COHORT 2 : "Anybody Can Do Math – A Primer for Quantitative Finance, Data Science & Machine Learning

This Course is designed for individuals looking to break into Quantitative Finance, Data Science, and Machine Learning, regardless of their academic background. If you're from a Non-STEM field and want to build a strong foundation in the essential mathematical concepts used in finance, this session is for you. It is particularly beneficial for professionals and students pursuing CFA, FRM, CQF, and CAIA who wish to strengthen their quantitative skills for trading, risk management, and financial modeling. Whether you're a finance enthusiast, an investment professional, or someone transitioning into quantitative roles, this primer will help you get started with confidence.

Course Start Date - 2nd August 2025

Course End date - 28th September 2025

Classes will be conducted on Weekends - Saturday and Sunday

Total Hours - 50 hours

Accessible via our custom Web + Mobile App with lifetime access to:

Recorded lectures

Annotated notes

Course Syllabus

Elementary Mathematics:

Topics such as inequalities, polynomials, basic statistics (mean, median, mode, variance, etc.), exponents and logarithms, sequences and series, and an introduction to matrices and determinants.

Pre-Calculus:

Focuses on set theory, relations and functions, complex numbers, and limits.

Calculus:

Covers differentiation (including rules and applications), partial differentiation, continuity and differentiability, series expansions (Maclaurin and Taylor series), integration techniques (definite and indefinite integrals), Fourier series, advanced integrals, and differential equations.

Linear Algebra:

Deals with vector spaces, linear transformations, diagonalization, eigenvalues and eigenvectors, matrix decompositions (such as LU, SVD, and Cholesky), inner products, orthogonalization methods, and other related topics.

Statistics and Probability:

Includes descriptive statistics (measures of central tendency, dispersion, etc.), probability theory (including conditional probability, Bayes’ theorem, and the law of large numbers), random variables and probability distributions, inferential statistics (estimation, hypothesis testing, ANOVA), regression and correlation, and Bayesian statistics.

Course Instructor -

Dr. Nisha Godani, Assistant Professor at Medicaps University, Indore. She has done a PhD in Mathematics from Dayalbagh Educational Institute (Deemed University), Agra on the topic "A study of Non-Manifold topologies on Lorentz Manifolds".

She has qualified NET and GATE examinations with All India rank 40 and 90 respectively. She has been awarded various awards such as the Best Teacher award, Best Researcher award, Excellent Classroom Teaching Award and Chancellor Award by GLA University, Mathura where she worked as an Assistant Professor for 6.5 years.

She has attended various workshops and presented research papers at many conferences. She has organized a one-day workshop on Matlab and R programming and delivered lectures in FDPs.

She has published 50 research papers on non-manifold topologies and wormholes in various reputed journals

She has more than 8 years of teaching experience. She is guiding one PhD scholar under my supervision.

Course Coordinator - Parth Bhanushali

Parth Bhanushali holds the Certificate in Quantitative Finance (CQF) and has a strong foundation in both traditional and quantitative finance. A commerce graduate, he has also completed two levels of the CFA program. While initially focused on conventional financial principles, his curiosity about the mathematical and algorithmic aspects of portfolio management and risk mitigation led him to pursue the CQF. Through this program, he developed a deep understanding of mathematical and computational techniques in finance.

Since then, Parth has been continuously delving into advanced concepts in risk management, financial engineering, and quantitative modeling, further expanding his expertise in the field of quantitative finance.

For any query reach out at

Email - abcdmaths@quantinsider.io

Phone Number - 8349489231

Quant Insider