Data science is the practice of using statistical techniques, regression models, machine learning and deep learning algorithms to produce advanced insights and build predictive applications. All software applications are intended to increase productivity and efficiency by automating human activity. Traditionally, these tasks needed to be repetitive in nature and based on a deterministic set of rules. An example would be an accounting system that can take sales and expenses and automatically create a balance sheet. The intent of data science applications is to automate tasks that require human judgement and are not driven by deterministic rules. Data science is a powerful discipline that can deliver great value to enterprises. It can be applied to a variety of domains and there are specialized domain specific techniques available. But data science problems are open-ended and require experimentation and an active spirit of enquiry. Statistics is a tool in the hands of mankind to translate complex facts into simple and understandable statement of facts. Both these approaches are used in this book with examples to explain the concepts. This book comprises previous question papers problems at appropriate places and also previous GATE questions at the end of each chapter for the benefit of the students
1.Descriptive Statistics, 2. Measures of Central Tendency and Variability, 3. Correlation 4. Regression, 5. Probability, 6. Random Variables & Distribution Functions, 7. Probability Distributions, 8. Estimation, 9. Testing of Hypothesis (Large Sample Tests), 10. Test of Significance (Small Sample Tests) • Statistical Tables
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