Wiley India

Access to data is no longer a differentiator.

The ability to interpret it is.

In practice, the gap between raw data and meaningful insight is where most decision-making fails, often due to poor data preparation, weak analysis, or lack of clarity in visualisation.

Data Analytics Using Python (2nd Edition) focuses on bridging that gap through structured workflows, from cleaning and exploration to analysis and interpretation.

Because better tools don’t guarantee better decisions.
Better thinking does.

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🏷️: data analytics using python, python data analytics, data science learning, business analytics

The wait is almost over.

The new JEE 2026–27 editions are on their way, updated to align with evolving exam patterns, deeper conceptual focus, and advanced problem formats.

For serious aspirants, the right preparation starts with the right foundation.

📌 Pre-orders opening soon
📌 Stay tuned for launch dates

The expansion of data has not simplified decision-making, in fact, it has made it more complex.

Without structured analysis, more data often leads to more ambiguity, not clarity.

The real shift lies in moving from data accumulation to analytical precision: identifying what matters, what doesn’t, and how insights translate into action.

Big Data and Analytics (2nd Edition) provides a framework for this transition.

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The 12th Edition of Financial Accounting (Indian Adaptation)- Now Available!

A profitable company can still face liquidity issues. The difference lies in how financial data is interpreted, not just presented.

This edition strengthens that understanding, helping learners move from reading numbers to analysing business reality.

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A common misconception in data science is equating it with coding.

In reality, the discipline begins much earlier with problem definition, data understanding, and structured thinking.

Tools and models play a role, but they are only effective when aligned with clear objectives and meaningful interpretation.

Introduction to Data Science and Fundamentals of Data Science using Python & R together offer a balanced approach, from foundational concepts to practical implementation.

Because the real skill lies not in writing code, but in extracting insight.

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Language has always been human-centric. It’s been fluid, contextual, and deeply nuanced.

With the rise of AI systems, it is now being structured, analysed, and computed at scale.

This transition introduces both opportunity and complexity; from enabling intelligent systems to exposing the challenges of modelling meaning, context, and intent.

Natural Language Processing by Bhattacharya offers a comprehensive view of this evolving field, bridging linguistic theory with computational application.

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