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Packt: Statistics for Data Science and Business Analysis

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MP4 | Video: AVC 1280x720 | Audio: AAC 44.1 KHz 2ch | Duration: 4 hours 19 minutes | English | 702 MB

Video Description
This course will teach you fundamental skills that will enable you to understand complicated statistical analysis directly applicable to real-life situations. Modern software packages and programming languages are now automating most of these activities, but this course gives you something more valuable—critical thinking abilities. This course will help you understand the fundamentals of statistics, learn how to work with different types of data, calculate correlation and covariance, and more. Careers in the field of data science are some of the most popular in the corporate world today. And, given that most businesses are starting to realize the advantages of working with the data at their disposal, this trend will only continue to grow

Style and Approach
A complete course (packed with extensive case studies, complete training, an HD video, and animations) that covers major statistical topics and helps you become a marketing analyst, a business intelligence analyst, a data analyst, or a data scientist. No prior experience is required for the course.

Table of Contents
INTRODUCTION
SAMPLE OR POPULATION DATA?
THE FUNDAMENTALS OF DESCRIPTIVE STATISTICS
MEASURES OF CENTRAL TENDENCY, ASYMMETRY, AND VARIABILITY
PRACTICAL EXAMPLE: DESCRIPTIVE STATISTICS
DISTRIBUTIONS
ESTIMATORS AND ESTIMATES
CONFIDENCE INTERVALS: ADVANCED TOPICS
PRACTICAL EXAMPLE: INFERENTIAL STATISTICS
HYPOTHESIS TESTING: INTRODUCTION
HYPOTHESIS TESTING: LET'S START TESTING!
PRACTICAL EXAMPLE: HYPOTHESIS TESTING
THE FUNDAMENTALS OF REGRESSION ANALYSIS
SUBTLETIES OF REGRESSION ANALYSIS
ASSUMPTIONS FOR LINEAR REGRESSION ANALYSIS
DEALING WITH CATEGORICAL DATA
PRACTICAL EXAMPLE: REGRESSION ANALYSIS

Packt: Statistics for Data Science and Business Analysis

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  1. 用于数据科学和商业分析的统计学 本教程将会为你带来能够让你理解复杂的统计分析,并直接应用于真实场景的基础性知识。现代软件和编程语言正在将这些活动自动化,但是本教程所为你带来的更有价值的内容是思辨性思考能力。本教程将会帮助你理解统计学的基础知识,学习如何使用不同类型的数据,计算相关性协方差等。在数据科学领域的职业是当今在公司中最流行的。并且大多数企业也开始意识到在在其支配数据时所带来的优势,这项趋势只会继续发展。 主要内容: • 样本或流行的数据; • 描述统计学基础 • 集中趋势、不对称和可变现的度量 • 分布 • 评估 • 置信区间 • 假设测试 • 回归分析基础知识 • 线性回归分析的假设 • 使用分类数据
    wilde(特殊组-翻译)1个月前 (10-15)