Data Science with Jupyter : Master Data Science skills with easy-to-follow Python examples

Step-by-step guide to practising data science techniques with Jupyter notebooks Ê Description Modern 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 powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. Ê 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, youÕll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data.Ê 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. Ê 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. Ê Audience The book is intended for anyone looking for a career in data science, all aspiring 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. Ê Key Features áÊÊÊÊÊÊ Acquire Python skills to do independent data science projects áÊÊÊÊÊÊ Learn the basics of linear algebra and statistical science in Python way áÊÊÊÊÊÊ Understand how and when they're used in data science áÊÊÊÊÊÊ Build predictive models, tune their parameters and analyze performance in few steps áÊÊÊÊÊÊ Cluster, transform, visualize, and extract insights from unlabelled datasets áÊÊÊÊÊÊ Learn how to use matplotlib and seaborn for data visualization áÊÊÊÊÊÊ Implement and save machine learning models for real-world business scenarios Ê Table of Contents 1 )Ê Data Science Fundamentals 2 )Ê Installing Software and Setting up 3 )Ê Lists and Dictionaries 4 )Ê Function and Packages 5 )Ê NumPy Foundation 6 )Ê Pandas and Dataframe 7 )Ê Interacting with Databases 8 )Ê Thinking Statistically in Data Science 9 )Ê How to import data in Python? 10 ) Cleaning of imported data 11 ) Data Visualization 12 ) Data Pre-processing 13 ) Supervised Machine Learning 14 ) Unsupervised Machine Learning 15 ) Handling Time-Series Data 16 ) Time-Series Methods 17 ) Case Study Ð 1 18 ) Case Study Ð 2 19 ) Case Study Ð 3 20 ) Case Study Ð 4

Author: Prateek Gupta

Publisher: BPB Publications

Publication Date: Mar 26, 2019

Number of Pages: 323 pages

Language: English

Binding: Paperback

ISBN-10: 9388511379

ISBN-13: 9789388511377