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data science

  1. Data Science
  2. INTRODUCTION:- Data Science Technology is a key of Data Science must to know how to analyze the problem. Data science is an interdisciplinary field that uses scientific methods. ,processes,algorithms and systems to extract knowledge and insights. from data in various forms,both structured and unstructured,similar. to data mining. Data science is a “concept to unify statistics,data. analysis, machine learing and their related methods.
  3. Meaning of Data Science:- Data science is the future of Artificial Intelligence.All the ideas which you seein Hollywood scientific movies can actually turn into reality by Data science.Therefore,it is very important to understand what is Data science and how can it add value to your business.
  4. Principle of Data Science:- Data science involves working with large amounts of data sets. You may want to be familier with machine larning. The business world produces a vast amount of data frequently.
  5. Uses of Data Science:- Data science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics and machine larning. This aspect of data science is all about uncovering findings from data.
  6. Data Science & Technology(DST):- The Data Science and Technology (DST) Department delivers innovative methods for solving data-intensive science problems. DST activities range from bas and applied research to deployment of software tools.
  7. Data Science features:- Feature Engineering is the art and science of selecting and/or generating the columns in a data table for a machine learning model. When we prepare a table for modeling, not all columns are useful in their raw form.
  8. Learning data science:- This technique is the one which most people are actually to when they talk about feature engineeringWe may generate a new attribute such as number of claims a member has filed for in a given time period, by combining date attribute and a nominal attribute
  9. Data Science Projects:- Data Cleaning. Data scientists can expect to spend up to 80% of theirtime cleaning data,Exploratory Data Analysis, Interactive Data Visualization,.Machine Learning. Communication.
  10. Conclusion:- Data science education is well into its formative stages of development; it is evolving into a self-supporting discipline and producing professionals with distinct and complementary skills relative to professionals in the computer, information, and statistical sciences. Regards,