Maths for Data Science

Mathematics is exceedingly useful for developing intelligent systems that can take decisions autonomously. For a lot of higher level courses in ML/DL/DS/AI, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it's used in Computer Science.

ML/DL/DS/AI is about developing models that can automatically extract important information and patterns from data. But here, an important question arises: what is the magic behind THEM?, and the answer is Mathematics. Mathematics is the core of designing ML/DL/DS/AI algorithms that can automatically learn from data and make predictions. Therefore, it is very important to understand the Maths before going into the deep understanding of ML algorithms.

Statistics and Probability form the core of Data Analytics. They are widely used in the field of machine learning to analyze, visualize, interpret data and discover insights. To understand how each algorithm works, you need to know linear algebra. You might have to revisit high-school mathematics for anything to do with Calculus. Machine learning uses the concepts of calculus to formulate the functions that are used to train algorithms. Learning mathematics in Data Science is not about solving a maths problem, rather understanding the application of maths in DS algorithms and their working. During the training, we shall expose the students to Python/R

This specialization aims to bridge the gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science. At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Target Audience:-
-Aspiring data scientists and analysts looking to strengthen their mathematical foundations
-Professionals transitioning into data science roles who need to refresh core math skills
-Students in computer science, economics, engineering, or statistics preparing for data-centric careers
-Machine learning enthusiasts seeking deeper understanding of algorithm mechanics
-Researchers and academics who want to apply mathematical rigor to data-driven studies

Learning Outcomes:-
-Understand and apply linear algebra concepts
-Use calculus fundamentals
-Apply probability theory
-Perform statistical analysis
-Grasp the mathematical intuition behind algorithms
-Translate mathematical concepts into code
-Evaluate model performance
-Build a strong mathematical foundation

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas