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 - 14th March 2026
Course End date - 26th April 2026
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
For any query reach out at
Email - [email protected]
Phone Number - 8349489231