Math for Data Science
Math for Data Science is an advanced online course that teaches linear algebra, probability, and statistical inference for data science. Priced variably, it best serves learners who have completed introductory data-science courses and need rigorous mathematical foundations for modeling and analysis.
● In stock
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Key features
- Linear-algebra foundation – systems, matrices, vector spaces.
- Probability fundamentals – axioms, counting, Bayes’ theorem.
- Random-variable mastery – PMFs, expectations, joint distributions.
- Continuous-distribution work – multivariate probability calculations.
- Statistical inference practice – hypothesis testing, confidence intervals.
- Self-paced format – video lessons, quizzes, downloadable resources.
Pros
- +Integrates linear algebra and probability in one course.
- +Self-paced with video, quizzes, and downloadable worksheets.
- +Directly applicable to real-world data-science projects.
Cons
- −Assumes completion of earlier Alison data-science courses.
- −Variable pricing may lack a fixed discount.
About Math for Data Science
What is Math for Data Science?
Math for Data Science is the third installment in Alison’s data-science learning pathway. The course opens with a focused review of linear algebra, asking “what makes equations linear?” and guiding students through systems of linear equations, matrix-solve techniques, and the construction of vector spaces. After establishing a solid algebraic base, the curriculum shifts to probability theory, covering axioms, discrete counting, Bayes’ theorem, random variables, probability mass functions, and joint distributions. The final modules explore continuous variables, multivariate probability calculations, and practical statistical inference, enabling learners to translate mathematical results into actionable data-science insights. Designed for self-paced online delivery, the material blends concise video lectures with interactive quizzes and downloadable worksheets. By the end of the program, students can confidently construct and solve linear models, compute probabilities for complex events, and apply inference techniques to real-world datasets.
Key features
- Linear-algebra foundation — systems, matrices, and vector spaces.
- Probability fundamentals — axioms, counting, Bayes’ theorem.
- Random-variable mastery — PMFs, expectations, joint distributions.
- Continuous-distribution work — multivariate probability calculations.
- Statistical inference practice — hypothesis testing and confidence intervals.
- Self-paced format — video, quizzes, and downloadable resources.
Who is Math for Data Science for?
This course targets learners who have completed introductory data-science modules such as “Data Science Masterclass for Beginners” and “Python for Data Science: From the Basics to Advanced.” It is ideal for aspiring data analysts, junior machine-learning engineers, and professionals seeking to solidify the mathematical underpinnings of predictive modeling. The material assumes comfort with basic programming and elementary statistics, but no prior linear-algebra or probability coursework is required.
How does Math for Data Science compare?
Compared with separate linear-algebra or probability MOOCs, this course merges both disciplines, mirroring the dual-skill requirement of modern data-science roles. It offers a cohesive learning path rather than fragmented modules, though specialized deep-dive courses may provide more depth in a single topic.
Best use cases
- →Designing regression models that require matrix inversion.
- →Building Bayesian classifiers for categorical datasets.
- →Conducting hypothesis testing on A/B experiment results.
- →Analyzing multivariate probability distributions in finance.
Is Math for Data Science right for you?
Buy Math for Data Science if you have finished introductory data-science tracks and need a rigorous mathematical toolkit. The course suits early-career analysts, junior ML engineers, and business professionals transitioning into analytics. Its modular format lets you study at your own pace, while the integrated quizzes reinforce concepts. If you prefer a single-subject focus, consider standalone linear-algebra or probability courses, but this combined offering saves time and aligns directly with data-science workflows.
How it compares: Compared with separate linear-algebra or probability MOOCs, this course merges both disciplines, mirroring the dual-skill requirement of modern data-science roles. It offers a cohesive learning path rather than fragmented modules, though specialized deep-dive courses may provide more depth in a single topic.
More from Alison
Frequently Asked Questions
What topics does Math for Data Science cover?
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The course starts with linear-algebra basics, teaching how to identify linear equations, solve systems, and work with matrices and vector spaces. It then moves to probability, covering axioms, counting techniques, Bayes’ theorem, random variables, probability mass functions, expectations, joint and continuous distributions, and finishes with statistical inference methods such as hypothesis testing and confidence intervals.
Does the course require prior programming knowledge?
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A basic familiarity with Python or another programming language is helpful but not mandatory. The curriculum focuses on mathematical concepts and provides illustrative code snippets, so learners who have completed Alison’s introductory data-science courses or have a modest coding background will find the material accessible. Complete beginners may need to supplement with a short programming primer.
How are assessments structured?
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Each module ends with a set of multiple-choice quizzes and practical exercises that test both conceptual understanding and problem-solving ability. Quizzes are automatically graded, providing instant feedback, while the exercises often require you to compute solutions manually or write short code snippets. Completion of all assessments awards a certificate of achievement.
Is the content suitable for professional certification?
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While the course itself does not grant a formal industry certification, the curriculum aligns with the mathematical competencies required for many data-science credentials such as the Certified Data Scientist exam. Completing the program provides documented proof of proficiency in linear algebra and probability, which can be cited on resumes or LinkedIn profiles.
Can I audit the course for free?
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Alison offers a free audit option that grants access to video lectures and reading materials, but graded quizzes and the certificate of completion require a paid enrollment. Auditing lets you explore the curriculum at no cost, though you won’t receive the full assessment feedback or official recognition without purchasing the course.
Is Math for Data Science in stock at Alison?
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Yes, Math for Data Science is currently in stock at Alison.
Specifications
- Category
- Software
- SKU
- 4246