__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**.

**Mastered the basic principles of calculus and recognize where to apply them.****The ability to visualize common functions, their graphs and data trends.****Know how to set-up equations and the tools to solve them.****Learnt the basics of ‘regression’, hypothesis testing and various distributions.****Be able to understand how conclusions are drawn based on samples, be it election outcomes, or**vaccine efficacy.

Any individual who:

**Aspires to pursue a career in data science but lacks strong quantitative formal training.****Wants to be able to adopt a numbers-based / data centric approach to problem solving.****Requires to demonstrate strong quant skills for a career switch or even for applications to foreign universities.**

**Simple algebraic expansions and factorization.****Able to manipulate indices. Equations and inequalities in one variable.****Able to solve simple simultaneous equations.****Introductory Co-Ordinate Geometry, slopes, distances, equation of lines.****Familiarity with the concept f(x) – functions will be a plus.**

30 hours instructor-led, live interactive training.

8 assignments, 6 quizzes, pre & post course final exam.

3 live, real-world examples.

Digital office hours + teaching assistant + lifetime recording access on LMS.

Industry event invitations + student network access

Support for Resume building & interview skills.

Worked in the IT industry for almost 30 years before taking up his passion, teaching.

As a part of the corporate world, he had organized and taught in technical training programs. He has been with MDAE for the last 4 years teaching Mathematics, Statistics as well as a Data and Programming Course. He is also part of visiting faculty at St. Xavier’s College, Mumbai where he has taught cross-faculty courses in Mathematics for the Economics, Life Science and Microbiology Departments.

His style of teaching is to drill-in concepts in an intuitive way making use of thought-provoking puzzles, examples and class participation. His class sessions build students’ aptitude in the subject while being thoroughly enjoyable.

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.

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.

*Arushi Mishra *

**Data Consultant**

*Vallari Naik*

**Trainee Decision Scientist**

*Pooja Joshi*

** Senior Research Analyst**

*Nishitha Mehta*

**Risk Analyst**

*Swati Shrimali*

**Business Analyst**

*Ujas Shah*

** Research Analyst**

*"Prof. Shailesh definitely has a very different approach to teaching maths. It was refreshing to see how he took an effort to make us understand the concept and it's applicability rather than dumping formulas and methods. It was also great how Prof. introduced us to various online calculators and other tools. This truly emphasized that getting the right answer isn't all that matters, but it is how you get there and the process of learning that truly matters. I also appreciate the continuous assessment Prof. carried out as it really give us an incentive to study on a regular basis. Moreover, he taught at a very comfortable pace and his explanations were on point. Overall, I had a great time attending Prof. Shailesh's lectures ."*

**Michelle Francis – Batch 2020-21**

*"What added to the experience was how engaging and interactive our classes were. There was never a dull moment in class. Puzzles, games, open book examinations and stories filled the air. I think quite often students struggle to see the practical applicability of what they study. In my opinion, studying with Shailesh Sir helps you close that gap. He not only exposes you to how a concept is applied outside of academia but also equips you to think about any given problem in a certain way. He always told me, "Work smarter, not harder". That was a big takeaway for me."*

*Tvesha Sippy – Batch 2018-19*

₹17,437.5

**(Excluding GST****)**

**Career Track**

**(4 Courses | 120 Hours)**

(MDAE Certified Data Scientist )

** (1 Course | 30 Hours)**

(Quantitative Essentials For Data Science )

Use code **MDAEPAST50 to get the recorded version of the course at 50% discounted price of the live version**

There are 3 distinct advantages of choosing a career track over a skills track –

1) Different certification – With the integrated 4 course training, candidates will become an MDAE Certified Data Scientist – an industry demanded certificate.

2) Small group mentoring – Candidates will receive live mentoring with industry’s best practitioners throughout the program.

3) Capstone projects – Deserving candidates can be evaluated for projects/internships with our industry partners.