Which is better data science or CS?

Computer science is the better path for people fascinated by software, hardware, and pushing the limits of what computers can do. Data science is the better path for people obsessed with pushing the boundaries of statistics, machine learning, AI, and heuristics.

What is operations research in data science?

Operations research (O.R.) is defined as the scientific process of transforming data into insights to making better decisions. Analytics is the application of scientific & mathematical methods to the study & analysis of problems involving complex systems.

Is operations research useful for computer science?

Operation Research also represents a clear idea about co-operation between intelligent relations with decision making. The optimization models are very useful in computer science, especially in software engineering and computer network domains. A system model can be built and mathematically prove by O.R models.

What is the difference between operations research and machine learning?

A very simplistic view may assign to Data Science a predominant role on Descriptive Analytics, Machine Learning being the reference approach to Predictive Analytics, while Operations Research being referred to as the main approach to Prescriptive Analytics.

What pays more computer science or Data Science?

The national average salary paid to data scientists is $116,654 per year. A computer scientist is likely to receive an average remuneration of $103,730 per year. A data scientist needs to have the technical skills to collect and analyze data, as well as exceptional communication and management skills.

What is the role of operations research in engineering?

The main aim of operations research (OR) is to apply developed analytical methods to help make better decisions. A significant feature of OR is its overall look at the system and trying to improve it as a whole instead of focusing on one or more system components.

What is operations research?

Operations Research is the science of mathematical optimization: you model a problem into “equations”, solve this mathematical model and translate the solutions back into your initial problem setting. It is a tool to help to make decisions: what should/can I do to obtain this or that.

Is Operations Research an orthogonal discipline to data science?

So, in my opinion, Operations Research and Data Science are mostly orthogonal disciplines, although there is some overlap. In particular, I think that time-series forecasting appears in a non-trivial amount in OR; it’s one of the more significant, non-math programming-based parts of OR.

What is the difference between DS and or in machine learning?

On a conceptual level I understand that DS tries to extract knowledge from the available data and uses mostly Statistical, Machine Learning techniques. On the other hand, OR uses the data in order to make decisions based on the data, for example by optimizing some objective function (criterion) over the data (input).

What is data science and why is it important?

Data science is a broad field that deals with data in general. If this sounds vague it is normal because it really is. It has been a buzz word for quite some years now. Essentially, it tries to find a way to exploit data: what can I do with my data (what insights can I get from it?).