Quant Reasoning enables us to apply basic math and stats skills to solve such real-world problems across disciplines. The idea of this course is to drill these concepts in a visual and interactive fashion and develop critical thinking which is a must-have for any career, especially one in Data Science!
This course is an essential primer for anyone who wishes to pursue the R or Python Career track.
Any individual who:
Introduction to Graphs – the why and how
Graphs of Common Functions.
To look at Data and be able to identify the nature of function which can best fit the data
Concept of Derivative – What it means and how to find derivatives of common functions.
The intuition behind rules for finding derivatives. Optimization using derivatives
Concept of partial derivatives and uses in optimization
Introduction to Integration.
Common tools and techniques used in the integration. Applications.
Formulating and solving a system of equations – the role of determinants and matrices.
Principles of Counting – Permutations Combinations with Lots of Interesting and Practical Examples. How the estimates help in determining complexities of algorithms.
Introduction to Probability Theory with Lots of interesting examples. Conditional Probability.
Representing data – common techniques to represent data.
Random Variables – Discrete and Continuous. Measures of Central Tendency.
Binomial, Poisson Distributions – Applications.
Introduction to the Normal Distribution.
Solving problems using tables, excel, calculators.
Hypothesis Testing – z test, t-test.
Point Estimates, Confidence intervals, p-values.
Hypothesis testing – Chi-Square test.
Linear Regression – Concept of fitting a line to a given data.
Testing for correlation coefficients.
A company sells 1000 washing machines per year, on an average. Getting them all at once will reduce the per order cost and headaches, but then there is a warehousing cost. What is the ideal quantity to order based on other parameters? What are these other parameters?
There is going to be a closely contested election at the state level. A simple sample of 500 people polled gives the candidate 52% of the votes. Is that enough to sit back and relax? What could be the sample size to give the candidate the confidence needed to take further action?
A person claims that she has ESP. Can we devise a test to check the claim. If probability dictates that she will be right 25% of the time, and she is right 27% of the time when asked to perform the experiment 1000 times, is that enough proof to validate her claim? What about 28%? What is that magic number when we can accept her claim? And even if we do, can we still be wrong? What is that margin of error?
MDAE alumni working in diverse roles across leading companies.
Trainee Decision Scientist
Senior Research Analyst
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