Mathematics Instructor

The Data Institute, Pakistan is looking for a Mathematics Instructor to join our roster of subject matter experts that have a passion for providing excellent service to ensure a high-quality student experience and collaborative working environment. Becoming an instructor at the DIP will provide you with a greater distribution advantage than any other platform, allowing you to grow your brand, develop your leadership skills, and build your network. You will also have the opportunity to develop core content for candidates looking to develop their Data Science expertise.

About DIP

The Data Institute is a project of The Shaikh Center for Data Sciences, USA and managed by Data Science Technologies Pvt Ltd, Pakistan. We foster the teaching and training of people in the space of Data Science and AI. Our focus is to attract talented individuals, educate and train them through the courses we provide; give them a platform that creates opportunities to innovate on a greater scale, and/or acquire talent once they’ve graduated into a world-class engineering company. Effectively, we are disrupting and seeding the future with emerging talent!

Our Three Minarets offer a wide range of support programs for the cohorts to help them better orient in the new environment.

Institution: Data Science Literacy & Talent Development

Incubator: Empower Ideas by helping students Ideate, Develop, & Go-To-Market

Industry: Hire students into product development roles

The Role We Play

We aim at bridging the gap between academia and industry through relevant and hands-on training. By arranging low-cost data science classes, workshops for the learning and skill development of students, entrepreneurs. Our goal is to Nurture Entrepreneurship; The most important building block is to inculcate the mindset in the youth that entrepreneurship is a viable career choice. We not only help students tap into Industry Partnerships across borders and mentors but also help them Raise and direct Capital

The Role You Will Play

You will be required to teach the courses catered for new entrants, along with responsibilities to the organization and institute as determined at the time of hiring. The position may be extended for the next academic year.

• Develop state of the art content supporting the following Data Science subject area(s):
• Establish the new entrant curriculum roadmap for Data Science courses using the DIP guidelines
• Build multiple modalities of content for instruction, including but not limited to assessments, hands-on labs, and study guides
• Coach other Data Institute community instructors on content selection

Instructor Requirements

This selected Instructor will introduce the mathematical foundations to derive Principal Component Analysis (PCA). He/She will cover some basic statistics of data sets, such as mean values and variances, teach how to compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, the instructor will then be expected to show how to derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.

By the end of this course, students should be familiar with important mathematical concepts and should be able to implement PCA all by themselves.

Courses:

Mathematics for Machine Learning: PCA, Linear Algebra & Multivariate Calculus Software Programming 101: SQL, R

Breakdown:

Statistics of Datasets

Principal Component Analysis (PCA) is one of the most important dimensionality reduction algorithms in machine learning. In this course, you will lay the mathematical foundations to derive and understand PCA from a geometric point of view. You will teach how to summarize datasets (e.g., images) using basic statistics, such as the mean and the variance. You will provide mathematical intuition as well as the skills to derive the results.

Inner Products

In this module, you will introduce the concept of an inner product which will allow students to understand geometric concepts in vector spaces. The selected instructor will start with the dot product as a special case of an inner product and then move toward a more general concept of an inner product, which plays an integral part in some areas of machine learning, such as kernel machines (this includes support vector machines and Gaussian processes).

Orthogonal Projections

In this module, the instructor will teach orthogonal projections of vectors, which live in a high-dimensional vector space, onto lower-dimensional subspaces.He/She will have to give pen-and-paper practice and a small programming example to the students.

Principal Component Analysis

Principal Component Analysis (PCA) is one of the most fundamental dimensionality reduction techniques that are used in machine learning. Within this course, you will be expected to go through an explicit derivation of PCA plus some coding exercises aiming to make our students proficient user of PCA.