15 common data science techniques to know and use

Data science has taken hold at many enterprises, and data scientist is quickly becoming one of the most sought-after roles for data-centric organizations. Data science applications utilize technologies such as machine learning and the power of big data to develop deep insights and new capabilities, from predictive analytics to image and object recognition, conversational AI systems and beyond. Indeed, organizations that aren't adequately investing in data science likely will soon be left in the dust by competitors that are gaining significant competitive advantages by doing so. What exactly are data scientists doing that provides such transformative business benefits? The field of data science is a collection of a few key components: statistical and mathematical approaches for accurately extracting quantifiable data; technical and algorithmic approaches that facilitate working with large data sets, using advanced analytics techniques and methodologies that tackle data analysis from a scientific perspective; and engineering tools and methods that can help wrangle large amounts of data into the formats needed to derive high-quality insights.