Data Science Solutions

Data is all around the world. In the data driven world there are many different terms to be understood to analyze the informations out of them. In this post you’ll understand the various data science solutions.


  • Predictive Analytics
  • Statistical Modeling
  • Machine Learning
  • Data Engineering
  • Attribution Modeling
  • Personalization
  • Decision/Portfolio Optimization
  • Content Optimization
  • Predictive Analytics: Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about future.
  • Statistical Modeling: In simple terms, statistical modeling is a simplified, mathematically-formalized way to approximate reality (i.e. what generates your data) and optionally to make predictions from this approximation. The statistical model is the mathematical equation that is used.
  • Machine Learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
  • Data Engineering: Data Engineering is also called as information engineering that they build pipelines that transform that data into formats that data scientists can use.
  • Attribution Modeling : Attribution modeling is a framework for analyzing which touchpoint, or marketing channels, receive credit for a conversion.
  • Personalization : Personalization is a means of meeting the customer’s needs more effectively and efficiently, making interactions faster and easier and, consequently, increasing customer satisfaction and the likelihood of repeat visits.
  • Decision Optimization: Decision Optimization technology uses advanced mathematical and artificial intelligence techniques to solve decision-making problems that involve millions of decision variables, business constraints and trade-offs.
  • Content Optimization: Content optimization is the process of making sure content is written in a way that it can reach the largest possible target audience.

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