000			nam a22 4500
999			_c10055 _d10055
005			20191114115237.0
008			191114b xxu||||| |||| 00| 0 eng d
020			_a9789388511377
040			_cAIKTC-KRRC
041			_aENG
082			_2DDC23 _a005.1 _bGUP
100			_910436 _aGupta, Prateek
245			_aData science with jupyter _b: Master data science skills with easy-to-follow python examples
250			_a1st
260			_aNew Delhi _bBPB Publication _c2019
300			_axii, 309p. _bPaperback _c24*19 cm
520			_aModern businesses are awash with data, making data-driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use a powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you’ll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. _b By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques. Audience The book is intended for anyone looking for a career in data science, all spiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience. Key Features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way. Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps. Cluster, transform, visualize, and extract insights from unlabeled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios. Table of Contents 1 ) Data Science Fundamentals 2 ) Installing Software and Setting up 3 ) Lists and Dictionaries 4 ) Function and Packages 5 ) NumPy Foundation 6 ) Pandas and Data-frame 7 ) Interacting with Databases 8 ) Thinking Statistically in Data Science 9 ) How to import data in Python? 10 ) Cleaning of imported data 11 ) Data Visualisation 12 ) Data Pre-processing 13 ) Supervised Machine Learning 14 ) Unsupervised Machine Learning 15 ) Handling Time-Series Data 16 ) Time-Series Methods 17 ) Case Study
650		0	_94622 _aComputer Engineering
942			_2ddc _cBK