Data Science and Analytics for SMEs

Consulting, Tools, Practical Use Cases
1. Auflage 2022
ISBN: 978-1-4842-8669-2
Verlag: Apress

Buch, Englisch, 335 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 540 g

Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. 
SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain.
This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly.  Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business. 

What You'll Learn

• Create and measure the success of their analytics project
• Start your business analytics consulting career
• Use solutions taught in the book in practical uses cases and problems 

Who This Book Is ForBusiness analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts 

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Tolulope, Afolabi Ibukun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Management Prozessmanagement
• Wirtschaftswissenschaften Betriebswirtschaft Management Unternehmensführung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensorganisation, Corporate Responsibility Kleine und Mittlere Unternehmen
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

Chapter 1: Introduction.- Chapter 2: Data for Analytics in Small Businesses.- Chapter 3: Business Analytics Consulting.- Chapter 4: Business Analytics Consulting Phases.- Chapter 5: Descriptive Analytics Tools.- Chapter 6: Predicting Numeric Outcomes.- Chapter 7: Classification Techniques.- Chapter 8: Advanced Descriptive Analytics.- Chapter 9: Case Study Part 1.- Chapter 10: Case Study Part 2.

Über Autor(innen)

Afolabi Ibukun is a Data Scientist and is currently a Senior Lecturer in the Department of Computer and Information Sciences, Covenant University. She holds a B.Sc in Engineering Physics, an M.Sc and Ph.D in Computer Science. Afolabi Ibukun has over 15 years working experience in Computer Science research, teaching and mentoring. Her specific areas of interest are Data & Text Mining, Programming and Business Analytics. She has supervised several undergraduate and postgraduate students and published several articles in international journals and conferences. Afolabi Ibukun is also a Data Science Nigeria Mentor and currently runs a Business Analytics Consulting and Training firm named I&F Networks Solutions