Data science is one of the fastest-growing fields in technology today, powering decisions across industries through data-driven insights, machine learning, and advanced analytics.
The book journeys from the fundamentals of data science to the advanced concepts and applications in the present-day computer vision techniques and data analysis. It covers the full data science pipeline, beginning with core fundamentals, ethics, and the analytics lifecycle, moving through data preprocessing, visualization, and a strong statistical foundation covering probability theory, Bayesian inference, and Monte Carlo simulation. This second edition expands on the first with two dedicated machine learning and deep learning chapters, adding AutoML, reinforcement learning, graph neural networks, transformer networks, and hybrid big data processing architectures.
By the end of this book, readers will emerge as competent data practitioners equipped with a thorough understanding of data science concepts. They will have gained the technical proficiency to use modern tools such as Python, PyTorch, TensorFlow, and data visualization tools, along with the analytical skills required for business problem-solving and data-driven decision-making across diverse real-world domains.
What you will learn
● Comprehensive understanding of the data science lifecycle and core tools.
● Building supervised, unsupervised, and semi-supervised learning models in Python.
● Performing time series analysis using ANN, SVM, and stochastic models.
● Developing deep learning models using CNN, RNN, and encoder-decoder networks.
● In this 2nd edition, explore AutoML, Transformers, and expanded deep learning.
● Conducting social media analytics through text mining and trend detection.
● Applying business analytics, financial modelling, and fraud detection strategies.
● Working with Hadoop ecosystem tools for scalable big data analytics.
Who this book is for
This book is designed for students, researchers, data analysts, and professionals such as software engineers, business analysts, and aspiring data scientists who seek a strong foundation in data science. It also serves educators and industry practitioners aiming to apply analytical and machine learning techniques to real-world challenges.
Table of Contents
1. Fundamentals of Data Science
2. Data Preprocessing
3. Data Plotting and Visualization
4. Statistical Data Analysis
5. Advanced and Computational Statistical Analysis Techniques
6. Machine Learning for Data Science
7. Advanced Machine Learning for Data Science
8. Time-series Analysis
9. Deep Learning for Data Science
10. Advanced Architectures in Deep Learning for Data Science
11. Social Media Analytics
12. Business Analytics
13. Big Data Analytics
