Data Science is the real-world application of machine learning.Data in our world increasing at exponential rate,big data technologies are evolving rapidly. Enterprises are now striving to evolve from descriptive analytics to predictive analytics. Data science offers exciting capabilities for businesses to continually unlock the potential of their data through techniques such as predictive analytics, machine learning, and data mining.JOIN US to know more about Data Science and Machine Learning.


Data science also known as data-driven science.Data Science is used to extract information from various forms of data, either structured or unstructured.Data science is similar to data mining.Data mining  is used to extract information from a data set and to transform it into  further usable information.

Data science is a concept  to unify statistics, data analysis, machine learning and their related methods  to  analyze actual method.Data science as an independent discipline, extending the field of statistics to incorporate “advances in computing with data”. Data science might therefore imply a focus involving data and, by extension, statistics, or the systematic study of the organization, properties, and analysis of data.


Machine learning is a field of computer science that gives computer systems the ability to “learn” with data, without being explicitly programmedMachine learning focuses on the development of computer programs that can access data and use it to learn for themselves.The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output value within an acceptable range.

Machine learning is a continuously developing field.Things in machine learning are repeated over and over, and hence machine learning is iterative by nature.We can say that using machine learning we can find patterns, and then create a model that recognizes those patterns with new iterative data.

  •     Genetics Analysis
  •     A/B Testing
  •     Clinical Trials of new Drugs
  •     Alerts And Diagnostics of                  Patient Data
  •     Customer Segmentation
  •     Market Analysis
  •     Credit Worthiness Evaluation
  •     Sales Forecasting