Ships within 1-5 business days: ■□ Popular Lecture Series Now a Book! □■
A popular lecture series from the University of Electro-Communications on data science has been published as a book.
Learn the process of data science in business by exploring and hypothesis-testing daily shopping behavior data (marketing data) using Python programs, and by building machine learning models.
The entire process is explained in a friendly and easy-to-understand manner, likening it to an RPG (Role-Playing Game).
Includes ample exercises and end-of-chapter problems. Develop "practical skills" in data science by cycling through input and output!
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Acquire the secret professional analytical skills of a rising business data scientist, played out in Python!
A complete reveal of methodologies and specific procedures for developing advantageous marketing strategies with data.
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▽ Features of this Book
- Uses data actually used in business.
- Written by individuals involved in actual business.
- Emphasizes a live feel, based on actual lectures and presentation materials.
- Incorporates learning elements for use in university classes and corporate study groups.
▽ Target Audience for this Book
- Those who want to acquire data science skills applicable to business.
- Those interested in analyzing marketing data.
- Those working in consumer goods manufacturers, retail companies, and similar industries.
▽ What You Can Learn from this Book
- How to proceed with data science in business.
- Challenges and countermeasures in shopper marketing.
- Techniques and processes for exploratory data analysis.
- Techniques and processes for hypothesis-driven data analysis.
- Tips for creating presentations and reports related to data science.
- Methods for building and evaluating machine learning models.
- Challenge yourself with Kaggle competitions.
▽ Table of Contents
Chapter 1: Overall Picture and Learning Approach
Chapter 2: Challenges in Shopper Marketing
Chapter 3: Overview of Shopper Behavior Analysis Data
Chapter 4: Exploratory Data Analysis (I)
Chapter 5: Exploratory Data Analysis (II)
Chapter 6: Hypothesis-Driven Data Analysis
Chapter 7: Creating Reports and Presentations (I)
Chapter 8: Preparing for Model Building
Chapter 9: Accuracy Evaluation Methods
Chapter 10: Decision Trees
Chapter 11: Logistic Regression
Chapter 12: Ensembles
Chapter 13: Creating Reports and Presentations (II)
Chapter 14: Systematization, Regression, and Clustering
Chapter 15: Overall Review and Looking Ahead
▽ Columns
- Number of Visitors = Number of Receipts Issued?
- Data Science is an Integration of Humanities and Sciences
- Handling "Outliers" and "Missing Values" Requires Business Knowledge
- Do People Decide on Products Within 4 Seconds of Arriving at the Store?
- Is Determining Order Quantities a "Master's Skill"?
- Is Improving Accuracy a Business Goal?
- Cognitive Dissonance Learned from Dodoria
...and many more.
| Product # | 4780610516 |
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| Weight | 580 g |
| Product package size | 26 x 18 x 2 cm |
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