Statistic-For-Data Science a preparation tools to enter into the data science field with explanation - applications of using it - weak point of it - strong point of it and how it works with the python codes

Statistics is the science of collecting, analyzing, interpreting, and presenting data. It is a fundamental tool for data science, which is the field of study that deals with the collection, analysis, interpretation, and presentation of data.

There are many reasons why statistics is important for data science. Here are a few of the most important reasons:

• Statistics can be used to describe data. This includes summarizing data using measures of central tendency and dispersion, as well as visualizing data using charts and graphs.
• Statistics can be used to make inferences about populations from samples. This is done using statistical inference techniques, such as hypothesis testing and confidence intervals.
• Statistics can be used to predict future events. This is done using predictive modeling techniques, such as regression and classification.
• Statistics can be used to optimize decision-making. This is done by using statistical decision theory techniques, such as cost-benefit analysis and decision trees.

In short, statistics is a powerful tool that can be used to make sense of data. It is an essential tool for data scientists, and it is becoming increasingly important in other fields as well.

Here are some specific examples of how statistics is used in data science:

• Data mining: Statistics is used to identify patterns and trends in data. This can be used to make predictions about future events, or to improve the performance of machine learning models.
• Machine learning: Statistics is used to develop and evaluate machine learning models. This includes selecting features, tuning hyperparameters, and evaluating model performance.
• Natural language processing: Statistics is used to analyze text data. This can be used to extract features, classify text, and generate text.
• Computer vision: Statistics is used to analyze image and video data. This can be used to identify objects, classify images, and track objects.

These are just a few examples of how statistics is used in data science. As data science continues to grow in popularity, the demand for statisticians is also expected to grow.