Linear Algebra one ofthe main branches of Math in Data Science. --> In building linear equations which are imp comp of M.L Algo development. --> to examine and observe datasets --> In ML , loss fn,regularization,covariance, regularization, SVM Classification.CALCULUS --> In gradient descent and Algo. training STATISTICS --> In ML in classification PROBABILITY --> Hypo testing and distribution No.THEORY --> O(1,3,5,7...),E(2,4,6,8...),P(1,3,5,7,11...),C(4,6,8,9,10,12..) etc --> 1(Module4): 0ne more than multiple of 4 (5,17etc) --> 3(Module4): 3 more than multiple of 4(7,19) TRIANGULAR No. Sequence of numbers that can form equilateral triangle when represented as dots or objects. which fully satisfy equation n(n+1)/2 1st T. no. is 1 2nd is 2(2+1)/2=3 3rd is 3(3+1)/2=6 10,15,21,28,36 etc. PERFECT NUMBERS: I'm going to be perfect now. i.e; rebuilding myself after being broken down into pieces. 6= sum of its propr divisors 1+2+3 28= 1+2+4+7+14 Fibonacci No. series of no.where nxt one is found by adding up the two numbers before it. like.. 0,1,1,2,3,5,8,13,21,34,etc..