Data Preparation and Analysis

Data science is an evolving field, and the ability to effectively prepare and analyze data is a critical skill for any aspiring professional. This book serves as a comprehensive introduction to the foundational concepts and tools of data science, making it ideal for beginners and aspiring data professionals.

This book provides a structured and comprehensive learning path, beginning with a broad introduction to data science, its applications, and fundamental analysis methods. You will then explore the core Python libraries for data manipulation, NumPy for efficient numerical operations, and Pandas for powerful data structuring and transformation. The book dedicates significant focus to real-world data challenges, walking you through the crucial steps of data gathering, preparation, and cleaning; addressing issues like scalability, missing data, and inconsistencies.

The book concludes with three real-world projects that apply the concepts in practical settings, making you proficient in the entire end-to-end data preparation and analysis pipeline. You will have a solid command of essential tools and techniques, empowering you to confidently tackle and derive meaningful insights from diverse datasets in any professional setting.

WHAT YOU WILL LEARN
● Implement ML models using NumPy, Pandas, Matplotlib, or scikit-learn.
● Gain a solid foundation in data science, principles, algorithms, and methodologies.
● Learn to frame real-world problems as ML tasks.
● Implement data cleaning for consistency and missing data.
● Conduct exploratory data analysis with descriptive statistics.
● Uncover data patterns using clustering and association techniques.
● Design and create effective time series visualizations.
● Build interactive visualizations to explore data.
● Apply an end-to-end data workflow in practical projects.

WHO THIS BOOK IS FOR
This book is ideal for students, programmers, and software engineers who want to learn data science from scratch. It assumes basic programming proficiency, but no prior data science knowledge is required to follow the comprehensive, hands-on curriculum.

1. Introduction to Data Science
2. NumPy
3. Pandas
4. Data Collection and Data Preprocessing
5. Data Cleaning
6. Exploratory Data Analysis
7. Data Visualization
8. Projects
Appendix

Dr. Pooja Sharma, assistant professor, in computer science and engineering, has teaching and research experience of more than 18 years. She is a gold medalist in postgraduate, and her other academic achievements include a fellowship for regular PhD from UGC, New Delhi, after qualifying UGC NET and JRF, several merit certificates, gold and silver medals in matric, higher secondary, undergraduate, and postgraduate levels. She was awarded a PhD in 2013 on Content-Based Image Retrieval under the supervision of Dr. Chandan Singh from Punjabi University, Patiala. She has several research publications in peer-reviewed international journals of Springer and Elsevier with significant Thomson Reuters impact factors. She is the author of various research book chapters in Taylor and Francis, Scrivener, and CRC. She has published books, viz. Programming in Python and Simplified Machine Learning. She is the reviewer of various international journals of Elsevier, IET (IEEE Computer Society), Scientific Reports, The Journal of Supercomputing, and Scientific Research and Essays. She has participated in various conferences and workshops. Her areas of specialization include data analysis, machine learning, content- based image retrieval, face recognition, pattern recognition, and digital image processing. Through her YouTube channel, she offers free guidance on effective research, academic publishing, journal writing, and the use of AI tools. She worked and was selected at various eminent Universities and Colleges, including Central University. She had been the head of department at DAV University for 3 years. Currently, she holds the position of assistant professor in the department of computer science and engineering, IKG Punjab Technical University, Main Campus, Kapurthala.