Capitalizing Data Science

Can you foresee how your company and its products will benefit from data science? How can the results of using AI and ML in business be tracked and questioned? Do questions like ‘how do you build a data science team?’ keep popping into your head?
All these strategic concerns and challenges are addressed in this book.

Firstly, the book explores the evolution of decision-making based on empirical evidence. The book then helps compare the data-supported era with the current data-led era. It also discusses how to successfully run a data science project, the lifecycle of a data science project, and what it looks like. The book dives fairly in-depth into various todays data-led applications, highlights example datasets, discusses obstacles, and explains machine learning models and algorithms intuitively.

This book covers structural and organizational considerations for making a data science team. The book helps recommend the use of optimal data science organization structure based on the companys level of development. Finally, the book explains data sciences effects on businesses by assisting technological leaders.

KEY FEATURES

* Includes todays most popular applications powered by data science and machine learning technology.
* A solid primer on the entire data science lifecycle, detailed with examples.
* An integrated approach to demonstrating the use of Image Processing, Natural Language Processing, and Neural Networks in business.

WHAT YOU WILL LEARN

* Learn the entire data science lifecycle and become fluent in each phase.
* Discover the world of supervised and unsupervised learning applications and structured and unstructured datasets.
* Discuss NLPs function, its potential, and the application of well-known methods like BERT and GPT3.
* Explain practical applications like automatic captioning, machine translation, and emotion recognition.
* Provide a framework for evaluating your teams data science skills and resources.

WHO THIS BOOK IS FOR

Startups, investors, small businesses, product management teams, CxO and all developing businesses desiring to leverage a data science team to gain the most from this book. The book also discusses the potential of practical applications of machine learning and AI for the future of businesses in banking and e-commerce.

1. Data-Driven Decisions from Beginning to Now
2. Data Science Life Cycle —Part 1
3. Data Science Life Cycle —Part 2
4. Deep Dive into AI
5. Applying AI with Structured Data—Banking
6. Applying AI with Structured Data—Ecommerce
7. Applying AI with Structured Data—On-Demand Deliveries
8. AI in Natural Language Processing
9. Bringing It All Together

Mathangi Sri has a proven track record in building world-class data science solutions and products. She has overall 20 patent grants in the area of intuitive customer experience and user profiles. She authored and published a book with Apress, Springer - “Practical Natural Language Processing with Python” She is currently the Chief Data Officer at Yubi. She has previously built data science teams across large organizations like Citibank, HSBC, GE and tech startups like 247.ai, PhonePe, and Gojek. She has brought about cultural change & shift in mindsets for adopting data-driven decisions across different startups. She is an active contributor to the Data Science community - through lectures, talks, blogs, and advisory roles. She is a guest faculty in many premium academic institutes across the country like IIIT Sri City, IIM Kashipur, NIT Trichy, etc. She is recognized as one of “The Phenomenal SHE” by Indian National Bar Association in 2019, the 50 most powerful influencers in AI 2022 by Engatica, the top 50 Influential AI leaders in India by Analytics India Magazine in 2021, top AI leaders in India 2021 by 3AI association etc.