Common Data Sense for Professionals: A Process-Oriented Approach for Data-Science Projects

Length: 104 pages
Edition: 1
Language: English
Publisher: Productivity Press
Publication Date: 2021-12-23
ISBN-10: 0367760509
ISBN-13: 9780367760502
Sales Rank: #0 (See Top 100 Books)

Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections to determine strategy and marketing. Data scientists take data, analyze it and create models to help solve problems. You may have heard of companies having data management teams, or Chief Information Officers (CIO) or Chief Analytics Officers (CAO), etc. These are all people that work with data, but their function is more related to vetting data and preparing it for use by data scientists.

The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Though an advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic statistical analysis software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data- related challenges.

The goal for this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study — it illustrates how the various topics discussed can be applied.

Essentially, this book helps traditional business people to solve data related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Foreword
Preface
Acknowledgments
Author
Chapter 1 The Meeting of Manju and Jim
Chapter 2 Understanding the Problem
	Phase 1
		Problem Definition
		Goal Setting
		Organizational Cohesion
		Measurement
Chapter 3 Analyzing the Problem and Collecting Data
	Phase 2
		Deep Dive Analysis
		Data Identification and Collection
		Understanding the Risk and Uncertainty
			Risk and Uncertainty in Data Measurement Error
			Risk and Uncertainty Due to the Existence of Variation
			Risk and Uncertainty in Prediction, Diagnosis, and Decision-Making
			Risk and Uncertainty in Analytics Process Execution
			Risk and Uncertainty Due to Incomplete Information
			Risk and Uncertainty Due to Procrastination
Chapter 4 Creating and Analyzing Models
	Phase 3
		Data Analysis and Model Selection
		Characteristics of Successful Analytics
		Different Types of Analytics
		Outcome Analysis
		Individualized Analytics for Eat Healthy Problem
Chapter 5 Project Structure
	Data Science Project Structure
	Six Sigma Process-Oriented Approach
Chapter 6 Data Science Stories
	Case Example 1: Proactive Detection and Diagnosis of Overall Health
	Case Example 2: Improving Customer Satisfaction by Building a Predictive Model
Chapter 7 Concept Review
	Concept Review
		Phase 1: Understanding the Problem
		Phase 2: Analyzing the Problem and Collecting Data
		Phase 3: Creating and Analyzing Models
	Project Structure
Chapter 8 Manju and Jim’s Concluding Meeting
References
Index