Ankit Rathi
Ankit Rathi is a Principal Data Engineer at NatWest Group, with nearly two decades of experience building data and AI systems that drive real-world business outcomes. His career spans roles across data engineering, analytics, machine learning, and moAnkit 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 andRead More...
Designing Decision Systems
Books by Ankit Rathi
Organizations today are rich in data but poor in decisions.
Despite advanced data platforms and AI models, many organizations struggle to translate data into real business impact. The missing link is not more data—it is better decision systems.
Designing Decision Systems presents a clear, systems-oriented approach to turning data into action. It shows how modern organizations convert observations of reality into better decisions through a
DS AI Self-Starter Handbook
Books by Ankit Rathi
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
Probability & Statistics for Data Science
Books by Ankit Rathi
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
×
