Asar Dasar Data Science Dan Aplikasinya Dengan Python
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Dasar-Dasar Data Science dan Aplikasinya dengan Python
Arus informasi dan teknologi yang semakin cepat sekarang ini, telah membawa banyak perubahan bagi kita sebagai masyarakat dunia, salah satu hasil teknologi adalah adanya software-software yang sangat membantu pekerjaan dan rutinitas kita sehari-hari, termasuk untuk pencarian dan pengolahan data dari berbagai sumber digital. Disini penulis mengetengahkan teori dasar tentang data science, tahapan pengolahan data dan apa saja yang perlu dikuasai untuk menjadi ilmuwan data, termasuk bahasa pemprogram data scince yang paling populer, yaitu bahasa Python. Pembaca akan belajar bagaimana membuat code python untuk menghasilkan diagram, menghitung statistik, membuat peramalan dan clustering data menggunakan kode python baik secara offline maupun online (menggunakan google colab). Dengan adanya teori dasar dan penerapannya dengan python, peluang pembaca untuk menjadi ilmuwan data yang sukses akan semakin besar. Penyajian materi diberikan secara jelas dan terperinci disertai dengan berbagai contoh kasus nyata sehari-hari. Dalam setiap bab diberikan contoh-contoh latihan dan diakhiri dengan soal latihan yang dapat membantu Pembaca untuk lebih memahami ulasan yang telah disajikan.
Introduction to Data Science
This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the interdisciplinary field of data science. The coverage spans key concepts from statistics, machine/deep learning and responsible data science, useful techniques for network analysis and natural language processing, and practical applications of data science such as recommender systems or sentiment analysis. Topics and features: Provides numerous practical case studies using real-world data throughout the book Supports understanding through hands-on experience of solving data science problems using Python Describes concepts, techniques and tools for statistical analysis, machine learning, graph analysis, natural language processing, deep learning and responsible data science Reviews a range of applications of data science, including recommender systems and sentiment analysis of text data Provides supplementary code resources and data at an associated website This practically-focused textbook provides an ideal introduction to the field for upper-tier undergraduate and beginning graduate students from computer science, mathematics, statistics, and other technical disciplines. The work is also eminently suitable for professionals on continuous education short courses, and to researchers following self-study courses.
Data Science Using Python and R
Author: Chantal D. Larose
language: en
Publisher: John Wiley & Sons
Release Date: 2019-03-21
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R. Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining. Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars. Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.