updated readme

Author: dingran

README.md

@@ -1,64 +1,99 @@
 # Quantitative interview questions and strategies
 
-Table of content
+## Table of content
 ---
-<TBA>
+
+
+
+---
+
+## Introduction: objective and scope
+
+Pertinent, concise, intuitive, self-contained.
+
+
 
-# Calculus and linear algebra
-# Probability
-# Statistics
-# Programming essentials
-# Numerical methods and optimization
-# Machine learning concepts
+## Calculus and linear algebra
 
 
 
-# Probability
-## Combinatorics
+## Probability
+### Combinatorics
 - Permutation
 - Combinations
 - Inclusion-exclusion
 
-## Conditional probability
+### Conditional probability
 - Bayes rule
 - Law of total probability
 
-## Probability Distributions
+### Probability Distributions
 - Expectation and variance equations
-- Discrete probability
+- Discrete probability and stories
 - Continuous probability: uniform, gaussian, poisson
 
-## Expectations, variance, and covariance
+### Expectations, variance, and covariance
 - linearity of expectation
 - law of total expectation
 - covariance and correlation
 - independence implies zero correlation
 - hash collision probability
 
-## Universality of Uniform distribution
+### Universality of Uniform distribution
 - proof
 - circle problem
 
-## Order statistics
+### Order statistics
 - expectation of min and max and random variable
 
-## Graph-based solutions involving multiple random variables
+### Graph-based solutions involving multiple random variables
 breaking stick
 meeting at the train station
 simplex
 frog jump
 
-## Approximation trick1: Central limit theorem
+### Approximation trick1: Central limit theorem
 - fake coin
 - monte carlo integration
 
-## Approximation trick2: Poisson paradigm
+### Approximation trick2: Poisson paradigm
 - birthday problem, birthday triplets, near birthday problem
 - repeated draws
 
-## Poisson count/time duality
+### Poisson count/time duality
 - possion from possions
 
-## Markov chain tricks
+### Markov chain tricks
 - various games
 - introduction of martingale
+
+
+## Statistics
+
+
+## Programming essentials
+The bare minimum of coding concept you need to know well.
+
+Material on these topics are widely available elsewhere, so we will just cite them here.
+
+Data structures:
+array, dict, link list, tree, heap, graph, ways of representing sparse matrix
+
+Sorting: brilliant.org
+
+Tree/Graph related algorithms
+traversal (BFS, DFS)
+shortest path (two sided BFS, djikstra)
+
+Recuision, iteration and DP:
+
+## Numerical methods and optimization
+
+## Machine learning concepts
+
+
+
+## Contribute
+If you wish to contribute to this writing, feel free to submit a PR.
+
+If you wish to contribute anonymously, please contact us at TBD