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Primer into the multidisciplinary world of Data ScienceKey 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 AWSDescriptionThe 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 will you 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 TableauWho this book is forThis 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 Contents1. Data Science in Practice2. Mathematics Essentials3. Statistics Essentials4. Exploratory Data Analysis5. Data preprocessing6. Feature Engineering7. Machine learning algorithms8. Productionizing ML models9. Data Flows in Enterprises10. Introduction to Databases11. Introduction to Big Data12. DevOps for Data Science13. Introduction to Cloud Computing14. Deploy Model to Cloud15. Introduction to Business Intelligence16. Data Visualization Tools17. Industry Use Case 1 – FormAssist18. Industry Use Case 2 – PeopleReporter19. Data Science Learning Resources20. Do It Your Self Challenges21. MCQs for AssessmentsAbout the AuthorThe 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).Your Blog links: https://probyto/resources/blogsYour LinkedIn Profile: https://www.linkedin.com/company/probyto