contexts.
Construct and optimise predictive models using multiple and nonlinear regression techniques to forecast outcomes and improve decision-making.
Synthesise probability and statistical knowledge to develop innovative solutions for complex analytical challenges.
Skills you'll gain
What you'll learn
Analyse and assess complex problems by applying set theory and functions, ensuring accurate and efficient solutions are developed.
Design and evaluate graph-based models to optimise algorithms and enhance network analysis in cryptography and database management contexts.
Critique mathematical proofs and reasoning to enhance problem-solving skills in varied scenarios.
Innovate discrete structures to efficiently solve problems in data structures, operating systems, and computation theory.
Skills you'll gain
What you'll learn
Analyse and evaluate complex data structures using advanced linear algebra techniques.
Implement sophisticated algorithms and apply advanced techniques to optimise and improve machine learning models.
Synthesise and apply mathematical theories to solve complex real-world problems.
Evaluate and develop innovative solutions using linear programming to address complex challenges in machine learning and AI systems.
Skills you'll gain
What you'll learn
Analyse computational problems to identify appropriate proof techniques and logical reasoning methods that best address their complexities.
Design comprehensive solutions to algorithm development challenges by synthesising and applying principles of propositional and predicate logic.
Evaluate system reliability by conducting model checking using temporal logics, and interpret the results to ensure system correctness.
Construct formal verification plans for algorithms and programs using Floyd-Hoare logics and justify their correctness through logical reasoning.
Skills you'll gain
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Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Instructor

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Frequently asked questions
This Specialization provides a comprehensive mathematical foundation essential for careers in engineering, data science, and artificial intelligence. It's important because strong mathematical skills are consistently cited as critical requirements for technical roles, and mastering these five mathematical domains will significantly enhance your problem-solving capabilities and career prospects.
This Specialization is designed for undergraduate and engineering students, computer science majors, data scientists, AI developers, and professionals seeking to strengthen their mathematical foundation. It's suitable for those with basic mathematical knowledge looking to advance their quantitative skills for technical applications.
Upon completing the Specialization, you will have developed a comprehensive mathematical foundation critical to solve complex engineering problems, design efficient algorithms, build and validate mathematical models, perform statistical analyses, implement machine learning techniques, and apply formal verification methods—skills highly valued across engineering, technology, and data science industries.
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Financial aid available,