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Subrat SaurabhAuthor of Kuch Woh PalAnkit Rathi is currently working as a Data Science Architect at SITA aero. He is a Data Science (ML/DL/AI) & Data Architecture (DB/ETL/DWH/BI) practitioner with more than a decade of demonstrated history of working in IT industry using Data & Analytics. His interest lies primarily in the application of artificial intelligence, particularly in developing business applications for machine learning and deep learning. Ankit's work at SITA aero has revolved around designing FlightPredictor product & building the CoE capability. Earlier as a Principal Consultant at Genpact HCM, Ankit arcRead More...
Ankit Rathi is currently working as a Data Science Architect at SITA aero. He is a Data Science (ML/DL/AI) & Data Architecture (DB/ETL/DWH/BI) practitioner with more than a decade of demonstrated history of working in IT industry using Data & Analytics. His interest lies primarily in the application of artificial intelligence, particularly in developing business applications for machine learning and deep learning.
Ankit's work at SITA aero has revolved around designing FlightPredictor product & building the CoE capability. Earlier as a Principal Consultant at Genpact HCM, Ankit architected and deployed machine learning pipelines for various clients across different industries like Insurance, F&A. He was previously a Tech Lead at RBS IDC where he designed and developed various data intensive applications in AML & Mortgages area.
Ankit is a well-known author for various publications (Data Deft, Towards Data Science, Analytics Vidhya etc) on Medium where he actively contributes by writing blog-posts on concepts & latest trends in Data Science. He is followed by 25K+ data science practitioners & enthusiasts on LinkedIn.
Achievements
From a time around when DS/AI field started picking up, every other day I get at least 8–10 messages from DS/AI starters & enthusiasts on ‘How can I get into DS/AI field?’. Over a while, I have improvised my response based on the follow-up questions they ask like: 1. What is the difference between DS, ML, DL, AI, DM? 2. What are the roles in DS/AI, who does what? 3. What concepts, processes & tools they need to learn? 4. Which books, courses, etc
From a time around when DS/AI field started picking up, every other day I get at least 8–10 messages from DS/AI starters & enthusiasts on ‘How can I get into DS/AI field?’. Over a while, I have improvised my response based on the follow-up questions they ask like: 1. What is the difference between DS, ML, DL, AI, DM? 2. What are the roles in DS/AI, who does what? 3. What concepts, processes & tools they need to learn? 4. Which books, courses, etc they need to refer to? 5. How to build a DS/AI portfolio? 6. How to write a resume for DS/AI? 7. How to build a helpful network? 8. How to search for the job? 9. How to prepare for the interview? 10. How to stay up to date in this still-evolving field? You can notice that these questions are not conceptual ones and there is no dedicated material to address these roadblocks. How about a book or course that gives you enough exposure to the DS/AI field that you can yourself analyze what is needed for you and build your own roadmap? My answer to the above question is this book. This book covers the framework to launch your DS/AI career in 8 chapters.
As the title says, this book covers all the topics for probability & statistics in context of data science. While working on data science projects, I tried to look for a reference book which can give reader holistic view of probability & statistics useful for data science, but I could not find everything at one place. So every time, I used to look for the term or topic at various places and then used to relate it in context of data science. At the end, I started
As the title says, this book covers all the topics for probability & statistics in context of data science. While working on data science projects, I tried to look for a reference book which can give reader holistic view of probability & statistics useful for data science, but I could not find everything at one place. So every time, I used to look for the term or topic at various places and then used to relate it in context of data science. At the end, I started writing about these topics in my blog (https://medium.com/@rathi.ankit) as my notes on probability & statistics which were well received by data science community. This book is for people who are working in data science field and want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The approach I have taken here is not to reinvent the wheel, so I try to give an intuitive understanding of each topic and if the user wants to dig further on that topic, he can refer to the companion GitHub notebook of this book, scan the QR code given in the book to get the link.
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