Data Science Crash Course

Data science is the engine driving modern innovation, making Python mastery essential for anyone looking to turn raw information into actionable strategy. This book serves as your streamlined roadmap, bridging the gap between basic data literacy and professional-grade analytical execution.

This book provides a solid foundation in Python programming, including loops and conditional statements, before advancing to high-performance libraries like NumPy, Pandas, Matplotlib, and SciPy. You will master the data analysis process, from cleaning missing values to advanced visualization with Seaborn and geospatial mapping. It concludes with the mathematical foundations of supervised and unsupervised learning, predictive mining, and building recommender systems through real-world case studies in healthcare, finance, and retail analytics.

By the end of the book, you will be well-equipped to handle complex datasets and deploy predictive models with confidence. You will possess a practical understanding of data science principles and a professional project portfolio, ready to apply these skills to solve real-world problems in any industry.

WHAT YOU WILL LEARN
● Apply supervised, unsupervised learning, and predictive mining algorithms.
● Configure Python environments using essential data science libraries.
● Optimize data manipulation using NumPy and Pandas DataFrames.
● Clean, structured, and unstructured data for analytical modeling.
● Master end-to-end data science workflows and professional roles.
● Implement Python control structures and complex data structures.

WHO THIS BOOK IS FOR
The book is designed for students, engineers, and mathematicians transitioning into data science. This book also supports analysts and managers aiming for strategic decision-making. Researchers and current professionals can strengthen their foundations, provided they possess a basic understanding of mathematics and logical reasoning.

1. Introduction to Data Science
2. Roles and Responsibilities of a Data Scientist
3. The Necessity of Python in Data Science
4. Introduction to Data Understanding
5. Data Preprocessing
6. Creating Synthetic Datasets in MS Excel
7. Basics of Python Programming
8. Working with Python Data Structures
9. Data Analysis Process
10. Essential Python Libraries for Data Science
11. Data Processing and Visualization
12. Mathematical and Scientific Applications
13. Developing Recommender Systems
14. Real-world Applications and Case Studies
15. Practical Examples and Exercises

Dr. Deepti Chopra is an accomplished academician specializing in information technology, with a primary focus on natural language processing (NLP) and artificial intelligence (AI). With over 10.5 years of experience in academia, she has made significant contributions to both research and teaching domains. Deepti's expertise lies in areas such as machine translation, named entity recognition, morphological analysis, and machine transliteration.

Deepti began her academic journey by obtaining a bachelor's degree in computer science and engineering from Rajasthan College of Engineering for Women. Throughout her undergraduate studies, she consistently excelled and secured top positions in her college. Driven by her passion for language and technology, she pursued a master's degree in computer science and engineering from Banasthali Vidyapith, where she once again showcased exceptional skills and graduated with top honors.

Motivated to delve deeper into her research interests, Deepti pursued a Ph.D. in computer science and engineering from Banasthali Vidyapith. Her doctoral research revolved around enhancing the quality of machine translation, and she achieved remarkable success in this area. Consequently, she earned a Ph.D. degree with a specialization in quality improvement of machine translation. Her doctoral work resulted in the publication of numerous research papers and the grant of an Australian patent for her innovative approach in named entity translation.

Deepti's commitment to advancing knowledge in her field is reflected in her extensive publication record. She has authored multiple books, most notably Building Machine Learning Systems using Python and Flutter and Dart: Up and Running. Additionally, her research papers have been published in reputable international conferences and journals.

Deepti's commitment and her ability to apply research findings to practical solutions solidify her position as a prominent figure in the field.