Internet, media, mobile devices, and sensors continuously collect massive amounts of data. Learning from this data gives improvements in science and quality of life. Big Data is a big blessing; that also presents big challenges arising from its inherent characteristics, namely volume, variety and velocity. Big Data is impossible to analyze by using traditional central methods and therefore, distributed processing with parallelization is needed. Data analytics often must be performed real-time or near real time. Gaining an answer to the analysis demands on almost real-time, is almost preferred to a precise decision but in a timely manner. Optimization algorithms for Big Data aim to reduce the computational, storage, and communications challenges. The data and parameter sizes of Big Data optimization problems are too large to process locally and since the Big Data models are inexact, optimization algorithms no longer need to find the high accuracy solutions. In this paper, we provide an overview of this emerging field; describe optimization methods used for Big Data Analytics (BDA) like first-order methods, randomization, heuristic, evolutionary and convex algorithms.