Statistics for Data Scientists and Analysts: Statistical approach to data-driven decision making using Python (English Edition)

Description

Statistics is a powerful tool for data analysis, visualization, and inference. Python is a popular programming language that offers a rich set of libraries and frameworks for statistical computing. Together, they can help you solve real-world problems and make informed decisions based on data. This book teaches you how to use Python to implement statistical concepts and techniques in a practical and effective way. You will also learn how to perform data science and analysis to generate insights, patterns, and trends.

This book introduces the basics of statistics, such as descriptive and inferential statistics, ML, probability distributions, hypothesis testing, and confidence intervals. It also covers advanced topics such as regression analysis, linear algebra, statistical tests, time series, survival, and correlation analysis. You will learn how to identify patterns, interpret data, and make data-driven decisions. The book emphasizes practical learning with examples, exercises, and code snippets using popular Python libraries like NumPy, Pandas, Matplotlib, Seaborn, and SciPy to perform various statistical tasks.

By the end of this book, you will have a solid foundation in statistics and Python programming. You will be able to explore, analyze, and visualize data using Python. You will also be able to perform various statistical tests and interpret the results.

Key Features

● Learn how to analyze data using statistics, with a focus on cutting-edge statistical methods, modeling, and visualization.

● Explore topics from basic to advanced, including data visualization, statistics, machine learning (ML), and large language models (LLMs).

● Includes clear examples, hands-on tutorials, and a real-world project to apply all concepts.

What you will learn

● Master data manipulation, cleaning, and visualization techniques using Python.

● Apply core statistical methods to analyze real-world datasets.

● Build and evaluate statistical models for regression, classification, and clustering.

● Interpret and communicate insights derived from statistical analyses effectively.

● Explore advanced statistical techniques like time series and survival analysis.

Who this book is for

This book is ideal for data scientists, ML engineers, statisticians, Python practitioners, researchers, and anyone who works with data and statistics.

Table of Contents

1. Foundations of Data Analysis and Python

2. Exploratory Data Analysis

3. Frequency Distribution, Central Tendency, Variability

4. Unravelling Statistical Relationships

5. Estimation and Confidence Intervals

6. Hypothesis and Significance Testing

7. Statistical Machine Learning

8. Unsupervised Machine Learning

9. Linear Algebra, Nonparametric Statistics, and Time Series Analysis

10. Generative AI and Prompt Engineering

11. Real World Statistical Applications