02857nam a22002537a 450000500170000000800410001702000180005804000070007604100120008308200170009510000260011224500400013826000380017830000280021636500530024452019380029765000360223565000350227165000270230665000770233394200120241099900190242295201620244120251030164443.0251029b        |||||||| |||| 00| 0 eng d  a9789389898064  cAL  aEnglish  a005.74bGUPP  aPrateek Gupta9243304  aPractical data science with jupyter  aNew DelhibBPB Publicationsc2023  axvii,341p.bPBc24x19cm  a4224b₹639.20c₹d₹799.00e20%f23-10-2025  aThis book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready.
This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
bKEY FEATURES
Essential coverage on statistics and data science techniques.
Exposure to Jupyter, PyCharm, and use of GitHub.
Real use-cases, best practices, and smart techniques on the use of data science for data applications.
WHAT YOU WILL LEARN
Rapid understanding of Python concepts for data science applications.
Understand and practice how to run data analysis with data science techniques and algorithms.
Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms.
Become self-sufficient to perform data science tasks with the best tools and techniques.
WHO THIS BOOK IS FOR
This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples.

  a Explore data cleaning 9243305  aCleaning preprocessing9243306  aData Wrangling9243307  aFeature Enginering and Machine Learning using Python and Jupyter9243308  2ddccBK  c240600d240600  00102ddc40708CSEaSEbSEd2025-10-23eBiblios Book Point, Surathkal-575014g639.20l0o005.74 GUPPpSE000131r2025-10-29 11:24:36v799.00w2025-10-23yBK