Math for Data Science
Math for Data Science
Mathematics is considered to be the core subject of any science discipline. Many techniques of modern data science including machine learning have a deep mathematical underpinning.
Machine Learning
Mathematical Underpinning
Advance LMS Platform
Ameltek features an advanced LMS platform that offers screen sharing and video conferencing for effective, one-on-one remote-tutoring and training. It ensures that our students receive the best support possible for success.
Data Science
Data Science is not just limited to a particular subject area and may deal with several diverse aspects like cancer diagnoses and social-behavior analysis. It provides the possibility of a dizzying array of n-dimensional mathematical objects, optimization objective function, statistical distributions, etc.
- Functions, Variables, Equations and Graphs
- Statistics
- Optimization and Operation Research Topics
- Linear Algebra
- Calculus
- Discrete Math
Machine Learning
In specialized fields like theoretical computer science, operation research or control theory, these topics are relevant. But a basic understanding of these powerful techniques prove to be effective in the practice of machine learning. Every machine learning algorithm virtually aims at minimizing some kind of estimation error subject to several constraints which is a problem of optimization.
- Basics of optimization and how to formulate the problem.
- Maxima, minima, convex function and global solution.
- Constraint programming and knapsack problem.
- Randomized optimization techniques: hill-climbing, simulated annealing and genetic algorithms.
- Integer, Simplex and Linear programming
Math for Data Science
It is safe to say that if you aim to become a top data scientist, you need to possess an extraordinary knowledge of all the necessary areas. It includes business acumen, programming ability and your unique and inquisitive mindset about the data. But it is always good to have all the scientific as well as the technical knowledge about the machines lying under the hood, rather than just being the person who is behind the wheels with absolutely no knowledge about the mechanics. By having a well understanding of the mathematical techniques lying behind the cool algorithms, you can gain a significant advantage over others.
Mathematical knowledge is essentially important for newcomers who are coming to data science from different fields like engineering, hardware, chemical industry, retail, health care & medicine, business management and others. Even though these fields require experience with spreadsheets, projections, and numerical calculations but the mathematical skills required in data science can be significantly different.