Primer into the multidisciplinary world of Data Science

Key Features

Explore and use the key concepts of Statistics required to solve data science problems

Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app

Learn how to build Data Science solutions with GCP and AWS

Description
The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.

What you will learn

Understand the multi-disciplinary nature of Data Science

Get familiar with the key concepts in Mathematics and Statistics

Explore a few key ML algorithms and their use cases

Learn how to implement the basics of Data Pipelines

Get an overview of Cloud Computing & DevOps

Learn how to create visualizations using Tableau

Who this book is for
This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.

Table of Contents
1. Data Science in Practice
2. Mathematics Essentials
3. Statistics Essentials
4. Exploratory Data Analysis
5. Data preprocessing
6. Feature Engineering
7. Machine learning algorithms
8. Productionizing ML models
9. Data Flows in Enterprises
10. Introduction to Databases
11. Introduction to Big Data
12. DevOps for Data Science
13. Introduction to Cloud Computing
14. Deploy Model to Cloud
15. Introduction to Business Intelligence
16. Data Visualization Tools
17. Industry Use Case 1 FormAssist
18. Industry Use Case 2 PeopleReporter
19. Data Science Learning Resources
20. Do It Your Self Challenges
21. MCQs for Assessments

About the Author
The book has been written by collective experience of many of Probyto past client projects, academic collaborations and team members for last 5 years. The collective work is represented by different experts in data driven decision making and portion they deal with in creating value for the clients at Probyto. The team has experienced professionals and freshers who have gained from the approach as mentioned in the book as well. Two key contributions for the book goes to Parvej Reja Saleh (Manager) and Namachivayam Dharmalingam (Senior Analyst).

Blog links: https://probyto/resources/blogs

LinkedIn Profile: https://www.linkedin.com/company/probyto

 

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Spis treści

Data Science for Business Professionals eBook -- spis treści