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Mathematical Foundations
Enrollment is Closed
Course
Mathematical Foundations
Master the core math behind AI and data science, including probability, linear algebra, regression, and optimization. Apply these concepts instantly to solve real-world problems with confidence.
Online Live Classes
Beginner Level
Mentor Support
The Mathematical Foundations course is designed to equip learners with the core math skills essential for success in data science, artificial intelligence, and modern analytics. Mathematics is the language of data, and this course simplifies complex concepts into clear, practical lessons that anyone can grasp.
In this course, youll begin by strengthening your understanding of numbers, algebra, equations, and functions before diving into probability, statistics, and Bayes theorem to interpret uncertainty and variability in data. You'll then explore linear algebravectors, matrices, and real-world applications such as representing images and feature sets. You'll also learn about regression and correlation to connect math with predictive modeling. Plus, you'll gain intuitive exposure to calculus concepts like derivatives, gradients, and gradient descent, demystifying how machine learning models learn.
Overall, this course balances theory and practical application. Through hands-on practice in Python or Excel, youll visualize regression, compute correlations, and see optimization in action.
By the end, youll not only understand the mathematics powering AI and data science but also feel confident applying it in real-world problem solving.
This course comprises 6 purposely designed modules that take you on a carefully defined learning journey.
Our proven learning methodology blends the best that instructor-led training and self-paced learning have to offer. Leveraging the power of instructor feedback, mentor-supported hands-on practice, and additional home-based studying, you will build the deep technical and practical understanding todays employers are looking for.
Additionally, you will enjoy learning via an interactive online classroom environment where you will be able to participate and actively engage with your peers, instructors, and mentors. Plus, you will get the opportunity to earn recognized certifications which will help your resume and LinkedIn profile stand out.
The materials for each module are accessible from the start of the course and will remain available for the duration of your enrollment. Methods of learning and assessment will include reading material, hands-on labs, and online exam questions.
As part of our mentoring service you will have access to valuable guidance and support throughout the course. We provide a dedicated discussion space where you can ask questions, chat with your peers, and resolve issues.
Once you have successfully completed the course, you will earn your Certificate of Completion.
By the end of this course, you will be able to:
- Apply probability and statistics to interpret uncertainty, trends, and variability in data.
- Work with linear algebra concepts (vectors, matrices, operations) to structure and manipulate data.
- Use regression and correlation to model relationships and make predictions.
- Understand derivatives, gradients, and gradient descent for optimization in machine learning.
- Connect mathematical theory to practical applications in AI and data science through hands-on practice.
- IT professionals and software developers transitioning into data science, machine learning, or AI.
- Career switchers from non-technical backgrounds who want to bridge the gap with math essentials.
- Analytics or business professionals who need mathematical fluency to interpret data trends and work with technical teams.
- No previous experience required.
- Basic familiarity with high-school level math.
Course Outline
Numbers, algebra, equations
Functions (linear vs. non-linear)
Basic notation used in AI/DS
Graded Quiz
Probability rules & conditional probability
Bayes’ theorem (intuitive)Mean, median, variance, standard deviation
Normal distribution
Graded Quiz
Vectors & matrices
Matrix operations (addition, multiplication)
Real-world examples (images as matrices, feature sets)
Graded Quiz
Correlation vs. causation
Simple linear regression
Interpretation of slope & intercept
Graded Quiz
Concept of derivatives & gradients
Intuition of slope and minima
Gradient descent in plain terms
Graded Quiz
Hands-on examples in Python/Excel, plot regression line
Compute correlation
See gradient descent visualized
Graded Quiz
Final Quiz
Why Learn with SkillUp Online?
We believe every learner is an individual and every course is an opportunity to build job-ready skills. Through our human-centered approach to learning, we will empower you to fulfil your professional and personal goals and enjoy career success.
Reskilling into tech? We’ll support you.
Upskilling for promotion? We’ll help you.
Cross-skilling for your career? We’ll guide you.
Personalized Mentoring & Support
1-on-1 mentoring, live classes, webinars, weekly feedback, peer discussion, and much more.
Practical Experience
Hands-on labs and projects tackling real-world challenges. Great for your resumé and LinkedIn profile.
Best-in-Class Course Content
Designed by the industry for the industry so you can build job-ready skills.
Job-Ready Skills Focus
Competency building and global certifications employers are actively looking for.
FAQs
This foundation of mathematics course builds the essential math skills needed for data science, AI, and analytics. It breaks down complex concepts like probability, linear algebra, and regression into clear, hands-on lessons using Python or Excel. By the end, youll understand how these mathematical ideas power real-world AI and data models.
Not at all. The foundation of mathematics course starts from the basics, covering algebra and probability before moving into topics like linear algebra and calculus for optimisation. Its ideal for beginners or career switchers who want to strengthen their math foundation for data-driven roles.
The course is instructor-led and includes live sessions, recordings, practice exercises, quizzes, and applied mini-projects. Youll get plenty of guided support and opportunities to practice what you learn using real data examples.
Youll work with Python or Excel to visualise mathematical concepts like regression and optimisation. These tools help you connect theory to practiceespecially useful if you plan to learn machine learning with Python later.
This course is perfect for IT professionals, business analysts, and anyone exploring AI or data science careers. Its also great for non-technical learners who want to build their mathematical foundation for data science.
Build the confidence to tackle data science and AI challengesenrol in the Mathematical Foundations course today and start learning from expert instructors.
This course is also part of the Certificate Programme in Data Science and AI/ML by IIT Jammu and SkillUp Online. If youre planning to build a career in data science or AI, then we recommend taking the full program for a holistic learning journey.
Absolutely. The mathematical foundation for data science course prepares you to progress into more advanced topics such as predictive analytics, neural networks, and deep learning. It forms the mathematical backbone of courses in data science, AI, and ML.
Yes. You can choose to complete exercises using Python or Excel. This flexibility makes this course accessible to both coding and non-coding learners.
Yes. Upon successful completion, youll receive a Certificate of Completion that showcases your applied understanding of core mathematical concepts for AI, data science, and analytics.
Type of certificate
Certificate of Completion
About this course
06 Modules
06 Skills
Includes
Discussion Space
Hands-On Labs
Learning Material
06 Knowledge Checks
01 Final Quiz
Create
Statistical Analysis
Linear Algebra Project
Regression Modeling
Optimization with Gradient Descent
Exercises to explore
Probability and statistics practice problems
Vector and matrix operations with examples
Simple linear regression and correlation exercise
Gradient descent visualization activity
Applied math mini-project in Python/Excel
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