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What is Data Science

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Learn Data Science - Introduction Introduction to Data Science History The field of data science has its roots in statistics and computer science and has evolved to encompass a wide range of techniques and tools for understanding and making predictions from data. The history of data science can be traced back to the early days of statistics when researchers first began using data to make inferences and predictions about the world. In the 1960s and 1970s, the advent of computers and the development of new algorithms and statistical methods led to a growth in the use of data to answer scientific and business questions. The term "data science" was first coined in the early 1960s by John W. Tukey, a statistician and computer scientist . In recent years, the field of data science has exploded in popularity, thanks in part to the increasing availability of data from a wide range of sources, as well as advances in computational power and machine learning. Today, data science is us...
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What is Model Evaluation and Selection

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Understanding the Model Evaluation and Selection  Techniques Content of  Model Evaluation •     Model Performance Metrics •     Cross-Validation Techniques •      Hyperparameter Tuning •      Model Selection Techniques Model Evaluation and Selection: Model evaluation and selection is the process of choosing the best machine learning model based on its performance on a given dataset. There are several techniques for evaluating and selecting machine learning models, including performance metrics, cross-validation techniques, hyperparameter tuning, and model selection techniques.     Performance Metrics: Performance metrics are used to evaluate the performance of a machine learning model. The choice of performance metric depends on the specific task and the type of machine learning model being used. Some common performance metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC score. Cross-...
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What is the Probability and Statistics

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Undrstand the Probability and Statistics in Data Science Contents of P robability and Statistics Probability Basics Random Variables and Probability Distributions Statistical Inference (Hypothesis Testing, Confidence Intervals) Regression Analysis Probability Basics Solution :  Sample Space = {H, T} (where H stands for Head and T stands for Tail) Solution :  The sample space is {1, 2, 3, 4, 5, 6}. Each outcome is equally likely, so the probability distribution is: Hypothesis testing involves making a decision about a population parameter based on sample data. The null hypothesis (H0) is the hypothesis that there is no significant difference between a set of population parameters and a set of observed sample data. The alternative hypothesis (Ha) is the hypothesis that there is a significant difference between a set of population parameters and a set of observed sample data. The hypothesis testing process involves the following steps: Formulate the null and al...
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